BitMEX 研究推出以太坊节点指标网站——Nodestats.org

摘要: BitMEX 研究非常高兴宣布推出一个监控以太坊网络的新网站  Nodestats.org 。 该网站连接到五个不同的以太坊节点,并每五秒钟收集一次数据。 网站主要专注于提供与每个以太坊节点所需的计算资源相关的指标。 在分析某些指标时,我们可能已经识别出与节点报告的数据完整性有关的问题,这可能是某些以太坊用户所关注的问题。Nodestats.org 是与 TokenAnalyst 合作制作,该公司是 BitMEX 研究的以太坊网络数据和分析合作伙伴。

(截至 2019 年 3月 12 日的网站截图)

 

概述

 Nodestats.org 通过整体采用来比较两个最大的以太坊节点客户端实现的统计数据—— Geth 和 Parity 。在这些客户端实现中,Nodestats.org 比较了不同节点配置的性能 ——快速、完整和归档节点。

 

 Nodestats.org 的主要目的如下:

  1. 提供比较不同以太坊实现的计算效率的指标。 例如,通过比较与以下相关的要求:
    •  CPU 使用率
    • 内存(RAM)
    • 带宽
    • 储存空间
  2. 比较运行以太坊节点软件和其他币(如比特币)之间的资源需求
  3. 通过查看关于节点是否足够快地处理区块以处于链端点,或差的区块传播是否导致节点在大部分时间不同步的指标,来评估以太坊 P2P 网络的实力和交易处理速度

 

 Nodestats.org 在 2019 年 3 月初才开始收集数据,要作出任何确切结论还为时过早。 不过,我们正在保存数据,并希望稍后分析长期趋势。 Nodestats.org 数据是通过每五秒(每小时 720 次)查询一次我们的五个以太坊节点或运行节点的机器生成的,然后将结果存储在数据库中。此数据生成的各种滚动平均值和其他指标显示在 Nodestats.org 网站上。

 

Nodestats 指标的说明

名称 说明 初步调查结果
同步的时间百分比%

这表示节点已验证并下载所有区块数据,到 P2P 网络通知节点是链端点的时间百分比

 

每小时指标通过确定节点是否每 5 秒处于端点来计算,其应该是每小时 720 次查询。 节点表示其处于端点的这些查询的比例是报告的指标。

 

该字段基于 web3 的 “isSyncing” 字段,我们认为该字段使用节点已看到的最高区块,即 “highestBlock” 字段,以确定该节点是否落后于其对等节点所看到的最高区块。

节点通常报告它们在 99.8% 的时间处于端点,这意味着在每小时 720 次查询中大约只有1次节点不是处于链端点。

 

唯一的例外是, Ethereum Parity (以太坊奇偶校验)完整节点,我们将在本报告后面讨论。

 

我们认为该指标的数据完整性很差,例如就 Parity 完整节点而言,所提供信息的完整性很弱,我们将在本报告后面解释。 展望未来,我们的目标是建立一种更有效的方法来计算这个指标。

在冲突链上的时间百分比

这表示节点在网站上跟随与其相对的节点的不同或冲突链的时间百分比。

 

这是通过在我们的数据库中存储所有区块哈希来确定的,如果节点在相同高度处拥有不同的区块哈希,则它们被认为是在不同链上。

通常, Nodestats.org 无法识别客户端跟随不同链的时间。因此,该指标通常为 0% 。(即一小时内 720 次中为 0 次)

CPU使用率

这表示机器 CPU 资源的平均使用率百分比。

 

Nodestats.org 使用的所有机器 都拥有 “Xeon(R)CPU E5-2686 @ 2.30GHz” 处理单元,并且为双核。 例外情况是归档节点,其拥有 16 个核心。

 

所有节点都使用 AWS “i3.large” 机器,但归档节点除外,其运行 “i3.4xlarge” 。

一般来说,CPU 使用率往往在 0.01% 到 1.0% 之间。 Parity 往往达到 1% 的水平,而 Geth 似乎使用较少 CPU 性能。

 

 Geth 的 CPU 使用率似乎不如 Parity 的稳定,Geth 的 CPU 需求偶尔会飙升至 1% 左右。

内存使用情况

 Nodestats.org 每 5 秒从机器读取一次,这与以太坊客户端使用的内存量有关。

 

Nodestats.org 使用的所有机器都拥有 14GB 内存,但归档节点除外,它是一台 120GB 内存的机器。

一般来说,无论有多少内存可用,节点都会占用绝大部分内存(例如超过 95% )。

 

客户端的内存需求似乎相当稳定。

对等者数量 节点每 5 秒向 Nodestats.org  提供一次网络对等者的数量。

 Parity 往往拥有大约 450 个对等者,而 Geth 只有大约 8 个。

 

 Geth 的对等者数量比 Parity 更不稳定,因为它似乎偶尔会下降到 6 个左右。

上行带宽  Nodestats.org 每 5 秒从机器读取一次,这与服务器的总网络上行带宽有关。

具有更多对等者的Parity往往使用超过100KB /秒的带宽(在每个方向)。 相比之下,Geth往往只使用大约4KB /秒的带宽。

 

Geth的带宽需求往往比Parity更不稳定,偶尔会飙升至60KB /秒左右。

下行带宽  Nodestats.org 每 5 秒从机器读取一次,这与服务器的总网络下行带宽有关。
链数据大小

该指标表示专用于客户端的所有目录使用的总数据。

 

与其他指标不同,所公开的数字是绝对值,而不是滚动1小时平均值。

目前, Parity 需要大约 180GB , Geth 使用不到 200GB ,完整归档节点需要 2.36TB 的数据。

 

 Parity 完整节点仍在同步

 Parity 完整节点于 2019 年 3 月 1 日开始,在撰写本文时( 2019 年 3 月 12 日)它尚未完全与以太坊链同步。客户端大约落后 450,000 个区块,而根据其当前的轨迹,它应该在几天内赶上主链端点。由于初始同步缓慢, “同步的时间百分比” 指标显示为接近 0% ,因为客户端永远不会同步。

 Ethereum Parity 完整节点机器的规格如下:

  • 双核 2.3GHz
  • 14GB 内存
  • 固态硬盘
  • 10 Gb/秒的互联网连接

事实上,具有上述规格的机器需要超过 12 天的同步可能表明,对于以太坊网络来说,初始同步问题可能比后同步问题(例如区块传播)更受关注。 虽然初始同步缓慢(至少对于这个系统设置而言)是一个潜在的问题,但以太坊尚未达到节点无法赶上的程度,因为同步速度比区块链增长速度快。

 

数据完整性问题

尽管落后于链端点数十万个区块,但Parity完整节点有时也报告它是同步的。例如,在本文开头的截图中,网站报告该节点有0.02%的时间是完全同步,表明该节点错误地认为其在某段时间内处于链端点。

如下面的 Parity 完整节点日志生成的图表所示,网络图上看到的最高区块(蓝色)似乎有可能不正确。在网络图上看到的最高区块的数值有时随着时间的推移而下降,并且始终远远落后于实际的链端点(以绿色显示)。有时这个有可能出错的数字朝着验证链的高度(橙色)下降,而我们的网站错误地报告该节点同步。这可能是一些以太坊用户关注的问题,因为Parity完整节点与网络有很多连接,因此这可能是一个错误。

 

以太坊 Parity 完整节点区块高度数据 —— 2019 年 3 月 11 日和 12 日(UTC 时间)

(资料来源: 以太坊 Parity 完整节点日志)

 

这个潜在错误可能会破坏我们网站的整个指标,即使对于其他节点也是如此,因为最高链端点的字段可能无法正常运行,以及我们的数字可能不准确。 不过,我们继续收录这个指标,因为 Nodestats.org 网站显示节点报告的数据,与我们对数据完整性的看法无关。 我们希望将来可以实施我们自己的改进指标。

有人可能会认为,如果攻击者以正确的方式利用这个潜在错误,其影响在某些有限情况下可能会很严重。 例如,用户可能会将收款或智能合约执行看作已经验证,而他们的节点声称处于网络链端点。 但是,客户端可能并不真正处于链端点,攻击者可能会利用此漏洞来欺骗收款人提供商品或服务。 攻击者将需要在易受攻击节点错误地认为是链端点的高度上花费多一倍功夫,其工作要求证明可能比主链端点低。 不过,成功执行此攻击的可能性极小,而用户也不太可能使用最高的区块功能。

 

总结

就像其同类网站 Forkmonitor.info 一样, Nodestats.org 还有很多工作需要改善。在未来几个月和几年内与 TokenAnalyst 的合作过程中,我们计划添加更多功能,例如:

  • 通过减少对节点报告的内容的依赖并开发我们自己的计算方法,提高数据的完整性
  • 用于分析长期趋势的图表和工具
  • 改进数据的粒度
  • 分叉检测系统
  • 与其他对等者相关的数据

目前, Nodestats.org 提供了一个有用的工具来评估运行以太坊节点的大致系统要求。 在非常基础的层面上,它还提供了评估以太坊网络及其各种软件实施的可靠性的机制。 不过,我们承认 “同步的时间百分比” 指标可能不可靠,但它确实突出了潜在问题。

 

 

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BitMEX (www.bitmex.com)

剖析新一轮全球金融危机

摘要: 我们探讨的是加密货币圈内人士经常提起的一个问题: “新一轮全球金融危机何时来临?” 我们试着回答这个问题,首先要解释的是,2008 年之后,经济危机的中心不再是银行,变成了资产管理行业。 因此我们认为,一旦 2008 年金融危机卷土重来,零售银行存款和支付系统受到威胁的可能性不大。 特别是,我们相信,在看似低波动率及低回报环境的推波助澜下,公司债投资基金和非常规债券投资工具可能成为金融体系中最为薄弱的一环。

(上一次全球金融危机距今已有 10 年,昔日报章已然泛黄,信贷情况可能在某些情况下大幅收紧,但资产管理行业是否已取代银行业的位置,处于危机中心?)

综述

2008年全球经济危机之后,比特币被视为金融乱象丛生和怀疑主义盛行的产物,部分原因在于比特币问世的时间。 因此,比特币投资者和加密货币界人士似乎时常问一个问题:

新一轮全球金融危机何时来临?

我们试着应要求解答这个问题。

首先,我们来探究一下这个问题本身。 我们的看法是,此问题背后主要存在三种假设:

  1. 未来数年内将出现新一轮金融危机,金融危机是不可避免的,每10年左右出现一次;
  2. 这种危机将对比特币价格产生积极影响;
  3. 新一轮全球金融危机情况与上一次相似,导致民众普遍质疑银行系统和电子支付系统的信用。

在这三种假设当中,我们真正同意的只有第一种。 虽然我们认为后两种假设可能成真,但其不确定性非常大。 

至于第二种假设,我们在 2018 年 3 月提过这个问题,但是我们指出,在交易方面比特币表现得更像是风险资产而非避险资产。 当然,从那时起比特币价格大幅下滑,未来情况可能有变。 如果比特币在新一轮全球金融危机中表现良好(在流动资金紧绌的情况下),将对比特币和价值储藏投资理念产生巨大的积极影响。 然而并没有大量证据表明这种情况将会发生。 我们认为,若这种情况发生的话,比特币价格需要与诸多替代币脱钩,这很明显是趋险型投资理念。 

至于第三种假设,新一轮全球金融危机的机制是本报告的焦点所在。

发达市场的银行财务状况相对稳健

有一句名言是这样说的,“历史不会重演,但总是惊人地相似。” 过去 10 年里,银行管理团队和银行业监管机构在 2008 年全球金融危机的阴影下如履薄冰。 因此,银行财务状况和资本比率显著加强。 发达市场的银行一级资本比率从经济危机前的 5% 左右提升至如今的 12% 左右(见图 1 )。 而更为基础的总资产权益率就更难以操纵,情况也相类似:从原先的 5% 左右提高至目前 9% 左右(见图2)。

图 1  – 美国与英国银行综合普通股权益一级资本比率

(资料来源: 英国综合数据来自于英格兰银行,美国数据来自于美联储

图 2 – 美国银行总资产与有形资产综合比率(资产规模超过 500 亿美元的银行)

(资料来源:美联储

或许,比上述比率更清晰和更发人深思的是以下简图(见图3)。 该图显示,自全球金融危机以来,西方主要银行从未扩大其资产负债表规模。 实际上,我们研究的九大银行总资产规模从 2008 年的 19.3 万亿美元大幅下降至 2018 年的 15.6 万亿美元。 也许有人会反驳说,并购活动是造成下表所示情况的主要原因,但我们的观点仍然站得住脚。

图 3 – 发达市场个别银行总资产规模 – 单位:万亿美元

(资料来源: BitMEX Research、银行收益、彭博社)

(注: 图中显示的是摩根大通、美国银行、花旗银行、富国银行、汇丰银行、苏格兰皇家银行、德意志银行、瑞士信贷银行以及瑞士联合银行对外公布的资产总额。)

我们的看法是,财务杠杆是金融风险的主要推动因素。 似乎自2008年开始,金融体系风险的中心开始转移。 2008年,银行系统的杠杆以及杠杆与按揭市场证券化之间的相互关系引发风险。 如今,在看似低波动率环境的推波助澜下,资产管理行业的杠杆,尤其是公司债的杠杆形成同等风险。

资产管理行业杠杆率的提高

资产管理行业的透明程度远不及银行业,要确定杠杆水平难度更大。 因此,无论是在资产管理行业杠杆问题上,还是在该杠杆的相关金融危机到来时间问题上,都难以得出结论。

2015年国际清算银行的《买方杠杆》报告指出,“风险从银行系统转移到资产管理行业,值得注意”。 报告提到,虽然投资基金在股票和固定收益领域的杠杆保持相对稳定,但是自2008年以来,杠杆率明显提高,新兴市场尤为如此。 国际清算银行报告得出以下结论:

银行系统杠杆是2008年全球金融危机的重要组成部分。 从那时起,银行紧缩财政,重新实现良好资产负债状况,藉此资产经理人(“买方”)迅速扩大全球融资领域的版图。 要获取投资基金的资产负债信息,比获取受到严格监管银行的资产负债信息难得多。 我们使用某市场数据供应商提供的资料,发现买方杠杆不容忽视,哪怕买方杠杆因基金类型而大相径庭。 股票基金投资组合的杠杆似乎使用率最低,而固定收益基金非常倚重借贷。

(资料来源:国际清算银行

国际清算银行使用的数据来自于专业的投资基金流公司EPFR,虽然我们认同报告结论,但是不能完全相信数据的可靠性。 我们尚未发现理想的全球数据来源,但是超过一定规模的美国注册投资基金必须向美国证券交易委员会提交有关杠杆使用情况的数据。 美国证券交易委员会从2013年第二季度起使用这类数据,我们概括出下列表格的主要趋势(见图4、5和6)。

数据显示,与银行业不同的是,资产管理行业从2008年开始显著扩张(见图4)。 与此同时,即使难以绘制出一张自2008年起的清晰图表,可杠杆率似乎也在上升。

图 4 – 美国基金行业资产总值(单位:十亿美元)

(资料来源: BitMEX Research、美国证券交易委员会

虽然存在着相互抵触的方法论,可是确定投资基金杠杆水平的最基础方法始终是计算总资产值与净资产值的比率,有时也称为杠杆比率。 可惜下表(见图5)时间跨度有限,但该表格似乎显示出杠杆率在适度扩大,至少在对冲基金方面情况如此。 

图 5 – 美国私募基金行业杠杆比率 – 资产总值/净资产值

(资料来源: BitMEX Research、美国证券交易委员会

由于忽略了衍生工具的影响,杠杆比率低估了真实的杠杆情况。 美国证券交易委员会还要求披露衍生工具的名义价值。 下图说明,美国对冲基金衍生工具使用量增加。

图 6 – 美国私募基金行业 – 对冲基金 – 衍生工具名义价值/净资产值

(资料来源: BitMEX Research、美国证券交易委员会
(注: 通过调整反映美国证券交易委员会数据报告方式的变化。)

新公司债市场投资工具

除了固定收益市场中投资基金杠杆使用量增加外,债券市场的机制日益复杂和不透明。 银行在公司债市场的地位被取代,导致各种相互联系、相互排斥的投资机构迅速增多。 下表概括了其中部分结构。

债券类型 描述/评论 参考资料
债务抵押证券 债务抵押证券(CLO)是指多家公司的一系列贷款汇集形成的一种证券。 这种产品通常划分为多个等级,低风险等级产品回报较低,高风险等级产品回报较高。 一旦公司破产,最后才向最高风险等级产品的投资者偿付。

这些产品的买家通常是退休基金、保险公司和对冲基金。 看重收益的亚洲投资者十分青睐这类产品。

市场增长 – 图 7
杠杆贷款 杠杆贷款通常是指由高负债公司发行的可变利率贷款。 大多数情况下是无担保贷款。 这类投资工具的持有者往往是退休基金和其他私人投资者。

