比特币现金 SV – 6 区块链裂

摘要: 2019 年 4 月 18 日,BitMEX 研究团队的比特币现金 SV 节点曾经历了 2 次区块重组。 先是一次 3 区块重组,接著是 6 区块重组。 在此简报中,我们给出了此次临时链裂的相关数据和图表。 此次链裂似乎是由太长而难以传播的大型区块,而非与共识相关的问题所导致的。 我们的分析显示,没有双重支出与此次分裂有关。

链裂图解 – 2019 年 4 月 18 日

资料来源: BitMEX 研究

注: 上图显示有两个有效的竞争链,并且在区块 578,639 处发生了非共识分裂。 我们的节点跟随左边的链直到 578,642 区块为止,然后跳到右边。 大约一个小时后,跳回到左边。 左边的链延续,而右边的链最终被抛弃。 

链裂交易数据

 
交易数
主链(6 个区块内)
754,008
分叉链
1,050,743
重叠(6 个区块内) 
753,945
最终双重支出
0

资料来源: BitMEX 研究团队 

根据我们对交易的分析,来自分叉链(右边)的所有 TXID 最终都会回到主链中,但生成交易明显是例外。 所以,我们认为没有发生于与此次意外相关的双重支付。

分裂相关区块的时间戳 – 2019 年 4 月 18 日

本地时间 区块时间戳 高度 哈希值 大小 (MB) Log2 计算
11:39:47 11:39:19 578,638 000000000000000001ccdb82b9fa923323a8d605e615047ac6c7040584eb2419 3.1 87.803278
12:04:51 12:04:37 578,639 0000000000000000090a43754c9c3ffb3627a929a97f3a7c37f3dee94e1fc98f 8.6 87.803280
12:28:01 12:20:36 578,640 00000000000000000211d3b3414c5cb3e795e3784da599bcbb17e6929f58cc09 52.2 87.803282
12:43:42 12:29:39 578,641 0000000000000000050c01ee216586175d15b683f26adcfdd9dd0be4b1742e9e 42.1 87.803285
12:59:27 12:51:40 578,642 00000000000000000a7a25cea40cb57f5fce3b492030273b6f8a52f99f4bf2a8 76.2 87.803287
13:05:18 12:32:39 578,640 000000000000000007ad01e93696a2f93a31c35ab014d6c43597fd4fd6ba9590 35.5 87.803282
13:05:18 12:33:16 578,641 0000000000000000033ed7d3b1a818d82483ade2ee8c31304888932b7729f692 0.1 87.803285
13:05:18 12:41:38 578,642 00000000000000000ae4a0d81d4c219139c22ba1a8a42d72b960d63a9e157914 1.0 87.803287
13:05:19 12:56:37 578,643 00000000000000000590821ac2eb1d3c0e4e7edab586c16d5072ec0c77a980dc 0.8 87.803289
13:19:36 13:14:22 578,644 0000000000000000001ae8668e9ab473f8862dc081f7ac65e6df9ded635d338e 128.0 87.803291
13:21:56 13:18:07 578,645 0000000000000000049efe9a6e674370461c78845b98c4d045fe9cd5cb9ea634 107.2 87.803293
14:12:54 13:15:36 578,643 0000000000000000016b62ec5523a1afe25672abd91fe67602ea69ee2a2b871f 23.8 87.803289
14:12:55 13:43:35 578,644 000000000000000003e9d9be8a7b9fc64ef1d3494d1b0f4c11845882643a6439 1.3 87.803291
14:12:55 14:01:34 578,645 0000000000000000052be8613e79b33a9959535551217d7fdacc2d0c1db1e672 0.0 87.803293
14:12:55 14:06:35 578,646 00000000000000000475ab103a92eb6cb1c3c666cd9af7b070e09b3a35a15d66 0.0 87.803296
14:27:09 14:24:37 578,647 0000000000000000062bade37849ade3e3c4dfa9289d7f5f6d203ae188e94e4f 77.0 87.803298

资料来源: BitMEX 研究团队 

如有兴趣,可参看我们在上表中披露的链裂相关区块的所有相关详情,其中包括:

  • 区块时间戳
  • 本地时间时间戳
  • 区块哈希值 
  • 区块大小
  • 每个区块累计总工作量证明(PoW)

通过上述细节,可以跟踪发生的与链裂相关的情况并创建时间线。

结论
我们提供本信息和分析的主要目的不是出于对比特币现金 SV 的兴趣,而是希望开发系统来分析和探测比特币网络上的此类事件。 我们的网站https://forkmonitor.info 目前正在开发系统,以帮助检测由区块传播不良或与共识相关的问题导致的链裂。 比特币现金 SV 的此次事件对我们来说是一次非常不错的实践。

至于比特币现金 SV,区块在重组期间的规模极为庞大。 在分叉链上,最后两个区块分别为 128MB 和 107MB。 在主链上,很多区块超过 50MB。  因此,在我们看来,大型区块可能是重组的根本原因,因为矿工无法在不同链上找到其他区块之前足够快地传播和验证这些大型区块。

至于这对比特币现金 SV 的影响,我们不作评论。我们会把这个问题留给他人。

欢迎转载,请注明文章来自

BitMEX (www.bitmex.com)

Schnorr 签名和 Taproot 软分叉提案

摘要:我们归纳和提供了近期比特币软分叉升级提案的来龙去脉,该提案包括一个新的数字签名算法(Schnorr),以及一个名为 Taproot 的添加了扩展比特币智能合约容量新功能的补充更新。升级的结构可确保它们同时提升可扩展性和隐私性。除增加了复杂程度外,该提案没有明显缺陷,其中最具争议的方面可能是缺少其他预期的功能。我们的结论是,虽然很多人会热衷于升级,并渴望看到它推出,但重要的是耐心。

(资料来源:Pexels

概述

2019 年 5 月 6 日,比特币协议开发人员 Pieter Wuille 向比特币开发者邮件群发名单发布了一个名为 “Taproot” 软分叉提案。如果该提案被接受,它可能会补充 2018 年 7 月 Pieter 发布的 Schnorr signature 软分叉升级。这些提案的好处与可扩充性(效能)和私密性有关。可扩充性和私密性现在看起来有一定的相关性而且不可分割。在去除有关业务的细节,确保减少交易处理(提升可扩充性)的同时,他们减少了披露的信息,因此可能无法与不同类型的交易区分,从而提高了私密性。