英格兰银行不久前估计,全球杠杆贷款市场规模达2.2万亿美元,并将其与2006年美国次级贷款市场的规模(1.3亿美元)相比较。

市场增长 – 图 8

信贷质量 – 图 15

私人债务交易 与杠杆贷款市场相似,不同的是债务一般不在二级市场交易。 市场增长 – 图9
债券基金交易所买卖基金和共同基金 在此期间,所有资产类别中的交易所买卖基金(ETF)均更受青睐,连公司债券基金也不例外。 市场增长 – 图 10
私募股权 信贷质量 – 图 16

(注: 上表中各栏相互间并不排斥)

各种途径得出的下列表格显示,上一次全球经融危机发生后,这些非银行公司融资机制全部大幅增加。

图 7 –债务抵押证券市场规模 – 单位:十亿美元

(资料来源: 花旗银行、金融时报

图 8 – 美国杠杆贷款市场规模 – 单位:十亿美元

(资料来源: 标准普尔、金融时报

图 9 – 私人债务市场规模 – 单位:十亿美元

(资料来源: 美国银行、金融时报

图 10 – 面向美国投资者的顶级债券ETF规模 – 单位:十亿美元

(资料来源: BitMEX 研究、彭博社)

(注: 图表显示的是下列债券 ETF 的总市值:iShares 核心美国 综合债券 ETF 、先锋总体债券市场 ETF 、 iShares iBoxx 美元投资级别公司债 ETF 、先锋短期公司债 ETF 、先锋短期债券 ETF 、先锋中期公司债 ETF 、 iShares 摩根 美元新兴市场债券 ETF 、先锋总体国际债券 ETF 、 iShares 按揭支持债券 ETF 、 iShares iBoxx 美元高收益公司债 ETF 、 PIMCO 增强短期策略基金、先锋中期债券 ETF 、 iShares 短期公司债 ETF 、 SPDR 巴克莱高收益债券 ETF 、 iShares 短期债券 ETF )

公司债市场状况

如以下图 11 所示,公司债务水平自 2008 年起显著提高,罗素 3000 指数中的公司当前总负债额为11万亿美元,上一次全球金融危机爆发时这些公司的总负债额刚刚超过8万亿美元。 公司利用低利率和上述新投资产品,贷款创下纪录。 

然而,图 11 的红线显示,罗素 3000 指数的公司负债情况看起来仍处于稳健水平,净负债与息税折旧摊销前利润(EBITDA)的比率略低于2.5倍。虽然该比率在过去几年间不断上升,但远远低于2008年经济危机前大约3.7倍的高水平。这种明显增长是由少数科技巨头囤积现金以及强大的经济带动企业收入提高所造成的。如果经济转向,随着公司收入减少,资产负债状况或许会再次变差。

图 11 – 公司债务水平

(资料来源: BitMEX 研究、公司数据、彭博社)
(注: 根据罗素 3000 指数中所有公司得出的总数据)

未来几年将有大量公司债券到期。这会在固定收益领域加剧流动性危机或压力的影响。 我们的分析显示(见图 12 ),美国将有 8800 亿美元公司债券将于 2019 年到期。

图 12 – 公司债券到期时间 – 单位:十亿美元

(资料来源: BitMEX Research、彭博社)
(注: 数据基于约6400只美国公司债券组成的数据库,债券发行总量为5.7万亿美元。)

或许最令人担忧的指标是公司债的质量。图13表示的是未偿还投资级别公司债的历史信用评级分布。截至2018年底,将近50%的债券评级为投资级别证券的最低评级水平,比例高于过去30年间的任何时期。图14表明,如果大量将要到期的公司债券是评级最低的投资级别债券,从2021年起情况将变得更加糟糕。

图13 – 美国公司债标准普尔信贷评级历史分布

(资料来源: 彭博社、汇丰美元投资级别指数成份股,包括金融与非金融公司)

图 14 – 未偿还美国公司债标准普尔信贷评级历史分布(按到期时间划分)

(资料来源: BitMEX 研究、彭博社)
(注: 数据基于约 6400 只美国公司债券组成的数据库,债券发行总量为5.7万亿美元。)

评估上述非常规债券工具的信贷质量,难度更大。 但是,穆迪不久前发布的一份报告显示,杠杆贷款市场投资者的保障程度大幅恶化,如以下图 15 所示。

图 15 – 穆迪对杠杆贷款契约质量的评估(美国与加拿大)

(资料来源: 穆迪、彭博社
(注: 5.0 为最低分, 1.0 为最高分。)

图 16 – 私募股权交易平均总负债与 EBITDA 倍数比例

(资料来源: 标准普尔、金融时报

低波动率环境

在我们看来,发达经济体采取的非常规货币政策压低了投资回报和波动性,同时降低贷款成本;这种情况刺激资产经理人使用更多的杠杆,追逐更高的风险。与此同时,同样的政策也鼓励公司承担更高的债务。低波动率对固定收益领域的影响甚于其他领域。 “风险均衡” 型投资策略越来越受欢迎,采用这种策略的基金经理根据每种资产类别的风险(波动率)构建投资组合,然后使用杠杆提高回报。杠杆有助于减轻持重低风险资产带来低回报的影响。 通常的做法是,持偏高比重的是固定收益而非股票,同时纳入更多杠杆,以抵消低风险资产的低回报影响。

2018 年 2 月,波动率指数飙升,做空波动率指数的投资策略(例如 Velocity Shares 每日反向波动率指数交易所交易票据)价值暴跌至零,因此波动率急速上升。 2018 年 3 月的 BitMEX 加密货币投资者文摘中讨论过该问题。其受害者是一小群贪图早期收益的投机型投资者,而波动率指数的影响仅限于金融体系的其他部分。不过,这种情况很可能日后出现在固定收益市场上, 2018 年 2 月的事件正是其缩影。而这一次波及的将是从人为低波动率和低借贷成本中渔利的主流投资者。市场在某个时间将进行整固,其影响将远比 2018 年 2 月严重,不仅仅是数亿美元蒸发,而是数万亿美元资产化为乌有。

事件发生的顺序描述如下,而各种不同因素将导致风险加剧:

  1. 出现某个催化因素,导致波动率急剧上涨。
  2. 投资者需要分散其投资组合的风险,首先要处理的是流动性最高的市场,即固定收益市场。
  3. 在流动性最高的市场中,机构主导交易,各大机构可能会在同一时间抽走所有流动资金。
  4. 投资者急于退市,导致固定收益市场出现波动,流动性下降并且无法运作。
  5.  CLO 和债券 ETF 等证券化债券资产以远低于净资产值的折扣价进行交易。
  6. 情况蔓延至其他的流动性资产类别,例如股票。
  7. 在未来几年里,新成立的债券发行机构开始缺乏资金;公司为了融资而苦苦挣扎,经济受到重挫。

当然,我们并不清楚什么是导致波动率增加的主要催化因素。它可能是某个地缘政治事件,新兴市场美元债券过量发行,中国资产管理行业的高杠杆,被动型 ETF ,高频率交易员,央行过快缩减资产负债规模,出乎意料的大批企业破产,欧元区债务危机,甚至是比特币出现灾难性重大漏洞引起波动等等……  

其实,无论哪个事件都不是问题的关键。问题的关键在于,在人为低波动率和过高杠杆的推动下,金融系统固有的不稳定性和脆弱发挥作用。许多人可能在某个事件发生后,将某个特定的催化因素视为金融危机的罪魁祸首,但理智告诉我们,这没有实事求是。

结语

对于金融体系和人类社会而言,银行比资产经理人更加重要。如果资产管理人承受压力,虽然部分高净值人士资产减值;散户和企业存款应该是安全的;因此新一轮金融危机的影响可能不及 2008 年严重。可重要的是,政府介入以减轻经济危机影响的可能性也比 2008 年时要低。 

首先也是最明显的,各国央行行长可动用的措施被严重削弱,利率已经很低,而资产负债表规模很高。 其次,或许更为重要的是政治层面。我们肯定难以捉摸每个人的看法,但特朗普、英国脱欧、法国黄背心运动等背后的那些特殊人群或许并不支持政府对金融市场的某种干预措施。 

在现今日益“民粹化”的政治环境中,要证明量化宽松计划,或牺牲没有大量金融资产的中等收入人群,从而提高资产价格的计划的正确性,难上加难。 因此,在新一轮全球金融危机中,管理好“政治动乱”的显著风险可促使各国央行行长所采取措施的范围大幅缩减。 

请记住, 2008 年后也有政党反对央行政策, 2011 年时这种反抗达到顶峰。这次的另一项重大差别在于领导反抗者可用的工具现在更加先进了,例如社交媒体。自2008年起西方国家的政治不确定性似乎在增加。如果这种不确定性开始与金融波动相互作用,风险将会加剧。

至于何时发生全球金融危机,我们不得而知。 我们的观点是,本报告中列示的图表说明了一个问题,但这些图表并不是暗示我们正处于重大危机的边缘;或许很多年后才会发生金融危机。 至于如何从这类事件中获利,其难度或许比预测金融危机到来时间更高。或许人们可以构建一个由波动率看涨期权、远期公司债ETF看跌期权、指数挂钩政府债券、波动率对冲基金、黄金甚至少量比特币组成的投资组合。再次声明,虽然我们不知道这些事件何时会发生,不过也许现在正是调整投资组合的时机。

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BitMEX 保险基金

摘要:在这篇文章中,我们探讨了为什么需要 BitMEX 保险基金及其运作方式。我们将 BitMEX 保险基金模型与其他更传统的杠杆市场(例如 CME )所使用的系统进行比较。我们得出结论,与传统的机构交易平台相比,提供杠杆和限制下跌的加密货币交易平台面临着一些独特的挑战。然而,BitMEX 保险基金的增长为获胜的交易者提供了一个合理的保证,保证他们能够获得预期的盈利。

 

(BitMEX 联合创始人兼首席执行官 Arthur Hayes(左)和 CME 主席兼首席执行官 Terrence Duffy(右))

杠杆交易平台

当人们在诸如 BitMEX 之类的衍生品交易平台上进行交易时,人们不会与平台进行交易。BitMEX 只是第三方之间衍生品合约交换的服务商。BitMEX 平台的一个主要特征是其杠杆比例,交易者可以存入比特币,然后利用杠杆比例(理论上高达100 倍)以及购买名义仓位远高于其存款的比特币价值的合约。

同时提供杠杆和交易者相互交易的能力意味着胜利者并不一定能获得他们期望的所有利润。由于涉及到的杠杆比例,输家的仓位中可能没有足够的保证金来向赢家支付。

请参照以下简例,其中平台由两个相互交易的客户组成:

  交易者 A 交易者 B
交易方向 做多 做空
保证金 1 BTC 1 BTC
交易执行价格 $3,500
杠杆 10x 10x
名义仓位大小 10 BTC 10 BTC
当前比特币价格 $4,000
预期利润 $5,000 ($5,000)

在上面的例子中,赢家交易者 A 预期获得 5000 美元的利润,这比输家交易者 B 为该交易放上的抵押品还要多( 1 个比特币价值 4000 美元)。因此,交易者 A 只能获得 1 BTC(4000 美元)的利润,这可能会让他/她有些失望。

传统交易所

像芝加哥商业交易所(CME)这样的传统交易所不会像 BitMEX 这样的加密平台一样分享这个问题。在传统的杠杆交易场所,往往有多达五层的保护,确保赢家获得预期利润:

  1. 如果个别交易者的损失大于其账户中的抵押品,使其账户余额为负,则需要向其账户注入更多资金为该仓位充值。如果他们不能或不愿这样做,其经纪商可以对交易者提起诉讼,迫使交易者提供资金或申请破产。每个交易者都必须使用经纪商,经纪商可以评估每个客户的资产负债表和资本,根据对其特定风险的评估,为每个客户提供定制的杠杆金额。
  2. 在传统的衍生品市场中,交易者通常不能直接进入交易平台。相反,客户通过经纪商(清算成员)进入市场,例如摩根大通或高盛等投资银行。如果交易者遭受损失,无法收回债款,经纪商必须支付交易所的费用,以使交易对方成为整体。从交易所的角度出发,这些经纪商有时被称为清算成员。
  3. 在清算成员违约的情况下,集中清算实体本身通常需要使交易对方成为整体。在许多情况下,清算和结算由独立的实体对经营交易所一方进行。清算所通常拥有各种保险基金或保险产品,为清算成员提供资金。
  4. 如果清算成员无法进行清算,而且集中清算实体也没有足够的资金的话,在某些情况下,其他有偿付能力的清算成员应提供资金。
  5. 金融监管机构通常认为许多大型清算所(甚至可能是大型经纪商)对全球金融体系具有系统重要性。因此,在清算日来临时,一家大型清算所可能会倒闭,政府可能介入并救助交易者,以保护金融系统的完整性。通常在利率互换市场,交易者和机构往往拥有大量的名义仓位(数兆美元)对冲其他仓位或工具。因此,重要的是大型清算所保持其偿付能力,否则整个金融体系可能崩溃。

CME

CME是世界上最大的衍生品交易所,年名义交易额超过1兆美元,是 BitMEX 的1000倍以上。CME有多层保护措施和保险,以在清算成员违约时提供保护。资金以各种方式筹集:

  • CME 提供
  • 清算成员提供
  • 清算成员发行的债券,成员违约时可由清算基金赎回。

CME 清算的多种保护措施和保险基金(2018)

基础金融保护方案
保证金出资 46 亿美元
指定公司出资 1 亿美元
评估权 127 亿美元
IRS金融保护方案
保证金出资 29 亿美元
指定公司出资 1.5 亿美元
评估权 13 亿美元

(来源CME

在特殊情况下,CME 也有权对非违约清算成员应用 “评估权” ,以帮助在所有其他保险资金耗尽时为违约成员提供资金。每一清算成员违约担保的基金评估权价值上限为 2.75 倍。

根据上表中保险基金的规模,CME 拥有大约220亿美元的各种保险基金。大约占到了 CME 年度名义交易价值的 0.002%。

BitMEX 和其他提供杠杆比例的加密货币交易平台目前无法像CME等传统交易所那样为赢家交易者提供相同的保护。加密货币是零售驱动的市场,客户期望直接访问该平台。同时,加密交易平台提供了限制下跌风险的能力,这对零售客户具有吸引力,因此加密交易不会紧追客户并要求那些账户余额为负的客户进行支付。BitMEX 一类的杠杆式加密货币平台为客户提供了一个有吸引力的提议:高度波动的基础资产限制下跌,但不限制上涨。但交易者为此付出了代价,因为在某些情况下,系统中可能没有足够的资金来支付赢家的预期所得。

BitMEX 保险基金

为了缓解这个问题,BitMEX 开发了一个保险基金系统,帮助确保赢家获得预期利润,同时仍然限制了亏损交易者的下跌负债。

当交易者持有未平仓杠杆仓位时,如果其维持保证金太低,其仓位就会被强制平仓(即强平)。与传统市场不同,交易者的盈利和亏损并不反映他们在市场上平仓的实际价格。在 BitMEX 上,如果一个交易者被强平,其与仓位相关的权益会下降到零。

示例交易仓位
交易方向 做多
保证金 1 BTC
比特币价格(开盘时) $4,000
杠杆 100x
名义仓位大小 100 BTC = $400,000
维持保证金占名义仓位的百分比 0.5%

在上面的例子中,交易者持有 100 倍的多仓。如果比特币的标记价格下跌 0.5%(至 3980 美元),该仓位将被强平,100 个比特币的仓位则需要在市场上卖出。从被强平的交易者的角度来看,这一交易的执行价格是多少无关痛痒,无论是 3995 美元还是 3000 美元,他们都会失去其仓位里的所有资金,就是那一整个比特币。

现在,假设市场存在流动性,那么买卖价差应该比维持保证金更紧凑。在这种情况下,强平会导致对保险基金的贡献(例如,如果维持保证金为万分之 50 ,但市场价差为万分之 10 ),那么当仓位被强平时,保险基金的增长几乎等于维持保证金。因此,只要具流动性的健康市场持续存在,保险基金应继续以稳定的速度增长。

下面的两个图表尝试说明上述示例。在第一张图表中,强平时市场状况良好,买卖价差很小,只有 2 美元。因此,当平仓价格高于破产价格(保证金余额为零的价格),保险基金就会获得收益。在第二张图表中,强平时买卖价差比较大。平仓价格低于破产价格,因此保险基金被使用,以确保赢家交易者获得预期盈利。这种情况看上去不太多见,但没有人能保证这种健康的市场状况能持续下去,尤其是在价格波动加剧的时候。在这些时候,保险基金的流失速度比其积累起来的速度要快得多。

贡献保险的说明性示例 — 100 倍多仓,抵押品为 1 BTC

(注意:上述说明基于以每 4000 美元 1 BTC 的价格和抵押 1 个比特币开设 100 倍多仓。说明过于简单,忽略了费用和其他调整等因素。买入价和卖出价代表强平时委托的状态。平仓价格为 3978 美元,与强平时的 3979 美元买入价相比,有 1 美元的滑点。)

损耗保险的说明性示例 — 100 倍多仓,抵押品为 1 BTC

(注意:上述说明基于以每 BTC 4000 美元和 1 个比特币作抵押的价格开设 100 倍多仓。说明过于简单,忽略了费用和其他调整等因素。买入价和卖出价代表强平时委托的状态。平仓价格为 3800 美元,与强平时的 3820 美元的买入价相比,有 20 美元的滑点。)

根据目前的比特币现价,BitMEX 保险基金目前约为 21000 个比特币或 7000 万美元。这仅占 BitMEX 名义年交易量的 0.007%,约 1 兆美元。虽然这比 CME 的保险基金占交易量的比例略高,但 BitMEX 上的赢家交易者面临的风险要比 CME 交易者大得多,原因如下:

  • BitMEX 不具有拥有大型资产负债表的清算成员,且交易者之间为直接接触。
  • BitMEX 不要求账户余额为负的交易者进行支付。
  • BitMEX 的标的工具比 CME 可用的更传统的工具更不稳定。

自动减仓

如果保险基金耗尽,赢家就无法取回其应得的盈利。相反,正如我们上面所描述的,赢家需要出资来弥补输家的损失。BitMEX 的这一过程被称为自动减仓

自 2017 年 3 月以来,BitMEX 比特币永续掉期合约未发生自动减仓。2017 年 3 月初,SEC 不批准 Winklevoss 申请比特币 ETF。当天,市场在 5 分钟内下跌了 30% 。急剧的价格下跌使保险基金完全损耗。许多 XBTUSD 空仓都是被自动减仓,其盈利受到了限制。

尽管自那以后 BitMEX 保险基金大幅增长,但加密货币交易是一个动荡和不确定的行业。尽管目前处于相当高流动性的健康时期,但我们认为比特币价格有可能大幅波动。即使在 BitMEX 比特币永续掉期合约上,也不能确定自动减仓不会再次发生。

保险基金数据

尽管保险基金的绝对值已经增长,如下图所示,作为 BitMEX 交易平台上的其他指标(如未平仓合约价值)的一部分,增长并不明显。

BitMEX 保险基金 — 2018 年 1 月起的每日数据

(来源: BitMEX)

作为 BitMEX 比特币永续掉期未平仓合约价值一部分的 BitMEX 保险基金 — 2018 年 1 月起的每日数据

(来源: BitMEX)

动机

假设保险基金仍然资本化,该系统的运作原则是,被强平的人支付强平费用,输家为输家买单。虽然这种方法可能有些新颖,但在某种程度上它是公平的,这在上面提到的一些替代模型中并不存在。问题的关键在于,为什么未参与高风险杠杆投资的交易者必须为那些参与了的交易者买单?

结论

尽管保险基金有 21000 个比特币,约占比特币总供应量的0.1%,但与传统杠杆交易平台提供的担保相比,BitMEX 无法为赢家交易者提供同样强大的担保。虽然保险基金已经达到了一个健康规模,但其规模可能还不够大,不足以让赢家交易者在加密货币领域不稳定、不可预测的坎坷道路上获得所需的信心。鉴于这种波动性,基金再次降至零并非不可能。

 

 

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BitMEX Research Launches Ethereum Node Metrics Website – Nodestats.org


Abstract: BitMEX Research is delighted to announce the launch of a new website to monitor the Ethereum network, Nodestats.org. The website connects to five different Ethereum nodes and collects data every five seconds. The main focus of the website is providing metrics related to the computational resources each Ethereum node requires. While analysing some of the metrics, we may have identified issues with respect to the integrity of the data reported by the nodes, which may be of concern to some Ethereum users. Nodestats.org was produced in collaboration with TokenAnalyst, who are BitMEX Research’s Ethereum network data and analysis partner.

(Screenshot of website as at 12 March 2019)

Overview

Nodestats.org compares the statistics of the two largest Ethereum node client implementations by overall adoption – Geth and Parity. Within these client implementations, Nodestats.org compares the performance of different node configurations – fast, full, and archive nodes.

The main purpose of Nodestats.org is as follows:

  1. To provide metrics comparing the computational efficiency of the different Ethereum implementations. For instance by comparing requirements related to:
    • CPU usage
    • Memory (RAM)
    • Bandwidth
    • Storage space
  2. To compare the resource requirements between running Ethereum node software and that of other coins, such as Bitcoin
  3. To evaluate the strength of the Ethereum P2P network and transaction processing speed, by looking at metrics related to whether the nodes have processed blocks fast enough to be at the chain tip or whether poor block propagation results in nodes being out of sync for a significant proportion of the time

Nodestats.org began collecting data at the start of March 2019 and it is too early to draw any firm conclusions. However, we are saving the data and hope to analyse the long term trends at a later point. The Nodestats.org data is produced by querying our five Ethereum nodes or machines running the nodes, every five seconds (720 times per hour) and then storing the results in a database. Various rolling averages and other metrics produced from this data, are displayed on the Nodestats.org website.

Description of the Nodestats metrics

Name Description Initial findings
% of time in sync This represents the percentage of time the node has verified and downloaded all the block data, up what the P2P network is informing the node is the chain tip.

The hourly metric is calculated by determining if the node is at the tip every 5 seconds, which should be 720 queries per hour. The proportion of these queries where the node says it is at the tip is the reported metric.

This field is based on the web3 “isSyncing” field, which we believe uses the highest block the node has seen, the “highestBlock” field, to determine if the node is behind what its peers regard as the highest block ever seen.
Nodes typically report they are at the tip around 99.8% of the time, which means that in only around 1 of the 720 hourly queries are the nodes not at the chain tip.

The only exception here is the Ethereum Parity full node, which we talk about later in this report.

We believe the data integrity of this metric is poor, for instance in the case of the Parity full node the integrity of the information provided is weak, as we explain later in this report. Going forwards we aim to establish a more effective way of calculating this metric.
% of time on conflicting chainThis represents the percentage of time the node is following a different or conflicting chain to the node opposite it on the website.

This is determined by storing all the block hashes in our database, if the nodes have a different block hash at the same height, they are considered to be on different chains.
Typically Nodestats.org is not able to identify times when the clients are following different chains. As such this metric is normally 0%. (i.e. 0 times out of 720 in a one hour period)
CPU UsageThis represents the average percentage utilization of the machine’s CPU resources.

All the machines Nodestats.org are using have the “Xeon(R) CPU E5-2686 @ 2.30GHz” processing unit with two cores. The exception to this is the archive node, which has 16 cores.

All the nodes are using the AWS “i3.large” machines, with the exception of the archive node, which is running “i3.4xlarge”.
Generally speaking, CPU usage tends to be between 0.01% and 1.0%. Parity tends to be towards the 1% level, while Geth appears to use less CPU power.

Geth’s CPU usage appears less stable than Parity’s, with Geth’s CPU demand occasionally spiking to around the 1% level.
Memory UsageNodestats.org takes a reading from the machines every 5 seconds, related to how much memory is being utilized by the Ethereum client.

All the machines Nodestats.org are using have 14GB of Ram, with the exception of the archive node, which is a 120GB of Ram machine.
Generally speaking, however much RAM is available, the nodes use up the overwhelming majority of it (e.g. over 95%).

The memory demands of the clients appear to be reasonably stable.
Peer countThe node provides Nodestats.org with the number of network peers, every 5 seconds.Parity tends to have around 450 peers, while Geth only has around 8.

Geth’s peer count is more volatile than Parity, as it appears to occasionally fall to around 6.
Upstream bandwidth Nodestats.org takes a reading from the machine every 5 seconds, related to the total network upstream bandwidth of the server. Parity, which has more peers, tends to use over 100KB/s of bandwidth (in each direction). In contrast Geth tends to only use around 4KB/s of bandwidth.

Geth’s bandwidth demand tends to be more volatile than Parity, with occasional spikes to around 60KB/s.
Downstream bandwidth Nodestats.org takes a reading from the machine every 5 seconds, related to the total network downstream bandwidth of the server.
Chain data sizeThis metric represents the total data utilized by all the directories dedicated to the client.

Unlike the other metrics, the disclosed figure is the absolute value, not a rolling 1 hour average.
Currently, Parity requires around 180GB, Geth uses just under 200GB, and the full archive node uses up 2.36TB of data.

The Parity full node is still syncing

The Parity full node was started on 1 March 2019, at the time of writing (12 March 2019) it has still not fully synced with the Ethereum chain. The client is around 450,000 blocks behind, and based on its current trajectory, it should catch up with the main chain tip in a few days. Due to the slow initial sync, the “% of time in sync” metric is shown as near 0%, as the client is never in sync.

The Ethereum Parity Full node machine has the following specifications:

  • Dual Core 2.3GHz
  • 14GB of RAM
  • SSD storage
  • 10 Gb/s internet connection

The fact that a machine with the above specification takes over 12 days to sync may indicate that it is the initial sync issues could be a greater concern for the Ethereum network than post sync issues, such as block propagation. While the slow initial sync is a potential problem, at least for this system setup, Ethereum has not yet reached a point where the node cannot catch up, as the sync is faster than the rate of blockchain growth.

Data integrity issues

The Parity full node also sometimes reports that it is in sync, despite being several hundred thousand blocks behind the chain tip. For instance in the screenshot at the start of this piece, the website reports that the node is fully synced 0.02% of the time, indicating the node falsely thought it was at the tip for some periods of time.

As the chart generated from the Parity full node logs below illustrates, the highest block seen on the network figure, in blue, appears potentially incorrect. The highest block number seen on the network figure, sometimes falls in value as time progresses and has remained consistently well behind the actual chain tip (shown in green). On occasion this potentially buggy figure fell towards the height of the verified chain (orange) and our website incorrectly reports the node as in sync. This may be of concern to some Ethereum users, since the Parity full node has many connections to the network, therefore this may be a bug.

Ethereum Parity Full Node Block Height Data – 11 and 12 March 2019 (UTC)

(Source: Ethereum Parity full node logs)

This potential bug could undermine this whole metric for our website, even for the other nodes, as the highest tip seen field may not function appropriately and our figures may be inaccurate. However, we continue to include this metric, since the Nodestats.org website displays the data reported by the nodes, regardless of our view on the integrity of the data. We may look to implement our own improved metric in the future.

One could argue the impact of this potential bug could be severe in some limited circumstances, if exploited by an attacker in the right way. For example a user could accept an incoming payment or smart contract execution as verified, while their node claims to be at the network chain tip. However, the client may not really be at the chain tip and an attacker could exploit this to trick the recipient into delivering a good or service. The attacker would need to double spend at a height the vulnerable node wrongly thought was the chain tip, which could have a lower proof of work requirement than the main chain tip. Although successful execution of this attack is highly unlikely and users are not likely to be using the highest seen block feature anyway.

Conclusion

Like its sister website, Forkmonitor.info, Nodestats.org is very much a work in progress. Along with TokenAnalyst, over the coming months and years, we plan to add more features, such as:

  • Improving the integrity of the data, by being less reliant on what the nodes report and developing our own calculation methodologies
  • Charts & tools for analysing longer term trends
  • Improved granularity of the data
  • Fork detection systems
  • Data related to other peers

For now, Nodestats.org provides a useful tool to assess the approximate system requirements for running Ethereum nodes. At at a very basic level, it also provides mechanisms to assess the reliability of the Ethereum network and its various software implementations. However, we accept that the “% of time in sync” metric may not be reliable, but it does highlight a potential issue.

Anatomy Of The Next Global Financial Crisis

Abstract: We examine a question which many in the crypto-currency community frequently ask: “When is the next global financial crisis going to happen?” We attempt to answer this by first explaining how that since 2008, the epicentre of financial risk seems to have shifted from the banks to the asset management industry. We therefore argue that a repeat of 2008, where retail banking deposits and payment systems are under threat, is unlikely. In particular, we assert that corporate debt investment funds and unconventional debt investment vehicles, encouraged by the deceptively low volatility and low return environment, could be the area where the fragility in the financial system is most significant.

(Ten years since the global financial crisis, as the newspapers from the time exposed to the sunlight turn yellow and pink, at some point credit conditions could tighten significantly again, but will the asset management industry, rather than the banking sector, be the epicentre of risk?)

Overview

Due in part to the timing of its launch, Bitcoin is said to have been born out of the financial chaos and scepticism resulting from the 2008 global financial crisis. Therefore, many Bitcoin investors and members of the crypto-currency community often seem to ask:

When is the next global financial crisis going to happen?

Due to the demand, we will attempt to address the issue.

First, we will look into the question itself. In our view, there appears to be three main assumptions behind the question:

  1. The next global financial crisis will arrive in the next few years and it’s inevitable one will occur every decade or so;
  2. Such a crisis will have a positive impact on the price of Bitcoin;
  3. The next global financial crisis will look similar to the last one, resulting in many questioning the integrity of the banking system and electronic payment systems.

Of these three assumptions, we only really agree with the first one. Although we think the latter two assumptions could possibly hold true, there is significant uncertainty about them.

As for the second assumption, we touched on this issue in March 2018, when we noted that Bitcoin was trading more like a risk-on asset than a safe-haven asset. Of course the Bitcoin price has fallen a lot since then and this could change going forwards. If Bitcoin does respond well in the next crisis (when liquidity is constrained), that will be a huge positive for Bitcoin and the store of value investment thesis. Although, there is no significant evidence for this yet. A decoupling of the Bitcoin price from many of the alternative coins, which more clearly have a risk-on type investment thesis (e.g. world computer or high capacity payment network), is necessary for this to occur, in our view.

As for the third assumption, the mechanics of the next global financial crisis, that is the focus of this report.

Bank Balance Sheets In Developed Markets Are Relatively Healthy

As the famous saying goes, “History doesn’t repeat itself but it often rhymes.” Over the last decade, bank management teams and banking regulators have operated in the shadows of 2008. As a result, bank balance sheets and capital ratios have significantly strengthened. Bank Tier 1 capital ratios in developed markets have improved from around 5% pre-crisis to around 12% today (Figure 1). The more basic ratio of equity to total assets, which is more difficult to manipulate, also illustrates a similar story: improving from c5% to c9% in the period (Figure 2).

Figure 1 – US & UK Bank’s Aggregate CET1 Capital Ratios

(Source: UK aggregate data from the Bank of England, US data from the Federal Reserve)

Figure 2 – US Bank’s Aggregate Tangible Equity to Total Assets Ratio (Banks with over $50 billion in assets)

(Source: Federal Reserve)

Perhaps even more revealing and compelling than the above ratios, is the following more simple chart (Figure 3). It illustrates that the main western banks have not expanded their balance sheets at all since the global financial crisis. Actually, the sample of the nine major banks we have reviewed experienced a significant decline in total assets in aggregate, from US$19.3 trillion in 2008 to US$15.6 trillion in 2018. One could argue that M&A activity is a driver of the below chart, but our point still stands.

Figure 3 – Total Assets Of Selected Banks In Developed Markets – US$ Trillion

(Source: BitMEX Research, Bank Earnings, Bloomberg)

(Note: The chart represents the total reported assets for JP Morgan, Bank of America, Citigroup, Wells Fargo, HSBC, RBS, Deutsche Bank, Credit Suisse and UBS.)

In our view, financial leverage is one of the the primary drivers of financial risk. The epicentre of risk in the financial system appears to have shifted since 2008. In 2008, the risk was caused by leverage in the banking system and the interrelationships between this and the securitisation of the mortgage market. Today, the equivalent risk is leverage in the asset management industry and in particular the corporate debt sector, driven by the deceptively low volatility environment.

Growth In Leverage In The Asset Management Industry

The asset management industry is far more opaque than banking and determining the degree of leverage is far more challenging. Therefore, it is difficult to conclude on either the extent of leverage in the asset management industry or the timing of any financial crisis related to this leverage.

A 2015 Bank of International Settlement (BIS) report entitled “Leverage on the buy side,”  focused on the shift in risk from the banking system to the asset management industry. The report notes that while investment fund leverage has been reasonably stable in equities, in the fixed income area, it has grown considerably since 2008, especially in emerging markets. The BIS report concludes with the following:

Leverage in the banking system was an important ingredient in the 2008 global financial crisis. Since then, asset managers (the “buy side”) have quickly increased their footprint in global financing, helped by the sharp retrenchment of banks nursing their balance sheets back to health. Balance sheet information for investment funds is much less readily available than for highly regulated banks. Using information provided by a market data vendor, we found that leverage on the buy side is not negligible, although it seems to vary considerably depending on the type of fund. Equity fund portfolios seem to be minimally leveraged, while fixed income funds tend to resort abundantly to borrowed money.

(Source: BIS)

The BIS report used data from investment fund flow specialist EPFR, and although we agree with the report’s conclusions, it is difficult to formulate a strong view of the data’s reliability. Although we have not found good sources of global data ourselves, US-domiciled investment funds over a certain size are required to submit data to the SEC about the extent of leverage used. The SEC has complied this data since Q2 2013 and we have summarised the main trends in the below charts (Figures 4, 5, and 6).