Schnorr 签名

Schnorr 签名算法由 Claus Schnorr 于 1991 年申请获得专利,并且在 2008 年到期。虽然据称 Schnorr 算法更强大,是它的变体,但数字签名算法(DSA)方案的采用更广泛,因为这一算法的专利在全球范围内免费使用。不过 Schnorr 博士本人一直认为 DSA 应该是在他的专利范围内。 

因使用广泛,所以当比特币在 2009 年推出时,DSA 的变体椭圆曲线数字签名算法(ECDSA)被用于其数字签名算法。但最初的 Schnorr 签名算法比 DSA 更简单和有效,减少了繁重的保安假设。经过比特币 10 年的使用,越发明显的是这些效率的优势会变得重要。因此,比特币转移至 Schnorr 签名算法似乎是合理的。

Schnorr 签名的主要好处是,多重签名交易在链上显示为正常的单一签名交易。使用 Schnorr 签名,多个签名者可以生成联合公钥,然后用一个签名共同签名,而不是在区块链上分别发布所有公钥和每个签名。这是一项重要的可扩展性和私密性改进。这意味着 Schnorr 签名会大量的节省空间和验证时间,随著传统多重签名交易的签名者的增加,比较优势将会越来越明显。

Schnorr 签名空间节省估计值

我们试图计算这种 Schnorr 多重签名(multisig)的集合特性可以带来的潜在比特币网络容量的增加。不过,由于涉及大量假设,下面 13.1% 的容量增加数值应视为非常近似的估计值。

基于 UTXO 计算的节省估计值

通过 UTXO 计数来估计的当前多重签名使用率 5.9%
假设 100% 采用 Schnorr 的有效网络容量增长 13.1%

(资料来源:BitMEX 研究团队计算和估值、p2sh.info)

(注:估计值忽略了 Schnorr 签名规模较小的影响,且只包括了加入公钥和签名的好处。通过使用与多重签名使用率相关的 p2sh.info 并对每个多重签名类型应用节省倍数(范围从 50% 到 85% )来估计容量增加。通过假设 UTXO 使用比例是区块链使用的典型值并对较大的多重签名交易应用较高权重来估计网络范围容量增加。未使用的 P2SH 输出根据未使用输出的比例被分配到多重签名类型。该数值应仅被视为非常近似的估计值。数据截至 2019 年 5 月 7 日)

上述估计的容量增加可以认为是很小,但应该考虑以下因素:

  • 多重签名技术的经济使用情况远比仅考虑 UTXO 计数更为普遍。大约 21.5% 的比特币存储在多重签名钱包中,远远高于 UTXO 计数采用的 5.9%。
  • 如下图所示,多重签名采用率正在快速增长。与此同时,像闪电网络这样的新系统需要采用多重签名,而 Schnorr 签名使得多重签名系统更加强大,采用率可能会增加

按 P2SH 地址类型存储的比特币——图表显示了多重签名技术的强劲增长

(资料来源:p2sh.info

因此,根据我们的基本计算,虽然根据网络的当前使用情况,即使 100% 采用 Schnorr 也只会带来 13.1% 的网络容量增长,但长期来看,潜在的空间节省和网络容量增长可能是远高于此。

默克尔抽象语法树 (MAST)

MAST 是比特币协议开发人员 Johnson Lau 博士 2016 年的一个想法。Lau 博士过去曾在 2002 年 2 月为 BitMEX 研究团队撰写题为 The art of making softforks: Protection by policy rule (软分叉的艺术:政策规则的保护)的文章。MAST 的想法是,除了时间锁定条件之外,交易还可能包含多个支出条件,例如 2 之 2 的多重签名条件。为了避免将所有这些条件和脚本放入区块链中,可以在默克尔树内部构建支出脚本,这样只有在使用它们时才需要显示它们,以及必要的默克尔分支哈希。

MAST 支出条件的图解说明

(资料来源: BitMEX 研究团队)(注:该图表试图说明假设 MAST 与 Schnorr 一起使用的交易结构。在上述结构中,如果 Bob 和 Alice 均签名,资金可以以合作方式赎回,或者在时间锁之后以不合作方式赎回。上述是为了说明打开和关闭闪电网络通道时可能需要的结构类型)

基于上述设计,可以假设只需要显示一种支出条件。例如,要花费输出,所有签名者需要做的是提供一个 Schnorr 多重签名和默克尔树右侧顶部的哈希(哈希(1和2))。因此,尽管存在默克尔树,但在大多数情况下,一切都按计划进行,只需要一个签名和 32 字节哈希。更简明地说,为了验证脚本,您需要通过显示其他分支哈希来证明这是默克尔树的一部分。

不过,这种结构的缺点是即使在正常的最佳情况下,当提供默克尔树左上角的单匙和脚本时,仍然需要用完 32 个字节的数据,向区块链公布另一个哈希(上图中的哈希(1和2))。 这一不足也会降低隐私,因为第三方总能确定是否存在更复杂的支出条件,因为默克尔树的顶部分支始终是可见的。

Taproot

据我们所知,Taproot 想法的起源来自于比特币开发者 Gregory Maxwell 于 2018 年 1 月发出的一封电子邮件。Taproot 的结构除在默克尔树的顶部外,类似于 MAST。就 Taproot 而言,在合作或正常情景中,可以选择仅公布单个公钥和单个签名,而无需公布默克尔树存在的证据。下面图表说明了 Taproot 交易结构。

Taproot 支出条件的图解说明

(资料来源: BitMEX 研究团队)

(注:该图表试图说明与上述 MAST 图表相同的支出标准)

左侧(或地址)上的调整后公钥可以从原始公钥和默克尔根哈希计算得出。在正常或合作支付的情况下,在赎回时,原始公钥不需要在链上,并且不显示默克尔树的存在,需要公布的仅是单个签名。在没有合作或异常赎回的情况下,原始公匙将与关于默克尔树的信息一同显示。

Taproot 与原始 MAST 结构相比的好处很明显,在合作的情况下,区块链或脚本本身不再需要包含额外的 32 字节哈希,从而提高效率。除此之外,交易看起来 “正常” ,只是一次拥有公钥和签名的付款,其他支出条件的存在不需要显示。这对于对外部第三方观察者来说是一个巨大的隐私好处,例如当打开闪电通道或者甚至进行合作闪电通道关闭时,交易看起来就像是常规的比特币支出。该交易可以被构建成使得仅在不合作的闪电通道关闭时,才需要显示默克尔树的存在。越多不同类型的交易看起来一样,隐私就越好,因为第三方可能不太能够确定正在发生哪种类型的交易和产生资金流。一些比特币开发者的长期目标可能是确保无论发生什么类型的交易,至少在所谓的合作情况下,所有交易看起来都一样。