The data shows that, unlike the banking sector, the asset management industry has expanded considerably since 2008 (Figure 4). At the same time, leverage also appears to have increased, although producing a clear chart since 2008 illustrating this is difficult.

Figure 4 – US Fund Industry Gross Asset Value (US$ billion)

(Source: BitMEX Research, SEC)

Although there are competing methodologies, the most basic method for establishing the degree of leverage for an investment fund is calculating the gross asset value over the net asset value, sometimes referred to as the gearing ratio. Unfortunately the time span in the below chart (Figure 5) is limited, but it appears to indicate a moderate expansion of leverage, at least in the hedge fund sector.

Figure 5 – US Private Fund Industry Gearing Ratio – Gross Asset Value/Net Asset Value

(Source: BitMEX Research, SEC)

The gearing ratio underestimates true leverage, by ignoring the impact of derivatives. Disclosure of the notional value of derivative exposure is also required by the SEC. The below chart illustrates the growth in usage of derivatives by US-based hedge funds.

Figure 6 – US Private Fund Industry – Hedge Funds – Notional Value Of Derivatives/Net Asset Value

(Source: BitMEX Research, SEC)
(Note: Adjustments were made to reflect changes in how the SEC reports the data.)

New Corporate Debt Market Vehicles

In addition to the increased use of leverage in the fixed income market by investment funds, the mechanics of the debt markets are becoming increasingly complicated and opaque. The replacement of the role of the banks in the corporate debt markets, has resulted in the rapid growth of a whole range of interrelated, non-mutually exclusive investment structures. Some of these structures are summarized in the table below.

Type of Debt Description/Comments Reference
Collateralized Loan Obligations (CLOs) CLOs are when a group of loans from multiple companies, are pooled together to form a security. The product is typically split into different tranches, lower risk tranches with lower returns and higher risk tranches with higher returns. Investors in the highest risk tranche are the last to be paid in the event of any insolvencies.

Typical buyers of these products are pension funds, insurance companies and hedge funds. They are particularly popular with yield hungry Asian investors.

Market growth – Figure 7
Leveraged Loans These are typically variable rate loans provided to companies who are already highly indebted. In the majority of cases the loan is fully unsecured. The typical holders of such instruments are pension funds and other private investors.

The Bank of England recently estimated the size of the leveraged loan market globally at US$2.2 trillion and compared it to the size of the US subprime mortgage market in 2006 (US$1.3 trillion).

Market growth – Figure 8

Credit quality – Figure 15

Private debt deals This is similar to the leveraged loan market, except the debt does not normally trade on a secondary market. Market growth – Figure 9
Bond fund ETFs and mutual funds ETFs have expanded in popularity in all asset classes in the period, corporate bond funds are no exception. Market growth – Figure 10
Private Equity Credit quality – Figure 16

(Note: The fields in the above table are not meant to be mutually exclusive)

As the following charts from various sources indicate, all these non-bank mechanisms for providing corporates with financing have grown considerably since the last global financial crisis.

Figure 7 – Size of Collateralized Loan Obligation (CLO) Market – US$ billion

(Source: Citi, FT)

Figure 8 – Size of US Leveraged Loan Market – US$ billion

(Source: S&P, FT)

Figure 9 – Size of Private Debt Market – US$ billion

(Source: Bank of America, FT)

Figure 10 – Size of the Top Bond ETFs Targeting US Investors – US$ Billion

(Source: BitMEX Research Bloomberg)

(Note: The chart represents the sum of the market capitalisations of the following bond ETFs: iShares Core U.S. Aggregate Bond ETF, Vanguard Total Bond Market ETF, iShares iBoxx $ Investment Grade Corporate Bond ETF, Vanguard Short-Term Corporate Bond ETF, Vanguard Short-Term Bond ETF, Vanguard Intermediate-Term Corporate Bond ETF, iShares J.P. Morgan USD Emerging Markets Bond ETF, Vanguard Total International Bond ETF, iShares MBS Bond ETF, iShares iBoxx $ High Yield Corporate Bond ETF, PIMCO Enhanced Short Maturity Strategy Fund, Vanguard Intermediate-Term Bond ETF, iShares Short-Term Corporate Bond ETF, SPDR Barclays High Yield Bond ETF, iShares Short Maturity Bond ETF)

Corporate Debt Markets Conditions

As Figure 11 below illustrates, corporate debt levels have increased considerably since 2008, with gross debt of Russell 3000 companies now totalling US$11 trillion, compared to being just over US$8 trillion at the time of the last crisis. Corporations have taken advantage of low interest rates and the new investment products mentioned above, to borrow money at record levels.

However, as the red line on Figure 11 illustrates, Russell 3000 corporate balance sheet conditions still appear reasonably healthy, with an aggregate net debt to EBITDA of just under 2.5x.   Although this ratio has been increasing in the last few years, it is nowhere near as large as the c3.7x level which was present before the 2008 financial crisis. This perceived strength is caused by a few large tech giants hoarding cash and the strong economy boosting earnings. If the economy turns, corporate balance sheets could start to look unhealthy again, as earnings fall.

Figure 11 – Corporate Debt levels

(Source: BitMEX Research, Corporate Data, Bloomberg)
(Note: The figures consist of aggregate data for all the Russell 3000 companies.)

There is a considerable volume of corporate bonds set to mature in the coming years. This could exacerbate the impact of any liquidity crisis or stress in the fixed income sector. As our analysis shows (Figure 12), US$ 880 billion of corporate debt in the US will mature in 2019.

Figure 12 – Corporate Bond Maturity Wall – US$ billion

(Source: BitMEX Research, Bloomberg)
(Note: Figures are based on a database of around 6,400 US corporate bonds with an aggregate amount outstanding of US$5.7 trillion.)

Perhaps the most alarming indicator is the quality of the corporate debt. Figure 13 shows the credit rating distribution of the outstanding investment grade corporate debt over time.  At the end of 2018, almost 50% of the bonds had been rated at the lowest possible rating for investment grade securities, a far higher proportion than any time in the past 30 years. Figure 14 indicates that the situation from 2021 will get even worse, when the overwhelming majority of corporate debt maturing will be at the lowest investment grade rating.

Figure 13 – S&P Credit Rating Distribution of US Corporate Bonds Over History

(Source: Bloomberg, HSBC USD IG index constituents, including financial and non financial companies)

Figure 14 – S&P Credit Rating Distribution of US Corporate Bonds Outstanding By Maturity

(Source: BitMEX Research, Bloomberg)
(Note: Figures are based on a database of around 6,400 US corporate bonds with an aggregate amount outstanding of US$5.7 trillion.)

Evaluating the credit quality of some of the less conventional debt vehicles mentioned above, is more challenging. However, a recent report from Moody’s indicated that there has been a significant deterioration in the level of protection for investors in the leveraged loans market, as Figure 15 below illustrates.

Figure 15 – Moody’s Assessment Of The Covenant Quality For Leveraged Loans (US & Canada)

(Source: Moody’s, Bloomberg)
(Note: 5.0 is the weakest score, 1.0 is the strongest.)

Figure 16 – Average Total Debt To EBITDA Multiple Of Private Equity Deals

(Source: S&P, FT)

Low Volatility Environment

In our view, the unconventional monetary policies in advanced economies have squeezed investment returns and volatility, all while reducing borrowing costs; this has created an incentive for asset managers to use more leverage and take on more risk. At the same time the same policies have encouraged corporates to take on more debt. It is the fixed income sector, more than any other, which has been impacted by this low volatility. “Risk Parity” type investment strategies have become increasingly popular, where funds manage risk by constructing portfolios according to the risk (volatility) of each asset class and then using leverage to increase returns. The leverage mitigates the impact of lower returns from the higher weighting to the lower risk assets. This typically involves a larger weighting to fixed income, rather than equities, while incorporating more leverage to offset the low returns of these supposedly lower risk assets.

In February 2018, there was a sharp increase in volatility as the VIX skyrocketed and investment strategies focused on shorting the VIX, such as the Velocity Shares Daily Inverse VIX ETN, plummeted in value to almost zero. This was discussed in the March 2018 edition of the BitMEX Crypto Trader Digest. The victims of this were a small number of opportunistic investors looking for easy returns and the impact of the “volocaust” was limited on the rest of the financial system. However, in a way, the February 2018 event was a microcosm for what is happening more generally in fixed income markets. This time the more mainstream investors, are taking advantage of artificially low volatility and cheap borrowing costs. At some point the market will correct, and the impact of this will be far greater than in February 2018, when a multi-trillion dollar asset class unwinds, rather than one only worth several hundred million dollars.

The sequence of events can be described as follows, with various different factors contributing to exacerbate risk:

  1. Some catalyst occurs, causing a sharp increase in volatility.
  2. Investors will need to de-risk their portfolios, concentrating first in the most liquid markets, fixed income.
  3. In the most liquid markets, machines dominate trading and machines are likely to all withdraw liquidity at the same time.
  4. Fixed income markets become volatile, illiquid and dysfunctional, as investors rush for the exit.
  5. Securitised bond-based assets, such as CLOs and Bond ETFs, trade at a significant discount to their net asset values.
  6. The contagion spreads across other liquid asset classes, such as equities.
  7. Over the coming years the newly-established components of the debt machine begin to dry up; corporates struggle to refinance and the economy suffers.

Of course we do not know what will be the main catalyst leading to increased volatility. It could be a geopolitical event, excessive levels of emerging market US Dollar-based debt, high levels of leverage in the Chinese asset management industry, passive ETFs, high frequency traders, the contraction of central bank balance sheets too quickly, a large unexpected corporate bankruptcy, the Eurozone debt crisis, even a catastrophic consensus bug in Bitcoin causing volatility ect ect…  

The point is, that whatever the particular event is, it doesn’t really matter. What does matter is the inherent instability and fragility of the financial system, driven by artificially low volatility and excessive leverage. Many may point their fingers at the particular catalyst after the event and blame it for the crisis, but that could be somewhat intellectually dishonest.

Conclusion

Banks are more crucial to the financial system and society than asset managers. If asset managers come under pressure, whilst some high net worth individuals may experience a write down in their assets; retail and corporate deposits should be safe; and therefore the coming crisis could be less intense than 2008. However, critically, the potential for government intervention to mitigate the impacts of the crisis may be more limited than in 2008.

Firstly and most obviously, the toolkit available to central bankers has been greatly diminished, with interest rates already low and their balance sheets already large. Secondly, and perhaps more importantly, is the political side of things. One cannot know for sure, but the typical people behind the Trump, Brexit, or the Yellow Vest movement may not be supportive of certain kinds of government intervention in financial markets.

In today’s more “populist” political climate, it may be more challenging to justify programs such as quantitative easing or other programs designed to increase asset prices at the relative expense of those earning median type salaries, who don’t own a large pool of financial assets. Therefore, in the next crisis, managing the perceived risk of a “political uprising” could significantly reduce the scope of action central bankers may be willing to take.

Keep in mind, there was also political opposition to central bank policy in the aftermath of 2008, with the rebellion peaking in around 2011. Another key difference this time is that available tools to those leading the rebellion then, such as social media, are now more developed. Political uncertainty in the West seems to have increased since 2008. If this uncertainty begins to interact with financial volatility, risks could be exacerbated.

As for when such a crisis will occur, we obviously do not know. In our view, the charts in this report identify a problem, but they do not seem to suggest that we are necessarily right on the precipice of a major crisis; it could be several years away. As for how to profit from such events, this is perhaps even more challenging than predicting their timing. Maybe one could construct a portfolio of VIX calls, long dated corporate bond ETF puts, index-linked government bonds, hedge funds specializing in volatility, gold and maybe to a lesser extent, even Bitcoin. Again, although one cannot know when these events will occur, perhaps now is a time to adjust one’s investment portfolio.

The BitMEX Insurance Fund

Abstract: In this piece, we explore why the BitMEX insurance fund is needed and how it operates. We compare the BitMEX insurance fund model, to the systems utilized by other more traditional leveraged market places (e.g. CME). We conclude that crypto-currency trading platforms which offer leverage and a capped downside face some unique challenges, when compared to traditional institutional trading platforms. However, the growth of BitMEX’s insurance fund provides a reasonable level of assurance to winning traders that they will be able to attain their expected profits.

(BitMEX Co-founder & CEO Arthur Hayes (left) and CME Chairman & CEO Terrence Duffy (right))

Leveraged Trading Platforms

When one trades on a derivatives trading platform such as BitMEX, one does not trade against the platform. BitMEX is merely a facilitator for the exchange of derivatives contracts between third parties. A key feature of the BitMEX platform is its leverage, where traders can deposit Bitcoin, then leverage it up, (in theory up to 100x) and purchase contracts with a notional position size far higher than the value of the Bitcoin they deposited.

The combination of offering both leverage and the ability for traders to trade against each other implies winners are not always guaranteed to get back all the profits they expect. Due to the leverage involved, the losers may not have enough margin in their positions to pay the winners.

Consider the following simplified example, where the platform consists of two customers trading against each other:

Trader A Trader B
Direction of trade Long Short
Margin 1 BTC 1 BTC
Trade execution price $3,500
Leverage 10x 10x
Notional position size 10 BTC 10 BTC
Current BTC price $4,000
Expected Profit $5,000 ($5,000)

In the above example, the winning trader A expects to make a profit of $5,000, which is greater than the amount of capital the loser, trader B, put up as collateral for the trade (one Bitcoin is worth $4,000). As such, trader A can only make 1 BTC ($4,000) in profits, perhaps making him/her feel slightly disappointed.

Traditional Exchanges

Traditional exchanges like the Chicago Mercantile Exchange (CME) do not share this problem to the same extent as crypto-platforms such as BitMEX. In traditional leveraged trading venues, there are often up to five layers of protection, which ensure winners get to keep their expected profits:

  1. In the event an individual trader makes a loss greater than the collateral they have in their account, such that their account balance is negative, they are required to finance this position by injecting more funds into their account. If they are unable or unwilling to do so, their broker may initiate legal proceedings against the trader, forcing the trader to provide the funds or file for bankruptcy. Each trader must use a broker, who may evaluate the balance sheet and capital of each of their clients, providing each client a custom amount of leverage depending on the assessment of their particular risk.
  2. In traditional derivative markets, traders are not typically given direct access to trading platforms. Instead, clients access the market through their brokers (clearing members), for instance investment banks such as JP Morgan or Goldman Sachs. In the event a trader endures losses and the debt cannot be recovered, the broker is required to pay the exchange and make the counterparties whole. From the perspective of the exchange, these brokers are sometimes referred to as clearing members.
  3. In the event of a clearing member default, the centralised clearing entity itself is often required to make the counterparties whole. In many circumstances clearing and settlement is conducted by a separate entity to the one operating the exchange. The clearing house often has various insurance funds or insurance products in order to finance clearing member defaults.
  4. In the event of a clearing member failing and the centralized clearing entity also having insufficient funds, in some circumstances the other solvent clearing members are expected to provide capital.
  5. Many of the larger clearing houses (and perhaps even the larger brokers) are often considered systemically important for the global financial system by financial regulators. Therefore in a doomsday scenario where it looks likely that a major clearing house could fail, it is possible the government may step in and bail out traders, to protect the integrity of the financial system. Traders and institutions often have massive notional positions (multi-trillions of USD) hedged against other positions or instruments, typically in the interest rate swap market. Therefore it is crucial that the main clearing houses remain solvent or the entire financial system could collapse.

CME

CME is the world’s largest derivatives exchange, with an annual notional trading volume of over one quadrillion USD; it is over 1,000x as large as BitMEX. CME has several buckets of safeguards and insurance to provide protection in the event that a clearing member defaults. The funds are financed in various ways:

  • Contributions from CME
  • Contributions from clearing members
  • Bonds placed by clearing members, redeemable by the clearing funds in the event of member default

CME Clearing’s Various Safeguards and Insurance Funds (2018)

Base Financial Safeguards Package
Guaranty Fund Contributions $4.6 billion
Designated Corporate Contributions $100 million
Assessment Powers $12.7 billion
IRS Financial Safeguards Package
Guaranty Fund Contributions $2.9 billion
Designated Corporate Contributions $150 million
Assessment Powers $1.3 billion

(Source: CME)

In exceptional circumstances, CME also has the power to apply “assessment powers” against non-defaulting clearing members to help finance the cost of defaulting members when all the other insurance funds have been drained. The value of the assessment powers is capped at 2.75x for each clearing member guarantee fund per member default.

Based on the size of the insurance funds in the above table, CME has around US$22 billion in various insurance funds. This represents around 0.002% of CME’s annual notional value of trading.

BitMEX and other crypto-currency trading platforms that offer leverage cannot currently offer the same protections to winning traders as traditional exchanges like CME. Crypto-currency is a retail-driven market and customers expect direct access to the platform. At the same time, crypto-trading platforms offer the ability to cap the downside exposure which is attractive for retail clients, therefore crypto-exchanges do not hunt down clients and demand payments from those with negative account balances. Leveraged crypto-currency platforms like BitMEX offer an attractive proposition to clients: a capped downside and unlimited upside on a highly volatile underlying asset. But traders pay a price for this, as in some circumstances there may not be enough funds in the system to pay winners what they expect.

BitMEX Insurance Fund

In order to mitigate this problem, BitMEX developed an insurance fund system, to help ensure winners receive their expected profits, while still limiting the downside liability for losing traders.

When a trader has an open leveraged position, if their maintenance margin is too low, their position is closed forcefully (i.e. liquidated). Unlike in traditional markets, the trader’s profit and loss does not reflect the actual price their position was closed on the market. On BitMEX if a trader is liquidated, their equity associated with the position always goes down to zero.

Example trading position
Direction of trade Long
Margin 1 BTC
Bitcoin price (at opening) $4,000
Leverage 100x
Notional position size 100 BTC = $400,000
Maintenance margin as percentage of notional position 0.5%

In the above example, the trader has a 100x long position.  If the mark price of Bitcoin falls 0.5% (to $3,980) the position is liquidated and the 100 Bitcoin position needs to be sold on the market. From the perspective of the liquidated trader, it does not matter what price this trade executes at, whether its $3,995 or $3,000, either way they lose all the equity they had in their position, they lose the entire one Bitcoin.

Now, assuming there is a liquid market, the bid/ask spread should be tighter than the maintenance margin. In this scenario, the liquidations result in contributions to the insurance fund (e.g. if the maintenance margin is 50bps, but the market is 1bp wide), then the insurance fund will rise by almost as much as the maintenance margin when a position is liquidated. Therefore, as long as healthy liquid markets persist, the insurance fund should continue to grow at a steady pace.

The two graphics below attempt to illustrate the above example. In the first chart, at the time of liquidation, market conditions are healthy and the bid/ask spread is narrow, at just $2. As such, the closing trade occurs at a price higher than the bankruptcy price (the price where the margin balance is zero) and the insurance fund benefits. In the second chart, at the time of liquidation the bid/ask spread is wide. The closing trade occurs at a price lower than the bankruptcy price, therefore the insurance fund is used to ensure the winning traders receive their expected profits. This may seem like it would be a rare occurrence, but there is no guarantee such healthy market conditions will continue, especially in times of heightened price volatility. In these times, the insurance fund can drain much faster than it builds up.

Illustrative example of an insurance contribution – Long 100x with 1 BTC collateral

(Note: The above illustration is based on opening a 100x long position at $4,000 per BTC and 1 Bitcoin of collateral. The illustration is an oversimplification and ignores factors such as fees and other adjustments. The bid and offer prices represent the state of the order book at the time of liquidation. The closing trade price is $3,978, representing $1 of slippage compared to the $3,979 bid price at the time of liquidation.)

Illustrative example of an insurance depletion – Long 100x with 1 BTC collateral

(Notes: The above illustration is based on opening a 100x long position at $4,000 per BTC and 1 Bitcoin of collateral. The illustration is an oversimplification and ignores factors such as fees and other adjustments. The bid and offer prices represent the state of the order book at the time of liquidation. The closing trade price is $3,800, representing $20 of slippage compared to the $3,820 bid price at the time of liquidation.)

The BitMEX insurance fund currently sits at around 21,000 Bitcoin or around US$70 million based on current Bitcoin spot prices. This represents only 0.007% of BitMEX’s notional annual trading volume, of around one trillion USD. Although this is slightly higher than CME’s insurance funds as a proportion of trading volume, winning traders on BitMEX are exposed to much larger risks than CME traders through the following:

  • BitMEX does not have clearing members with large balance sheets and traders are directly exposed to each other.
  • BitMEX does not demand payments from traders with negative account balances.
  • The underlying instruments on BitMEX are more volatile than the more traditional instruments available on CME.

Auto-deleveraging

In the event that the insurance fund becomes depleted, winners cannot be confident of taking home as much profit as they are entitled to. Instead, as we described above, winners need to make a contribution to cover the losses of the losers. This process on BitMEX is called auto-deleveraging.

Auto-deleveraging has not occurred on the BitMEX Bitcoin perpetual swap contract since March 2017. In early March 2017, the SEC disapproved the Winklevoss’ application for the COIN Bitcoin ETF. On that day, the market dropped 30% in five minutes. The sharp price drop depleted the insurance fund entirely. Many XBTUSD shorts were ADL’d (Automatic Deleveraging) and their profits were capped.

Although the BitMEX insurance fund has grown considerably since then, crypto-currency trading is a volatile and uncertain industry. Despite the current healthy periods of reasonably high liquidity, sharp movements in the Bitcoin price going forwards is a possibility, in our view. One cannot be certain that ADL’s won’t occur again, even on the BitMEX Bitcoin perpetual swap contract.

Insurance Fund Data

Although the absolute value of the insurance fund has grown, as the charts below show as a proportion of other metrics from the BitMEX trading platform, such as open interest, the growth is less pronounced.

BitMEX Insurance Fund – Daily data since January 2018

(Source: BitMEX)

BitMEX Insurance Fund as a proportion of the BitMEX Bitcoin perpetual swap open interest – Daily data since January 2018

(Source: BitMEX)

Incentives

Assuming the insurance fund remains capitalized, the system operates under a principle where those who get liquidated pay for liquidations, a losers pay for losers model. While this approach may be considered somewhat novel, in a way there is a degree of fairness to it, that isn’t present in some alternative models mentioned above. It begs the question, why should traders who do not engage in risky leveraged bets have to pay for those that do?

Conclusion

Although 21,000 Bitcoin inside an insurance fund, worth around 0.1% of the total Bitcoin supply may seem large, BitMEX cannot offer the same robust guarantees to winning traders, compared to those provided by traditional leveraged trading platforms. While the insurance fund has achieved a healthy size, it may not be large enough to give winning traders the confidence they need in the volatile and unpredictable bumpy road ahead in the crypto-currency space. Given such volatility, it’s not impossible that the fund is drained down to zero again.

比特币随机数模式之谜

摘要:我们注意到比特币区块头中的随机数值的分布似乎并不是随机的,出现了无法解释的间隙,随机数在间隙中的出现次数更少。然后我们推测为什么会出现这种情况,并提供了说明这种现象的图表。虽然在我们看来,解释的出发点是好的,但它仍然是一个谜。

概述和近期的推文

比特币随机数是构成区块头的一部分,矿工用它来提供熵,作为工作过程证明的一部分,努力找到满足难度要求的哈希。虽然它会取决于如何配置挖掘软件和硬件,但理论上随机数值的分布应该是随机的。在 2009 年,当 Satoshi 被认为是一个重要的矿工时(就像我们在早前的文章中所讨论的那样),随机数值遵循一种特定的模式。

2019 年 1 月 4 日,@100trillionUSD 在推特上发布了一张图片,说明了比特币的随机数值的分布。它似乎表明,从 2010 年年中到 2016 年初,随机数值是随机的,在那之后出现了四个随机数减少的神秘区域。

几天后,在 2019 年 1 月 7 日,@khannib 注意到 Monero 似乎也有不寻常的随机数值分布。Monero 硬分叉可能阻止了ASIC 的使用,似乎让分布再次随机化,这可能表明 ASIC 会导致这种模式。

2019 年 1 月 23 日,TokenAnalyst 通过对相关矿池的随机值进行着色,对比特币随机数值的分布模式进行了进一步的探索。

来自 TokenAnalyst 的另一条推文暗示 Antpool 是随机数值意外分布的主要原因,而 Bitfury 和 Slushpool 具有的随机值数对 “空白区间” 的产生作用可能不大。

新的随机数值分布散点图

我们复制了上述分析,产生了相似的散点图(从 2018 年开始);试图对这个问题深入了解。

我们还为 Antpool 、BTC.com 、F2Pool 、Slushpool 和 Bitfury 制作了单独的散点图。这些图表似乎与 TokenAnalyst 的数据一致,其中“空白区间”对于 Antpool 而言,比 Slushpool 和 Bitfury 更加清晰可见。虽然就 Slushpool 而言,空白区间依然可见,但比较微弱。Bitfury 可能没有找到足够的区块供人们观察到清楚的模式。统计分析也可能有用,但用人脑来解读这些散点图可能与某些形式的统计作用一样。

 

比特币随机数值分布 – 所有随机值(自 2018 年以来)

(资料来源:BitMEX 研究)

 

比特币随机数值分布 –  Antpool(自 2018 年以来)

(资料来源:BitMEX 研究)

 

比特币随机数值分布 –  BTC.com(自 2018 年起)

(资料来源:BitMEX 研究)

 

比特币随机数值分布 –  F2Pool(自 2018 年起)

(来源:BitMEX 研究)

 

比特币随机数值分布 –  Slush(自 2018 年以来)

(来源:BitMEX 研究)

 

比特币随机数值分布 –  Bitfury(自 2018 年以来)

(来源:BitMEX 研究)

比特币现金 ABC

比特币现金 ABC 也与比特币有着相同的随机数值分配模式。

比特币现金 ABC 随机数值分配 – (自 2018 年以来)

(来源:BitMEX 研究)

 

AsicBoost

隐性 AsicBoost 可能是这一模式的成因之一或是其起因。许多人推测在隐性 AsicBoost 算法启动时,这种模式便开始出现;而这一模式可能是在实施隐性 AsicBoost 中的一个巧合,需要对随机数进行操纵。然而,当2018年人们认为隐性 AsicBoost 在比特币中已经停止使用时,这种模式在延续。但有可能是,尽管隐性 AsicBoost 本身被停用了,但固件中的巧合仍然存在。

在下面的图表中,我们观察了使用显性 AsicBoost 挖掘的区块的随机数值的分布。同样,该模式仍然可见,不过是微弱的。这可能表明该模式与隐性 AsicBoost 无关,但还远未定论。

比特币随机数值分布 – 显性 AsicBoost 区块(自 2018 年以来)

(来源:BitMEX 研究)

结论

目前,随机数值在比特币上不寻常的分布仍然是一个谜。社群可能希望进一步深入研究这个问题,并进行更多分析,例如更细致地检查矿池软件和 ASIC 。我们猜想这只不过是一个带有良性原因的毫无意义的反常现象;但在比特币上的这样一个谜,可能会吸引一些分析师的兴趣。

欢迎转载,请注明文章来自

BitMEX (www.bitmex.com)

The Mystery Of The Bitcoin Nonce Pattern

Abstract: We note that the distribution of nonce values in the Bitcoin block header does not appear to be random, with unexplained gaps emerging, where nonces occur less often. We then speculate on why this may be the case and provide charts illustrating the phenomenon. Although in our view the explanation for this is likely to be benign, it remains a mystery.

Overview and Recent Tweets

The Bitcoin nonce forms part of the block header, which is used by miners to provide entropy as part of the Proof of Work process, to try and find a hash meeting the difficulty requirement. Although it may depend on how mining software and hardware is configured, in theory the distribution of the nonce values should be random. In 2009, when Satoshi is presumed to have been a significant miner, the nonce value followed a particular pattern, as we discussed in an earlier piece.

On 4th January 2019, @100trillionUSD tweeted a graphic illustrating the nonce value distribution for Bitcoin. It seemed to show that the nonce value was random from mid 2010 to the start of 2016, after which point four mysterious regions appeared, where nonces occurred less often.

A few days later, on 7th January 2019, @khannib noted that Monero also appeared to have an unusual nonce value distribution. The Monero hardfork, which may have prevented the usage of ASICs, appears to have made the distribution random again, perhaps indicating ASICs cause the pattern.

On 23rd January 2019, TokenAnalyst dug further into the Bitcoin nonce value distribution pattern, by colouring the nonce values for the relevant mining pools.

A further tweet from TokenAnalyst implies that Antpool is a major cause of the unexpected nonce value distribution, while Bitfury and Slushpool have nonce values which perhaps do not significantly contribute to the “white spaces”.

New Nonce Value Distribution Scatter Charts

We have replicated the above analysis, producing similar scatter charts (starting in 2018); in an attempt to shed more light onto the issue.

We have also produced an individual scatter chart for Antpool, BTC.com, F2Pool, Slushpool and Bitfury. The charts appear to agree with TokenAnalyst’s data, in that the “white spaces” are more clearly visible for Antpool, than for Slushpool and Bitfury. Although, with respect to Slushpool the white spaces are still visible, but they are more faint. Bitfury may not have found enough blocks for one to see a clear pattern. A statistical analysis may also be possible, although the human brain interpreting these scatter charts may be just as useful compared to some forms of statistical analysis.

 

Bitcoin nonce value distribution – All nonces (Since 2018)

(Source: BitMEX Research)

 

Bitcoin nonce value distribution – Antpool (Since 2018)

(Source: BitMEX Research)

 

Bitcoin nonce value distribution – BTC.com (Since 2018)

(Source: BitMEX Research)

 

Bitcoin nonce value distribution – F2Pool (Since 2018)

(Source: BitMEX Research)

 

Bitcoin nonce value distribution – Slush (Since 2018)

(Source: BitMEX Research)

 

Bitcoin nonce value distribution – Bitfury (Since 2018)

(Source: BitMEX Research)

Bitcoin Cash ABC

Bitcoin Cash ABC also shares the same nonce value distribution pattern as Bitcoin.

Bitcoin Cash ABC nonce value distribution – (Since 2018)

(Source: BitMEX Research)

 

AsicBoost

It is possible that covert AsicBoost may be either a contributing factor or cause of the pattern. The pattern began to emerge around the time many speculate covert AsicBoost started; and this pattern could be a quirk in the implementation of covert AsicBoost, which requires the manipulation of the nonce. However, the usage of covert AsicBoost is believed to have stopped in Bitcoin in 2018, when this pattern continued. Although it’s possible that despite covert AsicBoost itself being stopped, the quirk in the firmware remains.

In the below chart we have looked at the nonce value distribution for blocks mined using overt AsicBoost. Again, the pattern remains visible, albeit faintly. This may suggest the pattern has nothing to do with covert AsicBoost, but it’s far from conclusive.

Bitcoin nonce value distribution – Overt AsicBoost blocks (Since 2018)

(Source: BitMEX Research)

Conclusion

For now, the unusual nonce value distribution on Bitcoin remains a mystery. The community may wish to dig into this issue further and conduct more analysis, such as reviewing mining pool software and ASICs in more detail. We would guess this is nothing more than a meaningless anomaly with a benign cause; however a mystery in Bitcoin like this could be intriguing to some analysts.