对签名集合的困惑

减少区块链所需签名数量的潜在可扩展性好处巨大,因此这个概念往往会激动人心。Schnorr 签名确实能够在多重签名交易中集合签名,这对比特币来说应该是一个重大的好处。 不过,包含这一点以及其他与签名集合相关想法的存在,导致人们对潜在好处有一些不切实际的期望,至少在此升级提议方面是这样。据我们所知,对于这个特定的升级提议,唯一的集合好处是以在多重签名方案中加入签名的形式,而不是多重输入或多个交易。

签名集合想法的汇总表


包含在软分叉提议中
多重签名交易中将公钥和签名相结合——作为 Schnorr 的一部分包含在内
交易中多个输入需要联合签名
多个交易中多个输入需要联合签名(Grin币在该领域具有一些功能,可以使用Mimblewimble)

(资料来源: BitMEX 研究团队)

结论

我们认为,与此软分叉相关的好处不太可能有争议。此软分叉似乎在功能、可扩展性和隐私方面取得三赢局面。最大的争论方面可能是没有包含其他想法或为什么要这样做的争论。

话虽如此,许多人可能对这些升级的潜在好处感到兴奋,并希望尽快在网络上看到这些升级启动。不过,当谈到比特币,特别是对共识规则的变更时,极为需要耐心对待。

欢迎转载,请注明文章来自

BitMEX (www.bitmex.com)

Initial Exchange Offerings

Abstract: In this piece we present data on a relatively new phenomenon, Initial Exchange Offerings (IEOs). The ICO market is down around 97% in Q1 2019 (YoY), based on the amount of capital raised. In this relatively challenging climate to raise funds, some projects have changed the “C” in ICO to an “E”, perhaps in an attempt to assist with raising capital. At least for now, to some extent, this appears to be working, with almost $40m having been raised so far this year. However, we remain sceptical about the prospects for long term investors.

Overview

We consider an Initial Exchange Offering (IEO) as the issuance and sale of a token based on public-private key cryptography, where participation in the issuance occurs exclusively through one trading platform or exchange. This piece provides a basic overview of the largest IEOs and tracks various IEO token metrics, including investment performance.

ICO market

First we briefly look at the ICO market. As the following chart indicates, the market has dried up following a massive boom in 2017 & 2018.

Funds raised by ICOs – US$M

Source: BitMEX Research, icodata.io
Notes: Data as at 25 April 2019

As the below chart illustrates, the investment returns of the 2018 ICOs has been poor, many of the projects are down around 80% from the ICO price, if the coin even trades at all. Peak to trough, project token prices typically declined much further than this.

Top ten ICOs by funds raised in 2018 – Investment performance data

ICO Name
Funds raised – US$m
Return based on average ICO price
EOS
4,234
(4%)
Telegram
1,700
Coin not listed
Dfinity
195
Coin not listed
Bankera
150
(87%)
t0
134
Coin not listed
Basis
125
Returned capital to investors
Orbs
118
(64%)
PumaPay
117
(93%)
Jet8
33
(99%)
Unikoin Gold
32
(88%)

Source: BitMEX Research, tokendata.io
Notes: Data as at 25 April 2019

Changing a “C” into an “E” – The IEO market

Perhaps in an attempt to address some of the concerns about the poor investment returns and the lower levels of enthusiasm for ICOs, IEOs appear to have gained in popularity. Below is a list of the major IEOs and the main exchange platforms involved.

List of IEO token sales

CoinIEO DateIEO issue amount vs total coin supplyReturn vs first exchange trade priceReturn vs IEO price
US$m raised in IEO
Binance
Gifto21/12/20173.0%(90.5%)142.2%
0.4
Bread26/12/20177.9%(84.2%)164.6%
0.8
Fetch.AI02/03/20196.0%(55.0%)203.6%
4.1
BitTorrent03/02/20196.0%54.8%433.9%
7.5
Celer24/03/20196.0%(29.4%)82.0%
4.5
Matic24/04/201919.0%Ongoing
Binance Total
17.3
Huobi
TOP26/03/20197.5%10.2%357.0%
3.3
Newton16/04/20192.0%(23.0%)197.0%
4.8
Huobi Total
8.1
Bittrex
VeriBlock02/04/20193.3%(30.9%)(33.0%)
7.0
RAIDCanceled
OKEX
Blockcloud10/04/20195.0%(43.1%)599.7%
2.5
BitMax
Dos Network11/04/201914.2%(55.1%)74.2%
1.7
Kucoin
MultiVAC03/04/20196.0%(30.5%)20.0%
3.6

Source: BitMEX Research, IEO Launchpad websites, Coinmarketcap
Notes: Data as at 25 April 2019

The number of IEOs taking place has intensified in recent months, as the model is proving somewhat successful. Smaller exchange platforms are attempting to replicate the model, as the long list of IEOs below illustrates.

Other IEOs with limited data available

Coin IEO Date Platform
Coin Analyst07/07/2018Exmarkets
SID Token15/11/2018Exmarkets
Rebglo01/12/2018Coineal
Aerum01/01/2019Exmarkets
TerraGreen21/01/2019Exmarkets
Verasity01/03/2019Probit
Percival05/03/2019Coineal
Decimated06/03/2019Exmarkets
Menapay15/03/2019Exmarkets
Linix16/03/2019Probit
Levolution18/03/2019Coineal
WeGen18/03/2019Probit
Spin Protocol19/03/2019Probit
CharS20/03/2019Probit
Windhan Energy21/03/2019Exmarkets
HUNT23/03/2019Probit
KIZUNA GLOBAL TOKEN25/03/2019Coineal
PUBLISH26/03/2019Probit
ZeroBank31/03/2019Coineal
REDi03/04/2019Probit
VenusEnergy04/04/2019Exmarkets
Bit Agro05/04/2019Exmarkets
XCon06/04/2019Coineal
YellowBetter08/04/2019Bitker
Link by BlockMason09/04/2019BW
GTEX Gaming Platform12/04/2019Coineal
AlienCloud16/04/2019IDAX
Evedo16/04/2019Bitforex
PantheonX18/04/2019BW
NUVO19/04/2019Probit
Grabity19/04/2019BW
Farm2Kitchen22/04/2019Exmarkets
Cryptobuyer23/04/2019Coineal
Airsave Travel01/05/2019Exmarkets

Source: BitMEX Research, IEO Launchpad websites

With respect to all but one of the tokens, investors have earned strong positive returns based on the IEO price. However, after the tokens begin trading, the investment returns have typically been poor. This is illustrated by the below chart, which rebases the token price to the IEO issuance price.

IEO Investment performance since launch (IEOs in 2019)

Source: BitMEX Research, IEO Launchpad websites, Coinmarketcap
Notes: Data as at 25 April 2019

US$38.9m has been raised so far by IEOs in 2019 (up to 25th April). Binance has been the most prolific IEO platform by a considerable margin.

Top exchange platforms by IEO funds raised – US$m

Source: BitMEX Research, IEO Launchpad websites, Coinmarketcap
Notes: Data as at 25 April 2019

The proceeds from IEOs can be relatively small, however on average only 4.4% of the total token supply is made available in the sale. Therefore, there are opportunities for project teams to make considerable profits from selling coins they granted to themselves. The 2019 IEOs were priced at a level which implies a total market capitalisation of US$907.7m, based on the disclosed total token supply.

Top exchange platforms by IEO token market capitalisation at IEO price – US$m

Source: BitMEX Research, IEO Launchpad websites, Coinmarketcap
Notes: Data as at 25 April 2019

Conclusion

While exchanges, traders & subscribers may have done very well from IEOs thus far, we are less confident on the outlook for long term investors. However, this is simply a high level analysis – we have not looked into any of the individual projects in detail.

Disclaimer

Any views expressed on BitMEX Research reports are the personal views of the authors. BitMEX (or any affiliated entity) has not been involved in producing this report and the views contained in the report may differ from the views or opinions of BitMEX.

The information and data herein have been obtained from sources we believe to be reliable. Such information has not been verified and we make no representation or warranty as to its accuracy, completeness or correctness. Any opinions or estimates herein reflect the judgment of the authors of the report at the date of this communication and are subject to change at any time without notice. BitMEX will not be liable whatsoever for any direct or consequential loss arising from the use of this publication/communication or its contents.

If we have made any errors in relation to particular projects, we apologise and are happy to correct the data as soon as possible.

The Schnorr Signature & Taproot Softfork Proposal

Abstract: We summarise and provide context for a recent Bitcoin softfork upgrade proposal, which includes a new digital signature scheme (Schnorr), as well as a complementary upgrade called Taproot, which adds new capabilities that extend Bitcoin’s smart contracting capability. The upgrades are structured to ensure that they simultaneously improve both scalability and privacy. Other than increased complexity, there are no significant downsides to the proposal, and the most controversial aspect of it is likely to be the lack of other anticipated features. We conclude that although many will be enthusiastic about the upgrade and keen to see it rolled out, patience will be important.

(Source: Pexels)

Overview

On 6th May 2019, Bitcoin protocol developer Pieter Wuille posted a softfork upgrade proposal to the Bitcoin developer mailing list, called “Taproot”. If this proposal is accepted, it is likely to complement the Schnorr signature softfork upgrade, which Pieter posted in July 2018. The benefits of these proposals are related to both scalability (efficiency) and privacy. Scalability and privacy enhancements now appear somewhat interrelated and inseparable. Removing details about transactions, ensures both that transactions are smaller (improving scalability) and that they reveal less information and are therefore potentially indistinguishable from transactions of different types, thereby improving privacy.

Schnorr Signatures

The Schnorr signature scheme was patented in 1991 by Claus Schnorr and the patent expired in 2008. Although the Schnorr scheme is said to be stronger, a variant of it, the Digital Signature Algorithm (DSA) scheme was more widely adopted, as the patent for this scheme was made available worldwide royalty free. However, Dr Schnorr himself always maintained that DSA should be covered under his patent.

When Bitcoin was launched, in 2009, it therefore used a variant of DSA, Elliptic Curve Digital Signature Algorithm (ECDSA) for its digital signature scheme, due to its widespread adoption. However, the original Schnorr signature scheme was always more simple and efficient than DSA, with less burdensome security assumptions. After 10 years of experience of Bitcoin usage, it is becoming more apparent that these efficiency advantages could be important. Therefore it seems sensible that Bitcoin should migrate over to the Schnorr signature scheme.

The main benefit with Schnorr signatures, is that multi-signature transactions appear onchain as a normal single signature transaction. Using Schnorr signatures, multiple signers can produce a joint public key and then jointly sign with one signature, rather than publishing each public key and each signature separately on the blockchain. This is a significant scalability and privacy enhancement. This implies that Schnorr signatures result in significant space savings and savings to verification times, with the comparative benefits getting larger as the number of signatories on a traditional multi-signature transaction increase.

Schnorr signature space saving estimates

We have tried to calculate the potential Bitcoin network capacity increase this aggregation feature of Schnorr multisig can provide. However, due to the large number of assumptions involved, our 13.1% capacity increase figure below should be considered as a very approximate estimate.

Savings estimates based on UTXO count

Estimated current multi-signature usage by UTXO count
5.9%
Effective network capacity increase assuming 100% Schnorr adoption
13.1%

(Source: BitMEX Research calculations and estimates, p2sh.info)

(Notes: The estimates ignore the impact of Schnorr’s smaller signature size and only include the benefits of joining the public keys and signatures. The capacity increase was estimated by using p2sh.info related to multi-signature usage and applying a savings multiple to each multi-signature type (ranging from 50% to 85%). A network wide capacity increase was estimated by assuming the UTXO usage proportion was typical of blockchain usage and applying a higher weight to larger multi-signature transactions. Unspent P2SH outputs were allocated to multi-signature types in proportion to the spent outputs. This figure should only be considered as a very approximate estimate. Data as at 07 May 2019 )

The above estimated capacity increase can be considered as small, however one should consider the following:

  • Economic usage of multi-signature technology is far more prevalent than by merely looking at the UTXO count. Around 21.5% of all Bitcoin is stored in multi-signature wallets, a far higher figure than the 5.9% adoption by UTXO count
  • Multi-signature adoption is growing rapidly, as the below chart indicates. While at the same time new systems like the lightning network require multi-signature adoption and with Schnorr signature making multi-signature systems more powerful, adoption is likely to increase

Bitcoin stored by P2SH address type – chart shows strong growth of multi-signature technology

(Source: p2sh.info)

Therefore, although based on the current usage of the network, according to our basic calculation, even 100% Schnorr adoption only results in a 13.1% network capacity increase, in the long term the potential space savings and network capacity gains are likely to be far higher than this.