跟踪 ICO 发行者分配给自己的 240 亿美元代币

摘要:这是我们关于 ICO 的第三篇重要文章。在 2017 年 9 月的第一篇文章中,我们的重点说明了 ICO 团队成员之间的相互关系。在 2018 年 10 月的第二篇文章中,我们追踪了 ICO 资产账户中的以太坊余额。与TokenAnalyst合作,这篇文章重点关注以太坊网络上 ICO 代币自身的数字资产余额。该报告的主题是关于代币,其中团队控制持有的数字资产在发行时价值达到惊人的 242 亿美元(但实际上流动性太低,无法真正实现这一价值)。今天这一数字已下降到约 50 亿美元,差异主要是由于代币的市值出现下跌,以及 15 亿美元的代币从团队地址集群转出(可能进行了处置)。

--Macintosh HD:Users:SergiWFT:Downloads:icographic-1024x946.png

(资料来源:BitMEX 研究)

(注:来自 我们 2017 年 9 月 的交互式图形提醒我们 ICO 团队成员之间存在的各种相互关联)

团队控制的代币资产(自有代币) – 汇总数据

单位:十亿美元
分配给代币团队的 ICO 代币价值 21.5
ICO 之后向团队的发放量 2.7
向团队所控制的钱包的发放总量 24.2
从团队地址集群转出的代币(可能是出售) (1.5)
代币价格变化带来的盈利 /(亏损) (12.0)
Noah 的净影响(代币销毁) (4.4)
EOS 的净影响 (1.2)
目前团队持有 5.0
(资料来源:BitMEX 研究,TokenAnalyst,以太坊区块链,Coinmarketcap(代币价格信息))

(注:价格数据截至 2019 年 1 月,代币数据截至 2018 年 12 月,基于 108 种代币的数据)

在 ICO 项目团队发放给自己的价值 240 亿美元代币中,因代币价格下降而损失 了54%的价值。如果使用每种代币的单价峰值来计算,团队自己持有的代币最高价值超过 800 亿美元。这个更大的数字,意味着与峰值相比出现了 700 亿美元的“亏损”。由于缺乏流动性,使得峰值价值具有很高的不确定性,而且授予团队的多数代币实际上没有成本,因此将此类价格变动归为亏损可能并不恰当。与 ICO 投资者不同,团队并没有支付发行价或进行初始投资。然而,一些交易活动是按这些高得离谱的估值成交的,因此我们认为考虑这些数据仍然是有意义的,但同时也要牢记上述提醒。

根据目前无法变现的现货价格,ICO 团队似乎仍拥有约 50 亿美元的自有代币,这些钱实际上是零成本获得的,具体取决于人们采用何种观点。与此同时,基于一些代币从团队地址集群转出,团队可能通过销售代币已经实现了 15 亿美元的收益。尽管这个数字也可能被高估了,因为代币可能由于多种原因从团队地址集群转出。

计算方法的数据提示和不足

  • 很多此类代币的流动性很低,因此美元价值可能被严重高估,这对于初始分配、当前价值和任何亏损的价值都适用。在某些情况下,给予团队的代币(例如 Veritaseum 或 Noah 等项目)的价值相对于代币的实际交易量过于庞大,十分可笑。因此,根据代币的交易价格来估算团队持有的资产是不切实际的。
  • 生成此数据集所涉及的挑战和不确定性围绕着将代币分配给团队地址集群的过程。TokenAnalyst 进行了这种分配。使用的方法并不完善,我们并未深入研究个别项目。通过分析以太坊区块链上的代币智能合约和交易模式,并应用机器学习类型技术来建立每个项目团队控制的地址集群,由此获得数据。因此数据是概率估计,在单个项目的层面上可能并不准确。然而,本报告的主要动机是产生关于团队在以太坊持有的ICO 代币的宏观数据。虽然这种分析产生的结果远非完美,但我们相信人们可以从分析中得出合理的宏观结论。
  • 如上所述,我们的分析以对智能合约数据的审查和交易模式为基础,而非单个项目的文件和政策。因此,我们可能将代币作为包括在团队结余之内的一部分,但实际上它们是以其他形式的储备、托管或其他类别的一部分持有,在这种情况下将代币归于团队自有资金是不准确的。
  • 该数据假设发行日期与在 Coinmarketcap 上出现第一个价格的日期相同,这个假设可能不可靠。

汇总数据

发送至团队控制的地址集群的代币价值(自有代币) – 百万美元 – 前 10 名

(资料来源:BitMEX 研究、TokenAnalyst、以太坊区块链、Coinmarketcap(代币价格信息))

(注:代币数据截至 2018 年 12 月,数据基于发行时的价格)

团队控制资产(自有代币)的价值损失 – 百万美元 – 前 10 名

(资料来源:BitMEX 研究、TokenAnalyst、以太坊区块链、Coinmarketcap(代币价格信息))

(注:价格数据截至 2019 年 1 月,代币数据截至 2018 年 12 月)

团队控制的地址集群中代币价值(自有代币)的损失比例 – 前 10 名

(资料来源:BitMEX 研究、TokenAnalyst、以太坊区块链、Coinmarketcap(代币价格信息))

(注:价格数据截至 2019 年 1 月,代币数据截至 2018 年 12 月)

从团队控制的地址集群转出的代币(自有代币)价值 – 百万美元 – 前 10 名

(资料来源:BitMEX 研究、TokenAnalyst、以太坊区块链、Coinmarketcap(代币价格信息))

(注:价格数据截至 2019 年 1 月,代币数据截至 2018 年 12 月。Huobi 和 Qash 是交易所,代币显示已被发送到各自平台。上述数字可能代表了出售/“套现”,但转账也可能有其他原因)

团队控制的地址集群中代币的当前价值(自有代币) – 百万美元 – 前 10 名

(资料来源:BitMEX 研究、TokenAnalyst、以太坊区块链、Coinmarketcap(关于代币价格信息))

(注:价格数据截至 2019 年 1 月,代币数据截至 2018 年 12 月)

原始数据 – 团队持有的自有代币 – 百万美元

代币 ICO 的价值 ICO 后发行 从团队地址集群转出 价值损失 当前价值
VERI 4,762 0 (15) (3,196) 1,552
NOAH 4,478 0 (4,423) 55
KIN 980 0 (0) (703) 277
AGI 863 0 (27) (814) 22
POLY 842 0 (17) (727) 99
HT 643 0 (366) (29) 248
GNO 636 0 0 (533) 103
QASH 617 0 (177) (300) 140
MKR 596 0 (46) (445) 105
TEL 452 0 (8) (408) 36
ITC 334 0 (7) (323) 4
ZRX 333 0 (9) (155) 169
ZIP 266 0 0 (226) 41
BLZ 256 0 (32) (207) 17
GTO 241 0 (67) (157) 17
BNB 219 110 0 118 447
BTO 198 0 (28) (165) 5
ICX 160 0 (79) (67) 14
ETHOS 153 0 (15) (123) 16
TNT 152 0 (10) (133) 9
CENNZ 143 0 (6) (121) 15
AST 141 0 (24) (104) 13
KEY 132 0 (2) (124) 6
BIX 118 2 0 (85) 35
CVC 117 0 (1) (75) 41
FSN 100 0 (6) (75) 19
OCN 100 0 (31) (64) 5
DEW 95 0 (1) (87) 7
SRN 89 0 (15) (69) 4
MDS 88 0 (8) (75) 5
EDO 83 0 (11) (58) 15
ABT 76 0 0 (71) 5
WTC 69 0 (50) 17 37
INS 68 0 0 (66) 2
PPT 65 0 (55) (5) 5
IHT 65 0 (2) (58) 5
CPT 65 0 (0) (43) 21
SPHTX 64 0 0 (60) 4
DRGN 58 0 (47) (2) 8
MCO 54 0 (89) 72 37
XYO 54 0 (6) (23) 25
RCN 54 0 0 (48) 6
DPY 47 0 (23) (22) 2
THETA 45 0 0 (30) 16
MANA 41 0 (95) 127 73
R 40 0 0 35 75
APPC 35 0 (24) (9) 2
CMT 33 0 (1) (25) 8
FUEL 32 2 0 (29) 5
CREDO 31 0 (0) (6) 25
DMT 31 0 (17) (12) 2
POWR 30 166 0 (154) 42
LRC 30 8 0 (21) 17
WPR 26 0 0 (24) 2
AMB 24 0 0 (17) 7
RNT 22 0 (1) (15) 7
ENG 22 0 0 (12) 10
COB 22 0 (10) (5) 7
GTC 20 126 0 (141) 6
REN 19 0 (3) (13) 3
DENT 19 635 0 (564) 90
UTT 19 0 (0) (11) 8
AE 13 0 (19) 6 0
DATA 11 0 (3) (6) 3
BRD 10 17 0 (21) 7
SNGLS 8 0 0 (3) 6
LEND 6 0 (7) 3 2
RLC 6 0 (5) 2 3
PLR 6 3 0 (4) 5
HVN 5 0 (5) 0 1
CVT 5 11 0 (8) 9
LYM 5 0 (4) 0 2
SAN 5 0 (7) 5 4
GNT 4 0 (12) 31 23
KICK 3 2 0 (4) 1
DGD 2 0 (5) 5 3
EDG 2 0 (29) 28 1
ENJ 2 0 (0) 1 2
RHOC 1 14 0 (13) 1
ARN 0 6 0 (6) 1
ELF 0 45 0 (40) 6
PAY 0 142 0 (132) 11
DAI 0 1 0 0 1
HPB 0 134 0 (119) 15
CRPT 0 3 0 (2) 1
HOT 0 7 0 0 7
SALT 0 95 0 (92) 3
NAS 0 71 0 (50) 21
NGC 0 12 0 (11) 1
CPC 0 12 0 (9) 3
GVT 0 3 0 (2) 2
SNM 0 14 0 (11) 2
BTM 0 9 0 (1) 8
QRL 0 7 0 (6) 2
NULS 0 71 0 (52) 19
POE 0 58 0 (54) 4
TEN 0 29 0 (15) 13
MTL 0 188 0 (177) 11
WINGS 0 18 0 (15) 3
SPANK 0 106 0 (93) 13
OMG 0 195 0 (154) 41
STORJ 0 133 0 (85) 48
BAT 0 38 0 14 52
VIBE 0 10 0 (8) 2
IOST 0 218 0 (185) 34
总计 21,513 2,723 (14,805) (4,396) 5,035
(资料来源:BitMEX 研究、TokenAnalyst、以太坊区块链、Coinmarketcap(代币价格信息))

(注:价格数据截至 2019 年 1 月,代币数据截至 2018 年 12 月)

结论和汇总数据

本分析突出了以下观点:ICO 市场缺乏标准和透明度,尤其是在将代币分配至创始团队的钱包时更是如此。团队通常能够随意铸造、销毁、购买和出售(他们自己的)代币,而分析师无法轻松跟踪正在发生的情况。我们经常会在交易所集群中看到代币,并且很难判断代币项目是否为上市而向交易所“支付”了利益,或者代币项目是否仅仅是将其数字代币资产转移到交易所进行套现。

公平地说,也许我们可以花更多时间阅读各个项目的具体文件并与相关团队交流,从而对分析进行改进。这可以形成更可靠的数据集。

但是关于 ICO,许多人经常忽略的一点是,ICO 团队通常会用两种方式从发行中盈利:

  1. 出售新发行的代币(通常是换取以太币),和
  2. 自行发行他们的自有代币。

我们 2018 年 10 月的报告侧重于前者,而本报告则侧重于后者。下面的汇总表综合了我们两份报告中的数字。

ICO 团队的盈利单位:十亿美元
ICO 流程
募集的以太坊5.4
向创始团队发行自己的代币24.2
总募集资金29.6
代币价格的变化
以太币盈利/(亏损) – 大部分已实现0.8
自有代币盈利/(亏损) – 大部分未实现(17.6)
发行后的盈利/(亏损)总额(16.8)
ICO 团队总盈利12.8

(资料来源:BitMEX 研究、TokenAnalyst、以太坊区块链、Coinmarketcap(代币价格信息))

(注:以太坊价格数据截至 2018 年 10 月,自有代币价格数据截至 2019 年 1 月)

正如我们反复说明的那样,尽管在生成数据时存在许多不准确和假设,但根据我们的算法,看来ICO 团队从 ICO 程序中获利近 130 亿美元。我们认为,这些钱来得难以置信地容易,只需要极少量的工作,而可信度或透明度都非常有限。所以,对于项目创始人筹集资金而言,ICO 已被证明是极具吸引力的方式。当然,对投资者而言,结果却没有那么吸引人。

现在,ICO 周期似乎在一定程度上正在走向消亡,而且募集资金比 2017 年末困难得多。但由于赚到和亏损的资金是如此庞大,2017 年和 2018 年初的事件不太可能被很快被遗忘。企业家对成功念念不忘(并继续努力募集资金),而投资者将痛定思痛。因此,在几年内重现这一循环的可能性比绝大多数人想象的要低得多。

欢迎转载,请注明文章来自

BitMEX (www.bitmex.com)

Tracking US$24 billion Of Tokens ICO Makers Allocated To Themselves

Abstract: This is our third major piece on ICOs. In our first piece in September 2017 we focused on the interrelationships between ICO team members. In our second piece, in October 2018, we tracked the Ethereum balances in the ICO treasury accounts. In collaboration with TokenAnalyst, this piece focuses on the treasury balances of the ICO tokens themselves, on the Ethereum network. This report is based on tokens where the team controlled holding’s were worth an astonishing US$24.2 billion on issuance (in reality liquidity was too low for this value to be realized). Today this figure has fallen to around US$5 billion, with the difference primarily being caused by a fall in the market value of the tokens, alongside US$1.5 billion of transfers away from team address clusters (possibly disposals).

(Source: BitMEX Research)

(Note: A reminder of the various interconnections between ICO team members, from our September 2017 interactive graphic)

Team controlled token holdings (Own tokens) – summary data

US$ billion
Value of ICO coins allocated to token teams 21.5
Issuance to team post ICO 2.7
Total issuance to team controlled wallets
24.2
Coins leaving the team address cluster (Perhaps sales) (1.5)
Profits/(losses) due to token price changes (12.0)
Net impact of Noah (token burn) (4.4)
Net Impact of EOS (1.2)
Current team holdings 5.0

(Source: BitMEX Research, TokenAnalyst, the Ethereum blockchain, Coinmarketcap (for token prices))
(Notes: Price data up to Jan 2019, token data up to Dec 2018, based on data for 108 tokens)

Of the US$24 billion worth of tokens ICO project teams issued to themselves, 54% of the value has been lost due to coin price reductions. The peak valuation of the team holdings of their own tokens, using the individual price peak for each coin, is over US$80 billion. This larger figure implies US$70 billion of “losses” from the peak. Although peak valuation highly is dubious due to a lack of liquidity and most of the tokens were granted to the teams essentially for nothing, therefore classifying these price movements as losses may not be appropriate. Unlike ICO investors, the teams did not have an offering price or initial investment. However, some trading activity occurred at these ridiculously high valuations, therefore we believe it’s still interesting to consider these figures, while bearing these caveats in mind.

Based on current illiquid spot prices, the ICO teams still appear to own around US$5 billion of their own tokens, money they essentially got from nothing, depending on ones view. At the same time the teams may have realized gains of US$1.5 billion by selling tokens, based on coins leaving team address clusters. Although this figure may also be an overestimate, as coins could have left the team address cluster for a variety of reasons.

Data Caveats & weaknesses in calculation methodology

  • The liquidity of many of these tokens is low and therefore the US Dollar values may be gross overestimates, this applies to both the initial allocation, current value and the value of any losses. In some cases, the value of tokens given to the team, for instance with projects such as Veritaseum or Noah, were almost comically large relative to the real trading volume in the coins. Therefore it can be considered unrealistic to value the team holdings based on the exchange price of the tokens.
  • The challenge and uncertainty involved in producing this dataset surrounds the allocation of the tokens to the team address cluster. TokenAnalyst conducted this allocation. The methodology used was imperfect and we have not dug into individual projects. The data was obtained by analysing the token smart contracts and transaction patterns on the Ethereum blockchain and applying machine learning type techniques to establish a team controlled address cluster for the team of each project. The data is therefore a probabilistic estimate and is likely to be inaccurate at the individual project level. However, the primary motivation for this report was to produce macro data about the team holdings of ICO tokens on Ethereum. Although this analysis has produced results which are far from perfect, we believe one can draw reasonable macro conclusions from the analysis.
  • As mentioned above, our analysis is based on reviewing smart contract data and transaction patterns, not documents and policies of individual projects. Therefore, it’s possible we included tokens as part of a team balance, although in reality they are held as part of another form of reserves, escrow or some other category, where it’s inaccurate to attribute the coins to the team’s own funds.
  • The data assumes the issuance date is the same date as when the first price data appeared on Coinmarketcap, this may not be a reliable assumption.

Summary data

Value of coins issued to team controlled address clusters (own tokens) – US$ million – Top 10

(Source: BitMEX Research, TokenAnalyst, the Ethereum blockchain, Coinmarketcap (for token prices))
(Notes: Token data up to Dec 2018, data based on prices at the time(s) of issuance)

Loss in value of team controlled holding (own tokens) – US$ million – Top 10

(Source: BitMEX Research, TokenAnalyst, the Ethereum blockchain, Coinmarketcap (for token prices))
(Notes: Price data up to Jan 2019, token data up to Dec 2018)

Proportional loss in value of coins in team controlled address clusters (own tokens) – Top 10

(Source: BitMEX Research, TokenAnalyst, the Ethereum blockchain, Coinmarketcap (for token prices))
(Notes: Price data up to Jan 2019, token data up to Dec 2018)

Value of coins transferred out of team controlled address clusters (Own tokens) – US$ million – Top 10

(Source: BitMEX Research, TokenAnalyst, the Ethereum blockchain, Coinmarketcap (for token prices))
(Notes: Price data up to Jan 2019, token data up to Dec 2018. Huobi and Qash are exchanges and the tokens appear to have been sent to their respective platforms. It is possible the above figures represents sales/”cashing out”, although there could be other reasons for the transfers)

Current value of coins in team controlled address clusters (Own tokens) – US$ million – Top 10

(Source: BitMEX Research, TokenAnalyst, the Ethereum blockchain, Coinmarketcap (for token prices))
(Notes: Price data up to Jan 2019, token data up to Dec 2018)