Merkelized Abstract Syntax Tree (MAST)

MAST was an idea worked on by Bitcoin protocol developer Dr Johnson Lau in 2016. Dr Lau has written for BitMEX Research in the past, in his February 2018 piece entitled The art of making softforks: Protection by policy rule. The MAST idea is that transactions can contain multiple spending conditions, for example a 2 of 2 multi-signature condition, in addition to a time lock condition. In order to avoid putting all these conditions and scripts into the blockchain, the spending scripts can be structured inside a Merkle tree, such that they only need to be revealed if they are used, along with the necessary Merkle branch hashes.

Graphical illustration of MAST spending conditions

(Source: BitMEX Research)
(Notes: The diagram is trying to illustrate a transaction structure assuming MAST was used in conjunction with Schnorr. In the above construction funds can be redeemed the cooperative way if both Bob and Alice sign, or in an uncooperative way after a timelock. The above is supposed to illustrate the type of structure which could be required when opening and closing lightning network channels)

Based on the above design, it can be assumed that only one spending condition will need to be revealed. For example, to spend the output, all the signers need to do is provide one Schnorr multi-signature and the hash at the top of the right hand side of the Merkle tree (Hash (1 & 2)). Therefore despite the existence of a Merkle tree, in the majority of cases, where everything goes as planned, only a single signature and 32-byte hash is required. More concisely, in order to verify a script, you need to prove that it is part of the Merkle tree by revealing other branch hashes.

However, the disadvantage of this structure is that even in normal optimal circumstances, when the single key and script on the top left of the Merkle tree is provided, one still needs to publish another hash to the blockchain (Hash (1 & 2) in the above diagram), using up 32 bytes of data. This weakness also reduces privacy, since third parties can always determine if more complex spending conditions exist, as the top branch of the Merkle tree is always visible.

Taproot

As far as we can tell, the origins of the Taproot idea are from an email from Bitcoin developer Gregory Maxwell in January 2018. Taproot is similar in construction to MAST, except at the top of the Merkle tree. In the case of Taproot, in the cooperative or normal scenario, there is an option for only a single public key and single signature to be published, without the need to publish evidence of the existence of a Merkle tree. An illustration of the Taproot transaction structure is provided below.

Graphical illustration of Taproot spending conditions

(Source: BitMEX Research)

(Notes: The diagram attempts to illustrate the same spending criteria as the MAST diagram above)

The tweaked public key on the left (or address) can be calculated from the original public key and the Merkel root hash. In the event of a normal or cooperative payment, on redemption, the original public key is not required to be onchain and the existence of the Merkle tree is not revealed, all that needs to be published is a single signature. In the event of a lack of cooperation or abnormal redemption, the original public key is revealed along with information about the Merkle tree.

The benefits of Taproot compared to the original MAST structure are clear, in the cooperative case, one is no longer required to include an extra 32-byte hash in the blockchain or the script itself, improving efficiency. In addition to this, the transactions looks “normal”, just a payment with a public key and signature, the existence of the other spending conditions do not need to be revealed. This is a large privacy benefit, for example when opening a lightning channel or even doing a cooperative lightning channel closure, to an external third party observer, the transaction would look exactly like a regular spend of Bitcoin. The transaction could be structured such that only in an uncooperative lightning channel closure would the existence of the Merkle tree need to be revealed. The more different types of transactions look the same, the better it is for privacy, as third parties may be less able to determine which types of transactions are occurring and establish the flow of funds. A long term objective from some of the Bitcoin developers may be to ensure that, no matter what type of transaction is occurring, at least in the so-called cooperative cases, all transactions look the same.

The confusion over Signature aggregation

The potential scalability benefits of reducing the number of signatures needed on the blockchain are large and therefore the concept tends to generate a lot of excitement. Schnorr signatures do provide the capability to aggregate signatures in multi-signature transactions, which should be a significant benefit to Bitcoin. However, the inclusion of this and the existence of other signature aggregation related ideas, has lead to some unrealistic expectations about the potential benefits, at least with respect to this upgrade proposal. As far as we can tell, for this particular upgrade proposal, the only aggregation benefits are in the form of joining signatures in multi-signature schemes, not for multiple inputs or multiple transactions.

Summary table of signature aggregation ideas


Included in softfork proposal
Combined public key and signatures in multi-signature transactions – Included as part of Schnorr
Yes
Joint signature for multiple inputs in a transaction
No
Joint signature for multiple inputs in multiple transactions (Grin coin has some capabilities in this area, using Mimblewimble)
No

(Source: BitMEX Research)

Conclusion

In our view, the benefits associated with this softfork are not likely to be controversial. This softfork appears to be a win-win-win for capability, scalability and privacy. The largest area of contention is likely to be the absence of the inclusion of other ideas or arguments over why to do it this particular way.

That being said, many are likely to be excited about the potential benefits of these upgrades and keen to see these activated on the network as fast as possible. However, when it comes to Bitcoin, and in particular changes to consensus rules, the need for patience cannot be overstated.

闪电网络(第 2 部分)— 路由费经济学

摘要:BitMEX 研究团队对闪电网络路由费的市场动态以及为闪电节点运行者提供流动性的财务激励进行了研究。我们发现了通道流动性提供者的闪电路由费和投资回报之间的相互关系和平衡,这是网络面临的一个主要挑战,而不是路由问题的计算机科学方面。我们总结,如果闪电网络扩展(至少在理论上)、更大范围金融市场的环境(例如利率变化和投资者气氛)有机会影响闪电网络费的市场。但是,无论现时的经济环境如何,我们认为从长期来看,竞争将是价格的主要推动因素。进入市场的低壁垒,可能意味着平衡点将有利于用户和低手续费,而不是流动性提供者的投资回报。

(闪电击中新加坡市)(Pexels)

概述

我们首次写关于闪电网络的文章是在 2018 年 1 月,当时主要是理论性的。现在,随着闪电网络从抽象的概念转入试验,我们认为是时候再次进行审视了。本报告的主要重点是从金融和投资角度分析闪电网络,尤其是闪电网络提供者的收费和激励。  我们不会对技术的其它方面进行研究。

路由问题

闪电网络的批评者通常指出路由是个主要问题,通常被称为 “一个尚未解决的计算机科学问题”。总的来说,我们并不完全同意对路由问题的这种定性,并且不认为路由的计算机科学将会是一个主要挑战,在从不同通道中找到路径进行支付可能相对简单直接,并且跟其它 P2P 网络相似,比如比特币。

不过,我们确实认为流动性提供与支付路由的财务和经济方面之间的互动或平衡是它的一个主要挑战。闪电网络运行者需要通过路由费获得激励才能提供充分的流动性,从而让支付顺利地进行。流动性需要明确地分配至有需求的通道,而识别这些通道可能具有挑战性,尤其是有新的商家进入网络时。在确保网络对于用户来说手续费较低,以及确保手续费足够高能够对流动性提供者产生激励之间的这种平衡,可能将会是一个主要问题。正如我们在本文中将会进一步说明,此问题的重要性以及市场进行清算的费率,可能取决于经济环境。

闪电网络手续费市场动态

对于链上比特币交易,用户(或他们的钱包)确定进行支付时每笔交易的手续费,然后矿工试图通过选择每单位区块重量手续费较高的交易生成区块,以最大程度提高手续费收入。相反,闪电网络目前似乎是以另外一种方式运行,路由节点运行者设定手续费,然后用户为他们的支付选择路径,选择通道以最大程度降低手续费。对于闪电网络,最初是由供应商设定手续费,而不是用户。因此闪电网络可以提供一种优越的收费架构,因为供应商提供的是专门化服务,并且供应商之间(而不是普通用户)就费率展开竞争是更加适当的,其中优先考虑的是简单性。

在闪电网络中,有两种路由费节点运行者是必须注明,即基础费和费率。

两种闪电网络费

手续费类型 说明 惯例
基础费 每次通过通道为一笔支付提供路由收取的固定手续费 以 Satoshi 的千分之几表示。

例如,基础费 1,000 是指每笔交易需要 1 Satoshi。

费率 根据支付的价值按百分比收取手续费   以转账的 Satoshi 的百万分之几表示。

例如,费率 1,000 是指 1,000/1,000,000,即通过该通道转账价值的 0.1%。相当于万分之 10。

投资资金

为了为路由支付提供流动性以及赚取手续费收入,闪电节点运行者需要在支付通道内锁定资金(比特币)。

两种通道容量

  说明 创建
入站容量 入站流动性,是指节点的支付通道内可以用来接收入站支付的资金。

这些资金是由闪电网络中的其它参与者拥有。

如果支付通道关闭,这些资金不会退还给节点运行者。 

可通过以下两种方式之一创建入站余额:

* 当另外一个网络参与者利用该节点开放一个支付通道时

* 当节点运行者通过现有通道进行一笔支付时

出站容量 出站流动性,是指节点的支付通道内可以用来进行出站支付的资金。

这些资金是由节点运行者拥有,并且是他们投资资金的一部分。在考虑出站余额总额的同时,节点运行者可能会考虑其它投资的机会成本。

如果支付通道关闭,这些资金将会退还给节点运行者。

可通过以下三种方式之一创建出站余额:

* 当节点运行者利用其它网络节点开放一个支付通道时

* 当节点运行者通过现有通道接收一笔支付时

* 当通过节点为支付提供路由并且收到手续费时

通道入站和出站容量的图示说明

(资料来源:比特币闪电钱包
(注:橙色余额是入站容量,蓝色余额是出站容量)

闪电手续费市场的运行

要成为一个成功的路由节点比想象的要难。截至编写本文时,根据 1ml.com 的数据,目前有 7,615 个公共闪电节点。但是,在管理节点、重新平衡通道和以合适的方式设定手续费方面,其中只有几百个节点在提供流动性方面做得不错。

节点运行者可能需要:

  • 调整费率和基础费,监测调整的影响,以及针对最优收入最大化设置进行校准
  • 分析网络并且寻找有较高支付需求、连接不佳的闪电节点,例如新的商户
  • 分析手续费市场,不仅仅是针对整体网络,还有正在瞄准的高需求低容量路径
  • 持续监测和再平衡自己的通道,以确保有充足的双向流动性
  • 针对最新的通道状态实施定制的备份解决方案,以便在节点计算机崩溃时为资金提供保护

目前,没有能够实施上述功能的自动化系统。如果这一点不改变,可能需要成立专门的企业,为闪电网络提供流动性。但是,与流动性一样,克服这些技术问题面临的挑战并不一定意味着支付将变得非常困难或昂贵。这些技术挑战可能只会调整均衡市场费率。克服这些问题的难度越高,通道运行者将会获得的潜在投资回报就越高,同时解决这些问题的激励也越大。推动闪电网络成功的将会是需求,而不是节点运行者面临的挑战。

为了让闪电手续费市场有效运行,节点运行者可能需要根据竞争格局调整手续费,这可能以算法为基础或者是一个手动过程,目标是手续费收入最大化。为了模拟最终可能形成的标准做法,BitMEX 研究团队对此进行了试验,在为期三个月的时间内对我们其中一个节点的费率进行了修改,具体情况如下文所述。

费率试验

BitMEX 研究团队决定进行一次基础试验,以测试和评估手续费市场的状态,尽管闪电网络目前仍处于新生状态。我们搭建了一个闪电节点,并且定期修改费率,试图判断哪些费率将会使手续费收入最大化,正如随着闪电网络规模化节点运行者最终预期将会采取的做法那样。

我们采用下文的散布图对我们通过一个节点进行的基础非科学分析进行了说明。它似乎显示目前费率确实对闪电节点的手续费收入存在影响。当费率从 0 一直提高至大约万分之 0.1 个时,每日手续费收入呈现出快速加速的现象。一旦手续费提升至此费率上方,平均每日手续费收入呈现出逐渐下滑的趋势。因此,根据本次试验,似乎收入最大化的费率大约是万分之 0.1 ,与其它支付系统相比较,该费率无疑是非常低的。不过,当然这只是一个中继段的手续费,一笔支付可能有多个中继段。与此同时,目前闪电手续费市场几乎不存在,实际上 BitMEX 研究团队可能已经是通过改变手续费对经济收入最大化的行为进行了有意义的试验的为数不多的闪电节点之一。一旦该网络规模化并且其他参与者也尝试最大化收入,手续费市场的情况可能会很不同。因此本次操作只能视为是一次说明性试验,而不是对闪电手续费市场进行的具体揭示。