The raw data – Team holdings of own tokens – US$ million

Token Value at ICO Post ICO issuance Transfers away from team cluster Loss in value Current value
VERI 4,762 0 (15) (3,196) 1,552
NOAH 4,478 0 (4,423) 55
KIN 980 0 (0) (703) 277
AGI 863 0 (27) (814) 22
POLY 842 0 (17) (727) 99
HT 643 0 (366) (29) 248
GNO 636 0 0 (533) 103
QASH 617 0 (177) (300) 140
MKR 596 0 (46) (445) 105
TEL 452 0 (8) (408) 36
ITC 334 0 (7) (323) 4
ZRX 333 0 (9) (155) 169
ZIP 266 0 0 (226) 41
BLZ 256 0 (32) (207) 17
GTO 241 0 (67) (157) 17
BNB 219 110 0 118 447
BTO 198 0 (28) (165) 5
ICX 160 0 (79) (67) 14
ETHOS 153 0 (15) (123) 16
TNT 152 0 (10) (133) 9
CENNZ 143 0 (6) (121) 15
AST 141 0 (24) (104) 13
KEY 132 0 (2) (124) 6
BIX 118 2 0 (85) 35
CVC 117 0 (1) (75) 41
FSN 100 0 (6) (75) 19
OCN 100 0 (31) (64) 5
DEW 95 0 (1) (87) 7
SRN 89 0 (15) (69) 4
MDS 88 0 (8) (75) 5
EDO 83 0 (11) (58) 15
ABT 76 0 0 (71) 5
WTC 69 0 (50) 17 37
INS 68 0 0 (66) 2
PPT 65 0 (55) (5) 5
IHT 65 0 (2) (58) 5
CPT 65 0 (0) (43) 21
SPHTX 64 0 0 (60) 4
DRGN 58 0 (47) (2) 8
MCO 54 0 (89) 72 37
XYO 54 0 (6) (23) 25
RCN 54 0 0 (48) 6
DPY 47 0 (23) (22) 2
THETA 45 0 0 (30) 16
MANA 41 0 (95) 127 73
R 40 0 0 35 75
APPC 35 0 (24) (9) 2
CMT 33 0 (1) (25) 8
FUEL 32 2 0 (29) 5
CREDO 31 0 (0) (6) 25
DMT 31 0 (17) (12) 2
POWR 30 166 0 (154) 42
LRC 30 8 0 (21) 17
WPR 26 0 0 (24) 2
AMB 24 0 0 (17) 7
RNT 22 0 (1) (15) 7
ENG 22 0 0 (12) 10
COB 22 0 (10) (5) 7
GTC 20 126 0 (141) 6
REN 19 0 (3) (13) 3
DENT 19 635 0 (564) 90
UTT 19 0 (0) (11) 8
AE 13 0 (19) 6 0
DATA 11 0 (3) (6) 3
BRD 10 17 0 (21) 7
SNGLS 8 0 0 (3) 6
LEND 6 0 (7) 3 2
RLC 6 0 (5) 2 3
PLR 6 3 0 (4) 5
HVN 5 0 (5) 0 1
CVT 5 11 0 (8) 9
LYM 5 0 (4) 0 2
SAN 5 0 (7) 5 4
GNT 4 0 (12) 31 23
KICK 3 2 0 (4) 1
DGD 2 0 (5) 5 3
EDG 2 0 (29) 28 1
ENJ 2 0 (0) 1 2
RHOC 1 14 0 (13) 1
ARN 0 6 0 (6) 1
ELF 0 45 0 (40) 6
PAY 0 142 0 (132) 11
DAI 0 1 0 0 1
HPB 0 134 0 (119) 15
CRPT 0 3 0 (2) 1
HOT 0 7 0 0 7
SALT 0 95 0 (92) 3
NAS 0 71 0 (50) 21
NGC 0 12 0 (11) 1
CPC 0 12 0 (9) 3
GVT 0 3 0 (2) 2
SNM 0 14 0 (11) 2
BTM 0 9 0 (1) 8
QRL 0 7 0 (6) 2
NULS 0 71 0 (52) 19
POE 0 58 0 (54) 4
TEN 0 29 0 (15) 13
MTL 0 188 0 (177) 11
WINGS 0 18 0 (15) 3
SPANK 0 106 0 (93) 13
OMG 0 195 0 (154) 41
STORJ 0 133 0 (85) 48
BAT 0 38 0 14 52
VIBE 0 10 0 (8) 2
IOST 0 218 0 (185) 34
Total 21,513 2,723 (14,805) (4,396) 5,035

(Source: BitMEX Research, TokenAnalyst, the Ethereum blockchain, Coinmarketcap (for token prices))
(Notes: Price data up to Jan 2019, token data up to Dec 2018)

Conclusion & summary data

This analysis highlights the lack of standards and transparency in the ICO market, especially when it comes to the allocation of tokens to the founding team’s wallet. Teams were often able to mint, burn, buy, and sell (their own) tokens at will, without analysts being able to easily track what is occurring. We would often see tokens in exchange clusters, and it was hard to tell whether the token project “paid” the exchange to list tokens or the token project just transferred their treasury to the exchange to cash out.

To be fair, perhaps we could improve the analysis by spending more time reading the specific documentation of the individual projects and by speaking to the teams involved. This would have resulted in a more robust dataset.

But one thing about ICOs that many people often overlook, is that ICO teams often make profits in two ways from the issuance:

  1. Selling the newly issued tokens (often for Ethereum), and,
  2. Issuing themselves their own tokens.

Our October 2018 report focused on the former, while this report focuses on the latter. The summary table below combines the figures from both of our reports.

ICO team profits US$ billion
ICO process
Ethereum Raised 5.4
Own tokens issued to founding teams 24.2
Total raised 29.6
Changes in coin price
Ethereum profits/(losses) – Mostly realised 0.8
Own token profits/(losses) – Mostly unrealised (17.6)
Total profits/(losses) post issuance (16.8)
Total ICO team profits 12.8

(Source: BitMEX Research, TokenAnalyst, the Ethereum blockchain, Coinmarketcap (for token prices))
(Notes: Ethereum price data to October 2018, Own token price data to January 2019)

Although, as we have repeatedly explained, there are many inaccuracies and assumptions involved in producing the data. Based on our methodology, it appears as if ICO teams have profited by almost US$13 billion from this ICO process. In our view, this money was made incredibly easily, with very little work, accountability or transparency. Therefore, ICOs have proven to be an extremely attractive way for project founders to raise funds. The results for investors of course, have not been as attractive.

The ICO cycle now appears to be dying down to some extent and it’s much harder to raise funds than it was in late 2017. But with so much money made and lost, the events of 2017 and early 2018 are not likely to be quickly forgotten. Entrepreneurs will remember the success (and keep trying to raise money) while investors will remember the pain. A repeat of this cycle within a few years is therefore less likely than many may think.

原子掉期和去中心化交易所:不经意创造的看涨期权

摘要:在这篇文章中,我们将探讨去中心化交易所和交叉链原子掉期所面临的共同问题:即是“不经意创造的看涨期权”。不提供托管服务的去中心化交易系统经常在不经意中创建了一个美式的看涨期权,与直接用资产与另一种资产交易这种的更简单的操作有所不同。我们将就该问题如何体现在这些去中心化交易平台进行探讨,如 Bisq 和其他交叉链原子掉期结构产品等。然后我们再探讨 IDEX 在一开始如何解决该问题,但后来又要求用户在某种程度上信任平台运营商,并消除了去中心化交易所一些优势来解决问题。我们的结论是,尽管增加了复杂性,但在某些情况下,最好还是接受看涨期权作为产品,而不是尝试忽略它或与它抗争。

概述

去中心化稳定币及去中心化交易所( DEX )通常被视为加密币生态系统中的两个圣杯。然而,与去中心化稳定币类似, DEX 所面临的挑战往往被低估。在这篇文章中,我们专注探讨去中心化交易系统所面临的一个特定挑战:与其以让两个资产进行直接交易,这些系统通常无意中创建出了美式看涨期权。

不经意创造的看涨期权的理论

当在任何完全不提供托管服务的系统中交易加密币时,一方必须先支付,然后另一方再支付。从理论上讲,后执行方有着某种程度上的选择权:- 他或她可以玄子继续并完成交易,或者停止采取行动并取消交易。在先执行方采取行动后及后执行方采取行动前如果后执行方原先想要购买的代币的价格下降,或者他原先想要出售的代币的价格增加,他便有动机停止及取消交易。这意味着:

  1. 先采取行动的交易员就两种资产之间的差价发行了美式看涨期权。
  2. 这些交易所可以自然地发生,也可以作为两个单独的交易。让我们以爱丽丝用莱特币向鲍勃购买比特币作为例子。
描述 问题
原子交易

莱特币和比特币的交易同时发生,或交易同时失败 (交叉链原子掉期 )。

一方必须先行动,然后后执行方可以决定是否执行这个交易。这一决定可能受到采取行动期间两种资产中任何一种资产价格变化而影响。这为后执行方提供了选择权。
非原子交易

交易中有可能一个交易成功了而另一个交易失败了。在这种情况下,通常需要某种半监管机制,例如多重签名托管,以防止交易的其中一方作弊。( 如与 Bisq 类似的平台 )。

一方必须先行动,然后后执行方可以决定是否执行这个交易。后执行方反悔可能导致:

  • 后执行方成功从第一方窃取资金
  • 第三方托管代理还原第一笔交易

无论如何,后执行方都可选择是否继续执行交易。

如上表所示,无论交易是否为原子交易,后执行方对是否取消依然拥有选择权。

人们可能认为这是一个微不足道的问题,因为时间段很短或这种潜在期权的价值可能很低; 然而,通常情况并非如此:这种期权期限通常为 24 小时,加密币价格可能在该时间内非常不稳定。这种高波动性通常也是交易员一开始希望互相交易代币的原因。 因此,潜在期权价值可能很大且影响着交易量。

通过一系列的步骤包括收取保证金可以减轻或解决这个问题,但我们还没有见到实施这些方案的交易系统。缓解问题的另一种方法是通过交易员以某种形式暴露自己的身份,然后以声誉和用户之间的信任形成一种去中心化的网络 。如果交易其中一方因为价格波动取消交易,他们的声誉就会收到影响。然而,这种设置可能极大地增加了该交易系统的复杂性,因为想要建立一个能够抵挡 sybil 攻击的去中心化声誉系统的难度相当高。

下面我们将看看三种不同的去中心化交易系统(或半 DEX ),并解释它们分别是如何衍生出看涨期权的。

案例研究

Bisq

摘要表

种类 非原子
期权窗口 24 小时 (最多 8 天)
托管 仅对销售比特币的交易员提供多重签名托管

Bisq (前称为 Bitsquare )是一种点对点应用程序,它容许使用以法定货币买卖加密币,以及加密币之间的交易。 Bisq 本质上是一个 DEX ,因为用户通过点对点网络相互连接并直接相互交易。

Bisq 每日成交量(美元)

(资料来源: Coinmarketcap )

Bisq 平台的截图

(资料来源: BitMEX 研究)

下面我们将解释 Bisq 平台潜在交易的一些例子,并描述最终的期权问题。

示例一:在 Bisq 上以美元购入比特币

爱丽丝希望使用美元从鲍勃那里购买 1 个单位的比特币:

  • 步骤 1:鲍勃将 1 BTC 放入需要至少 2/3 同意的三方多重签名账户中。这三个签名方分别属于鲍勃,爱丽丝和第三方仲裁员。这代表了鲍勃的报价,其中也包含价格(例如每 BTC 3,800 美元)。
  • 步骤 2:爱丽丝可以通过向另一个多重签名账户支付小额押金(可退款)来接受鲍勃的报价。押金由鲍勃设定(例如 0.01 BTC )。
  • 步骤 3:爱丽丝有 24 小时进行银行电汇,向鲍勃的账户支付 3,800 美元。如果没有争议并且电汇成功,爱丽丝便会收到 1 BTC 和押金返还。 如果没有发生电汇,爱丽丝会损失押金,并且将 1 BTC 退还给鲍勃。 任何争议均由第三方仲裁员调解。

以上看似仅仅代表了爱丽丝购买比特币的行为; 然而,在考虑所背后涉及的经济激励的情况下,由于爱丽丝能够以有限的损失退出交易,我们可以看做,在步骤 2 之后,她实际上持有了下列美式看涨期权:

看涨期权内容
  • 标的资产:比特币
  • 数量:1
  • 行使价: $3,800 美元
  • 到期日: 24 小时
  • 期权费: 0.01 BTC

因此,当鲍勃在决定爱丽丝需要支付的保证金数额时,理论上他应该考虑比特币的波动性并使用期权定价公式来确保爱丽丝无法以过于便宜的价格买到该期权。根据 Bisq 目前的价格看来,似乎其中许多的期权价格都被低估了。

示例二:在 Bisq 上以门罗币购入比特币

爱丽丝希望使用门罗币( XMR )从鲍勃那里购买 1 个单位的比特币:

  • 步骤 1:鲍勃将 1 BTC 放入需要 2/3 同意的三方多重签名账户中。这三个签名方分别属于鲍勃,爱丽丝和第三方仲裁员。这代表了鲍勃的报价,其中也包含价格(例如每 BTC 卖 80 XMR )。
  • 步骤 2:爱丽丝可以通过向另一个多重签名账户支付小额押金(可退款)来接受鲍勃的报价。押金由鲍勃设定(例如 0.01 BTC )。
  • 步骤 3:爱丽丝有 24 小时的时间向鲍勃的账户支付 80 XMR 。如果没有争议并且汇款成功,爱丽丝便会收到 1 BTC 和押金返还。 如果没有发生电汇,爱丽丝会损失押金,并且将 1 BTC 退还给鲍勃。 任何争议均由第三方仲裁员调解。

同样,上面的例子可以仅仅被视为爱丽丝购买比特币的行为; 然而,在考虑背后的逻辑,我们不难看出由于爱丽丝能够以有限的损失退出交易,我们可以视为她买入并持有了以下美式的看涨期权:

看涨期权内容
  • 标的资产:比特币
  • 数量:1
  • 行使价: 80 XMR
  • 到期日: 24 小时
  • 期权费: 0.01 BTC

如果一个人试图获得以低价购买看涨期权所带来的好处,那么使用门罗币交易可能比使用美元更有利,因为门罗币价格的波动性更大,因此期权的价值更高。 由于门罗币价格比比特币更具波动性,因此将这笔交易看做爱丽丝获得看跌期权而非看涨期权可能更为经济正确,期权内容如下。

看跌期权内容
  • 标的资产:门罗币
  • 数量: 80
  • 行使价: 0.013 BTC
  • 到期日: 24 小时
  • 期权费:  0.01 BTC

作为一名交易员,如果想要利用这种结构,人们可以先较低的溢价购买这些门罗币期权,然后通过在中心化平台上买入门罗币来对冲风险。然而, Bisq 的但单笔交易金额有限,因此盈利规模也有限。

虽然这些潜在期权的特性可能使平台推广上更具挑战性,但它对 Bisq 来说也可能是个机会,它们可以将这些交易重新命名为期权,并鼓励比特币卖家设定合理的保证金价格,使其和市场上的期权的定价一样是基于价格波动的,例如它们可以考虑使用 Black-Scholes 模型来定价。

交叉链原子掉期

摘要表

种类 原子
期权窗口 24 小时 (或交易方设置的时限)
托管

我们认为 TierNolan 在 2013 年 5 月的 Bitcointalk 论坛上首次描述了交叉链原子掉期。交叉链原子掉期允许用户以原子方式将一个资产换成另一个资产,这样整个交易捆绑在一起,只能一起成功或一起失败。这样,任何一方都不会因为自己履行了交易而对方没履行交易而蒙受损失。

下图描述了链上原子掉期的过程。我们继续以爱丽丝和鲍勃之间的交易做为例子,爱丽丝以 1 个比特币向鲍勃交易 100 个莱特币。

交叉链原子掉期结构

# 执行方 描述
1 爱丽丝 爱丽丝选择一个随机的数字 X 。
2 爱丽丝 爱丽丝创建一个交易并发送 1 BTC 给鲍勃。

交易 1

交易可以在以下任何一种情况下被赎回:

  1. 得到鲍勃签名并且知道 X 这个数字,X 散列是一个必须知道的数字。
  2. 得到爱丽丝和鲍勃双方签名。
3 爱丽丝 爱丽丝创建并签署一笔交易,将交易 1 的 1 BTC 输出发送回给自己。

交易  2

交易时间锁定为 24 小时。

4 爱丽丝 爱丽丝发送交易 2 给鲍勃.
5 鲍勃 鲍勃签署交易 2 并还给爱丽丝.
6 爱丽丝 交易 1 在比特币网络上发布。
7 鲍勃 鲍勃创建一笔莱特币交易并发送 100 LTC 给爱丽丝。

交易 3

交易可以在以下任何一种情况下被赎回:

  1. 得到鲍勃签名并且知道 X 这个数字,X 散列是一个必须知道的数字。
  2. 得到爱丽丝和鲍勃双方签名。
8 鲍勃 鲍勃创建和签署交易,将交易 3 的 100 LTC 输出发送回给自己。

交易 4

交易时间锁定为 24 小时。

9 鲍勃 鲍勃将交易 4 发送给爱丽丝。
10 爱丽丝 爱丽丝签署交易 4 并还给鲍勃。
11 鲍勃 交易 3 在莱特币网络上发布。

此时,爱丽丝有个选择。如果 LTC / BTC 价格比率增加,她可以继续交易。 或者,如果 LTC / BTC 价格比率下降,爱丽丝可以停止交易。

看涨期权内容
  • 标的资产:莱特币
  • 金额: 100
  • 行使价: 0.01 BTC
  • 到期日: 24 小时
  • 期权费: 0
  • 类型:美式
12 爱丽丝 爱丽丝将交易 3 的输出发送给自己,得到 X 。爱丽丝得到 100 LTC 。
13 鲍勃 鲍勃将交易 1 的输出发送给自己,爱丽丝提供的 X 。鲍勃得到 1 BTC 。