闪电节点每日手续费收入与费率对比

(资料来源:BitMEX 研究团队

(闪电手续费收入数据图——备注和说明:

* 从 2018 年 12 月 31 日至 2019 年 3 月 24 日的每日数据

* 数据来自一个闪电节点

* 整个期间的基础费为 0

* 投资回报数据不包括链上比特币交易手续费,当包括手续费影响时,除最优费率外所有组别都将显示负的投资回报

* 数据包括工作日和周末,一般来说闪电网络流量在周末要低很多

* 费率每天在 UTC 时间 21:00 左右变更。每天降低费率,在经过几天下调后跳升至费率区间顶部,然后开始下一个费率下调周期。这样操作是因为有些钱包(例如移动钱包)不是每次尝试通过节点为支付提供路径时都会查询费率,因此在上调费率时,很多支付都会失败。例如,当开放从一个移动钱包到闪电节点的一个通道,然后上调费率并且立即尝试进行支付时,该支付通常会失败,因为钱包尝试按照之前很低的手续费进行支付。我们认为,为了让闪电网络手续费市场有效运行,节点运行者可能需要定期变更手续费,因此钱包可能需要更频繁地查询费率。

 * 手动进行通道再平衡,每两周一次。每次花费时间大约30分钟

* 闪电节点运行的是 LND,软件每两周连接主机更新一次

* 大约 30% 的通道(按价值)是用 Autopilot 开放,其余 70% 是手动开放

* 投资回报的计算是采用网络每天的出站通道容量,根据每日手续费收入对投资回报进行年化处理,然后根据特定区间内某个费率的所有天数计算一个简单的平均值

* 数据只是基于一个节点以及该节点特定的通道集,其它节点运行者的体验可能会有很大不同

* 我们曾尝试用我们的公共节点进行此试验,但是手续费收入过于分散,一些网络参与者定期支付明显高于公布的费率的手续费金额,使得数据不可靠

* 遗憾的是我们两个轴都需要采用对数刻度。在费率方面,我们不确定收取什么样的费率,甚至设定哪个数量级,因此我们尝试了很大范围的手续费率,从 0.0001% 到 0.5% ,并且对数刻度比较合适。与此同时,每日手续费收入波动很大,范围从 0 Satoshis 一直到超过 3,000 Satoshis 。因此对数刻度似乎最为合适。随着网络的发展并且变得更加可靠,以及手续费市场消息的更加流通,线性刻度可能会变得更加合适。)

手续费收入和投资回报

除了每日手续费收入外,还可以考虑与运行一个闪电节点和各种费率相关的年化投资回报。此投资回报的计算,是对每日手续费收入进行年化,然后除以每日出站流动性。

试验中实现的最高年化投资回报是 2.75% ,而最高手续费组别投资回报大约是1%。对于理论上应属于相对低风险的投资论而言,这似乎是一个具有合理吸引力的回报,至少实时备份闪电通道的能力得到了实施。现有的比特币投资者可能会被这些回报吸引并且为闪电网络提供流动性,或者,美元本位的投资者也可能买入比特币,利用杠杆对冲比特币价格风险,然后尝试赚取闪电网络手续费收入。

不同手续费组别的闪电节点年化投资回报

(资料来源:BitMEX 研究团队

当然,这些投资回报可能无法激励目前闪电网络中的流动性提供者。目前的节点运行者可能是业余的,当考虑到开放和再平衡闪电通道所需要的链上手续费时,绝大部分节点运行者都在亏损。虽然这种以业余者为基础的流动性可以维持网络一段时间,但是为了满足很多人对于闪电网络的庞大规模要求,需要有潜在的投资回报吸引投资者。

闪电网络手续费和经济环境

在目前的低收益环境下 1% 的投资收益率可能看起来具有吸引力,但闪电网络最初可能很难吸引合适的商业流动性提供者。此领域的投资者一般都寻求高风险高回报的投资,为闪电流动性提供者提供的这种相对低风险低回报似乎是处于另外一个极端。因此可能需要一个新的投资者类别,即与这种情况相匹配的投资者类别。

如果闪电网络达到一个较大的规模,可能这种具有稳定低风险回报、高度流动的投资产品,对于经济环境会比较敏感。

考虑以下情景:

  1. 美联储基础利率为 1.0%。
  2. 闪电节点运行者对于他们的出站余额一般赚取 1.5% 的年化投资收益率。
  3. 由于强大的经济环境和通胀压力,美联储公开市场委员会将利率从 1% 上调至 3% 。
  4. 由于投资回报更具吸引力,闪电网络节点运行者从闪电网络撤回资金,并且买入政府债券。
  5. 由于闪电网络中的流动性减少,用户进行支付需要支付更高的手续费,并且闪电网络变得更加昂贵。

不过,如果闪电网络流动性具有足够大的规模,可以适用于上述逻辑,闪电网络就已经是一个了不起的成功。

无风险回报率

在一定程度上,如果闪电网络成熟后,投资者甚至可以将运行闪电节点的投资回报视为比特币的无风险回报率,或者至少是没有信用风险的回报率。在传统金融中,这通常是投资者持有政府债所赚取的回报率,其中政府有支付本金和票息的法定义务以及创造新资金以偿付债券持有人的手段,因此风险接近于零。理论上,经济体中的所有其它投资项目或贷款都有着比此无风险回报率更高的回报。相同的理论也适用于比特币,将闪电节点流动性提供者的回报率视为比特币生态系统内的基础利率。

在未来,如果运行节点中涉及的多数挑战都已经克服,并且存在具有竞争力的手续费设定算法,此闪电网络无风险回报率可能最终由以下方面决定:

  • 宏观金融市场环境——利率越高,可能意味着闪电网络无风险回报率就越高
  • 对闪电网络交易的需求——需求增加或者资金流动速度加快,应该会提升闪电网络无风险回报率

结论

专业对冲基金和风险资本投资者是否将会像他们在2018年中对待权益证明(PoS)系统的 “抵押即服务”(staking as a service)商业模式那样,对成为闪电网络流动性提供者拥有同样的热情,依然有待观察。虽然闪电网络流动性提供者的投资回报看起来还没有吸引力,但由于该网络正处于它的形成阶段,我们确实看到了这种商业模式中的潜在优点。