(资料来源: BitMEX 研究)

如上表所示,虽然有人试图构建原子掉期,但与 Bisq 类似,它无意中创建了美式看涨期权。在渠道建设期间,同样的问题似乎在建立在闪电网络的多货币路由或基于闪电网络的链外原子掉期的通道时都无法避免。虽然有可能通过一系列的步骤和更多的保证金要求来解决这些问题,但会因为提高复杂性而使得具体实施变的更具挑战性。就像上面的 Bisq 一样,交叉链原子掉期的开发人员不如接受其潜在的期权特性并使其成为产品,而不是试图规避这个问题或者为了解决它而增加了系统的复杂性。

IDEX

摘要表

种类 原子
期权窗口 不适用
托管 在 IDEX 交易的双方都存在部分托管,带有日落条款

IDEX 是一个基于以太坊网络的交易平台。交易员将资金存入以太坊智能合约,提交委托,执行交易或付款等都需要交易员和 IDEX 平台的签名才可执行。

在一定时间范围后,用户可以在没有 IDEX 签名的情况下从智能合约中提取资金, 这保障了在 IDEX 突然消失的情况下用户仍然可以取回存款。提交委托,取消委托和委托匹配在 IDEX 服务器上进行脱链匹配,以实现快速无缝的用户体验。然后将指令按顺序提交给以太坊区块链,并且只有来自用户的有效签名才使指令生效。 因此, IDEX 无法在未经用户授权的情况下窃取用户资金或进行交易。

Dex.Watch 称, IDEX 是全球排名第一基于以太坊的 DEX ,市场份额约为 50% 。  IDEX 类型的平台在很多方面比上述提到的交易所更先进,因为它们可以通过在交易期间将双方的资金部分保存在托管中来解决看涨期权问题。

IDEX 每日交易量(美元)

(资料来源: Coinmarketcap )

虽然 IDEX 无法在未经授权的情况下窃取用户资金或进行交易,但指令的顺序由 IDEX 集中处理。 IDEX 可能无法及时执行委托以及偷步交易或未能及时取消委托。因此,虽然杜绝了中心化交易所常见的一些风险,但实际上他们仍然暴露一些典型的中心化交易所的风险。但是,与完全中心化的交易所相比,我们仍然认为 IDEX 类型平台是一个重大改进。 IDEX 还有其他限制,例如只能交易基于以太坊的资产,平台最终会受到以太坊网络容量的限制。

结论

在某些方面, Bisq 的模型比 IDEX 和交叉链原子掉期更加雄心勃勃。 IDEX 将自身限制在以太坊网络上存在的代币,而原子掉期只涉及某些加密币。相比之下, Bisq 试图交易美元等法定货币。虽然使用以太坊智能合约或更复杂的闪电网络结构解决看涨期权还是可能的,但一旦当涉及到法定货币时,可能就无法解决了。

当然,如果存在去中心化的美元稳定币,则原子掉期和 IDEX 类型平台便可涉及美元。 这说明了两个圣杯,去中心化的稳定币和去中心化的交易所是相关联的。在 catch-22 类型的情况下,双方是环环相扣的,只有两方都存在才能稳健地运行。

在我们看来,如果没有去中心化的稳定币,当通过去中心化系统以法定货币交易加密币时,所产生的看涨期权是不可避免的。 Bisq 可能是一个去中心化进入加密币生态系统的突破口; 然而,与其试图解决看涨期权问题,也许 Bisq 应该接受它。也许有效的进入加密币生态系统的突破口便是通过美式看涨期权。虽然这可能并不容易,但它可能是构建强大的反审查的唯一途径。

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BitMEX (www.bitmex.com)

Atomic Swaps and Distributed Exchanges: The Inadvertent Call Option

Abstract: In this piece, we look at a common problem facing both distributed exchanges and cross-chain atomic swaps: what we call the “inadvertent call option.” Non-custodial fully-distributed trading systems often inadvertently create an American-style call option, rather than the more simple desired operation of exchanging one asset for another. We review how this same issue applies to some specific distributed trading platforms like Bisq and particular cross-chain atomic swaps constructions. We then look at how IDEX solves this problem, but then requires users to trust the platform operator, to some extent, by removing some benefits of distributed exchanges. We conclude that despite the added complexity, in some circumstances it may be better to embrace the call option feature as a viable product, rather than ignoring or fighting it.

Overview

Alongside distributed stablecoins, distributed exchanges (DEXs) are often seen as one of the two holy grails within the cryptocurrency ecosystem. However, similar to distributed stablecoins, the challenges involved in DEXs are often underestimated. In this piece, we focus on one specific challenge with distributed exchange systems: instead of allowing simple exchange, these systems often inadvertently produce American-style call options.

The Theory Of The Inadvertent Call Option

When trading one cryptocurrency asset for another in any fully non-custodial system, one party must act first and the second party must follow. In theory, at some point, this second party then has optionality: – he or she can either follow through and complete the trade, or take no action and stop the trade. In the time interval between the first party taking the necessary action and the second party being required to act, if the price of the token the second actor is attempting to buy falls in value, or the price of the token he is selling increases in value, he could refuse to complete the trade. This means the following:

  1. The trader who acts first has written an American call option on the spread between the two assets.
  2. The trader who acts second has purchased an American call option on the spread between the two assets.

These exchanges can either happen atomically or as two separate transfers. Let’s consider Alice buying Bitcoin from Bob, using Litecoin.

Description Call option problem
Atomic trading

Either both the Litecoin transaction and Bitcoin transaction occur, or both transactions fail

(e.g. Cross-chain atomic swaps).

One party must act first and then the second party can decide to execute both trades or not. This decision can be influenced by price changes in any of the two assets in the intervening period. This provides the second party to act with optionality.
Non-atomic trading

It is possible for one of the transactions in the trade to succeed and the other to fail. In this case, typically a quasi-custodial mechanism such as multi-signature escrow, is required to prevent cheating for at least one side of the trade

(e.g. Bisq-type platforms).

One party must act first and then the second party can decide to execute his part of the transfer or not. Failing to execute the second transfer could result in either:

  • The second party successfully stealing funds from the first party
  • The third party escrow agent reversing the first transaction

Either way, the second party has optionality.

As the table above illustrates, whether the trading is atomic or not, the same optionality principle applies.

One may think this is an insignificant issue, as the time periods can be short or the option value may be low; however, this is typically not the case: the option period is often around 24 hours and cryptocurrency prices can be very volatile. This high volatility is typically the reason traders wish to exchange the tokens in the first place. Therefore, the option value can be significant and impact trading.

It may be possible to mitigate or solve this problem by using more steps involving more deposits, but we have not yet observed a system achieving this. The other way to mitigate the problem is via the reputation of individual traders and a distributed web of trust-based systems, with traders revealing a form of identity. Traders can then lose reputation if they cancel trades based on price volatility. However, this may greatly increase the complexity of the systems, as functioning sybil attack resistant distributed reputation systems are also challenging to construct.

Below we examine three differently constructed distributed exchange type systems (or quasi DEXs) and explain how the call option problem arises.

Case Studies

Bisq

Summary table

Type Non-atomic
Optionality period 24 hours (up to 8 days)
Escrow Multisignature escrow for the trader selling Bitcoin only

Bisq (previously known as Bitsquare) is a peer-to-peer application, which enables one to buy and sell cryptocurrency with fiat money, as well as trade between crypto-tokens. Bisq is essentially a DEX, as traders connect to each other over a peer-to-peer network and transact with each other directly.

Bisq Daily Trading Volume (USD)

(Source: Coinmarketcap)

Screenshot from the Bisq platform

(Source: BitMEX Research)

Below we explain some examples of potential trading activity on Bisq and describe the resulting options.

Example 1: Acquiring Bitcoin with USD on Bisq

Alice wishes to purchase 1 BTC from Bob, using U.S. dollars:

  • Step 1: Bob places 1 BTC in a 2 of 3 multisignature account. The three signatures belong to Bob, Alice, and a third-party arbitrator. This is Bob’s offer, which includes a price (e.g. US$3,800 per BTC).
  • Step 2 – Alice can accept Bob’s offer by paying a small refundable deposit into another multisignature account. The fee is set by Bob (e.g. 0.01 BTC).
  • Step 3 – Alice has 24 hours to conduct a bank wire transfer, paying US$3,800 into Bob’s account. If there is no dispute and the wire transfer occurs, Alice receives the 1 BTC and her deposit back. If no wire transfer occurs, Alice loses the deposit and the 1 BTC is returned back to Bob. Any dispute is mediated by the third-party arbitrator.

The above represents Alice buying Bitcoin; however, when considering the economic incentives involved, since Alice can back out of the trade with limited consequences, one could consider that, after step 2, she has acquired the following American-style call option:

Call option details
  • Underlying: Bitcoin
  • Quantity: 1
  • Strike: $3,800
  • Time to expiry: 24 hours
  • Premium: 0.01 BTC

Therefore, when Bob determines the value of the deposit Alice is required to pay, in theory he should consider Bitcoin’s volatility and use options pricing systems to ensure Alice is unable to acquire the option for a cheap price. Based on the prices currently available on Bisq, it appears many of these options are undervalued.

Example 2: Acquiring Bitcoin with Monero on Bisq

Alice wishes to purchase 1 BTC from Bob, using Monero (XMR):

  • Step 1 – Bob places 1 BTC in a 2 of 3 multisignature account. The three signatures belong to Bob, Alice, and an arbitrator. This is Bob’s offer, which includes a price (e.g. 80 XMR per BTC).
  • Step 2 – Alice  can accept Bob’s offer by paying a small refundable deposit and a fee set by Bob (e.g. 0.01 BTC).
  • Step 3 – Alice has 24 hours to do an on-chain Monero transfer. If there is no dispute and the transfer occurs, Alice receives the 1 BTC and her deposit back. If no wire transfer occurs, Alice loses the deposit and the 1 BTC is returned back to Bob. Any dispute is mediated by the arbitrator.

Again, the above example may be considered as Alice buying Bitcoin; however, when considering the incentives, since Alice can back out of the trade with limited consequences, one could consider that she has acquired the following American-style call option:

Call option details
  • Underlying: Bitcoin
  • Quantity: 1
  • Strike: 80 XMR
  • Time to expiry: 24 hours
  • Premium: 0.01 BTC

If one is trying to capture the benefits of buying a call option with a low premium, this Monero trade may be more beneficial than the U.S. dollar version, since the Monero price is more volatile, and therefore the value of the option is higher. Since the Monero price is more volatile than Bitcoin, it may be more economically correct to conclude that Alice has acquired the following put option, rather than a call option.

Put option details
  • Underlying: Monero
  • Quantity: 80
  • Strike: 0.013 BTC
  • Time to expiry: 24 hours
  • Premium: 0.01 BTC

As a trader, if one wants to take advantage of this structure, one could purchase these Monero puts for a low premium and then hedge the exposure by going long Monero on a centralised platform. However, Bisq has small position limits and therefore the size of the profit-making opportunity is limited.

Although it may make marketing the platform more challenging, it might make Bisq more robust to rebrand these trades as options and encourage sellers of Bitcoin to set the deposit price such that it’s consistent with the premium payment for the equivalent option based on the price volatility of the assets involved, for example by using the Black–Scholes model.

Cross-Chain Atomic Swaps

Summary table

Type Atomic
Optionality period 24 hours (or whatever the parties set as the lock time period)
Escrow None

We believe cross-chain atomic swaps were first described by TierNolan on the Bitcointalk forum in May 2013. Cross-chain atomic swaps allow users to exchange one asset for another atomically, such that the entire process either succeeds or fails. This allows for no risk for either party losing out by only one of the two transfers completing.

The following illustration describes the on-chain atomic swap process.  It is based on a swap between Alice and Bob, with Alice exchanging 1 Bitcoin for 100 Litecoin belonging to Bob.

Cross-chain Atomic Swap Construction

# Actor Description
1 Alice Alice picks a random number X.
2 Alice Alice creates a transaction sending 1 BTC to Bob.

Transaction 1

The transaction can be redeemed when either:

  1. Bob signs it and X is known, such that the hash of X is a necessary value.
  2. Both Alice and Bob sign it.
3 Alice Alice creates and signs a transaction sending the 1 BTC output of transaction 1, back to herself.

Transaction 2

The transaction is time locked for 24 hours.

4 Alice Alice sends transaction 2 to Bob.
5 Bob Bob signs transaction 2 and returns it to Alice.
6 Alice Transaction 1 is broadcast to the Bitcoin network.

7 Bob Bob creates a Litecoin transaction sending 100 LTC to Alice.

Transaction 3

The transaction can be redeemed when either:

  1. Alice signs it and X is revealed, such that the hash of X is the necessary value.
  2. Both Alice and Bob sign it.
8 Bob Bob creates and signs a transaction, sending the 100 LTC output of transaction 3 back to himself.

Transaction 4

The transaction is time locked for 24 hours.

9 Bob Bob sends transaction 4 to Alice.
10 Alice Alice signs transaction 4 and returns it to Bob.
11 Bob Transaction 3 is broadcast to the Litecoin network.


At this point, Alice has the optionality.
If the LTC/BTC price ratio increases, she could continue the swap process. Or, if the LTC/BTC price ratio falls, Alice can end the process here.

Call option details
  • Underlying: Litecoin
  • Quantity: 100
  • Strike: 0.01 BTC
  • Time to expiry: 24 hours
  • Premium: 0
  • Type: American
12 Alice Alice spends the output of transaction 3 to herself, revealing X. Alice now has 100 LTC.

13 Bob Bob spends the output of transaction 1 to himself, using the X Alice provided above. Bob now has 1 BTC.

(Source: BitMEX Research)

As the table illustrates, although one is attempting to structure an atomic swap, similar to Bisq, it has inadvertently resulted in an American-style call option. The same issue appears to apply to either multi-currency routing via the lightning network or off-chain lightning-based cross-chain atomic swaps, during the construction of the channels. It could be possible to solve these issues with more steps and a longer series of deposits, although this added complexity may make implementation more challenging. Just as for Bisq above, it may be more appropriate for cross-chain atomic swap developers to embrace the call options and make it the product, rather than to try to brush this problem under the carpet or solve it with added complexity.

IDEX

Summary table

Type Atomic
Optionality period n/a
Escrow Partial escrow for both sides of the trade with IDEX, with a sunset clause

IDEX is an exchange platform using the Ethereum network. Traders deposit funds into an Ethereum smart contract, where the signature of both the traders and the IDEX platform is required to submit orders, execute trades, or make payments.

After a certain time horizon, users can withdraw funds from the smart contract without a signature from IDEX, which protects user deposits in the event that IDEX disappears. Order submission, order cancellation, and order matching is conducted off-chain on the IDEX servers, to allow for a fast and seamless user experience. The events are then submitted in sequence to the Ethereum blockchain and are only valid with a valid signature from the users. Therefore, IDEX is unable to steal user funds or conduct trades without user authorisation.

According to Dex.Watch, IDEX is the global number one Ethereum-based DEX, with an approximate market share of 50%. IDEX-type platforms are in many ways more advanced than the exchanges above as they can solve the call option problem by having both party’s funds partially held in escrow during the trading period.

IDEX Daily Trading Volume ( USD)

(Source: Coinmarketcap.com)

Although IDEX cannot steal user funds or conduct trades without authorisation, the order of events is determined centrally by IDEX. IDEX could fail to execute an order in a timely manner as well as front run orders or fail to execute an order cancellation in a timely manner. Therefore, while users are protected from some risks common in centralised exchanges, in practise they are still exposed to many of the risks most often talked about with respect to typical centralised exchanges. However, we still consider IDEX type platforms to potentially be a significant improvement compared to the fully centralised alternatives.  IDEX also has other limitations such as one can only trade Ethereum-based assets and the platform is eventually constrained by Ethereum network capacity.

Conclusion

In some ways Bisq’s model is more ambitious than IDEX and cross-chain atomic swaps. IDEX limits itself to tokens that exist on the Ethereum network while atomic swaps only deal with some cryptocurrencies. In contrast, Bisq attempts to handle fiat currencies such as the U.S. dollar. While solving the call option problem may be possible using Ethereum smart contracts or more complex lightning network constructions when, fiat currency is involved, it may be impossible to solve.

Of course atomic swaps and an IDEX type platform could work with US dollars if there is a working distributed U.S. dollar stablecoin. This illustrates how the two holy grails, distributed stablecoins and distributed exchanges, are interrelated. In a catch-22 type situation, each can only function robustly if the other exists.

Without a distributed stablecoin, in our view, when trading fiat currency for cryptocurrency through distributed systems, the use of call options could be inevitable. Bisq is potentially a useful distributed onramp into the cryptocurrency ecosystem; however, rather than trying to solve the call option problem, perhaps Bisq should embrace it. Maybe an effective onramp into the cryptocurrency ecosystem could be via American-style call options. While this may not be easy, it may be the only way to structure a robust highly censorship-resistant way in.