我们认为,闪电网络可以轻松达到比特币目前链上交易量的数倍规模,而不会遭遇任何经济手续费市场周期或问题,一切都只是单纯地以业余流动性提供者为基础。但是,如果该网络要达到很多闪电网络拥护者所希望的规模,就需要吸引来自追求风险调整后收益投资收益最大化的渴望收益的投资者的流动性。如果这样,遗憾的是,随着投资环境随时间推移而改变,该网络的手续费市场情况可能会发生显著变化。

但是,建立节点、提供流动性、以及通过减少同行的收入来赚取手续费收入是相对容易。运行节点的经营性通道、流动性提供的程度以及投资回报之间,将会最终在哪里达到平衡,很明显我们不得而知。但是,如果一定要做出猜测,根据闪电网络的架构和设计,我们认为该系统会更倾向用户和低手续费,而不是流动性提供者。

欢迎转载,请注明文章来自

BitMEX (www.bitmex.com)

Bitcoin Cash SV – 6 block chainsplit

Abstract: On 18th April 2019, the BitMEX Research Bitcoin Cash SV node experienced 2 block re-organisations. First a 3 block re-organisation, followed by a 6 block re-organisation. In this brief piece, we provide data and graphics related to the temporary chainsplit. The chainsplit appears to be caused by large blocks which took too long to propagate, rather than consensus related issues. Our analysis shows there were no double spends related to the split.

Chainsplit diagram – 18 April 2019

Source: BitMEX Research
Notes: The above image indicates there were two valid competing chains and a non-consensus split occurred at block 578,639. Our node followed the chain on the left until block 578,642, then it jumped over to the right. About an hour later, it jumped back over to the left hand side. The chain on the left continued, while the chain on the right was eventually abandoned.

Chainsplit transaction data

Number of transactions
Main chain (within 6 blocks)
754,008
Fork chain
1,050,743
Overlap (within 6 blocks)
753,945
Eventual double spends
0

Source: BitMEX Research

Based on our analysis of the transactions, all the TXIDs from the forked chain (on the right), eventually made it back into the main chain, with the obvious exception of the coinbase transactions. Therefore, it is our belief that no double spends occurred in relation to this incident.

Timestamps of the blocks related to the split – 18 April 2019

Local ClockBlock TimestampHeightHashSize (MB)Log2 Work
11:39:4711:39:19578,638 000000000000000001ccdb82b9fa923323a8d605e615047ac6c7040584eb24193.187.803278
12:04:5112:04:37578,639 0000000000000000090a43754c9c3ffb3627a929a97f3a7c37f3dee94e1fc98f8.687.803280
12:28:0112:20:36578,640 00000000000000000211d3b3414c5cb3e795e3784da599bcbb17e6929f58cc0952.287.803282
12:43:4212:29:39578,641 0000000000000000050c01ee216586175d15b683f26adcfdd9dd0be4b1742e9e42.187.803285
12:59:2712:51:40578,642 00000000000000000a7a25cea40cb57f5fce3b492030273b6f8a52f99f4bf2a876.287.803287
13:05:1812:32:39578,640 000000000000000007ad01e93696a2f93a31c35ab014d6c43597fd4fd6ba959035.587.803282
13:05:1812:33:16578,641 0000000000000000033ed7d3b1a818d82483ade2ee8c31304888932b7729f6920.187.803285
13:05:1812:41:38578,642 00000000000000000ae4a0d81d4c219139c22ba1a8a42d72b960d63a9e1579141.087.803287
13:05:1912:56:37578,643 00000000000000000590821ac2eb1d3c0e4e7edab586c16d5072ec0c77a980dc0.887.803289
13:19:3613:14:22578,644 0000000000000000001ae8668e9ab473f8862dc081f7ac65e6df9ded635d338e128.087.803291
13:21:5613:18:07578,645 0000000000000000049efe9a6e674370461c78845b98c4d045fe9cd5cb9ea634107.287.803293
14:12:5413:15:36578,643 0000000000000000016b62ec5523a1afe25672abd91fe67602ea69ee2a2b871f23.887.803289
14:12:5513:43:35578,644 000000000000000003e9d9be8a7b9fc64ef1d3494d1b0f4c11845882643a64391.387.803291
14:12:5514:01:34578,645 0000000000000000052be8613e79b33a9959535551217d7fdacc2d0c1db1e6720.087.803293
14:12:5514:06:35578,646 00000000000000000475ab103a92eb6cb1c3c666cd9af7b070e09b3a35a15d660.087.803296
14:27:0914:24:37578,647 0000000000000000062bade37849ade3e3c4dfa9289d7f5f6d203ae188e94e4f77.087.803298

Source: BitMEX Research

If one is interested, we have provided the above table which discloses all the relevant details of the blocks related to the chainsplit, including:

  • The block timestamps
  • The local clock timestamps
  • The block hashes
  • The block sizes
  • The total accumulated PoW up to each block

With the above details one can follow what occurred in relation to the chainsplit and create a timeline.

Conclusion
Our primary motivation for providing this information and analysis is not driven by an interest in Bitcoin Cash SV, but instead a desire to develop systems to analyse and detect these type of events on the Bitcoin network. Systems are being developed on our website, https://forkmonitor.info, to help detect chainsplits, caused either by poor block propagation or consensus related issues. This event on Bitcoin Cash SV is good practice for us.

As for Bitcoin Cash SV, the block sizes were particularly large during the period of the re-organisations. On the forked chain, the last two blocks were 128MB and 107MB respectively. On the main chain many of the blocks were over 50MB. Therefore, in our view, it is likely the large sizes of the blocks were the root cause of the re-organisations, as miners couldn’t propagate and verify these large blocks fast enough, before other blocks on different chains were found.

As for the implications this has on Bitcoin Cash SV, we have no comment. We will leave that to others.

The Lightning Network (Part 2) – Routing Fee Economics

Abstract: BitMEX Research examines the market dynamics of Lightning network routing fees and the financial incentives for Lightning node operators to provide liquidity. We identify the interrelationship and balance between Lightning routing fees and investment returns for channel liquidity providers, as a major challenge for the network, rather than the computer science aspects of the routing problem. We conclude that if the Lightning network scales, at least in theory, conditions in wider financial markets, such as changing interest rates and investor sentiment may impact the market for Lightning network fees. However, regardless of the prevailing economic conditions, we are of the view that in the long term, competition will be the key driver of prices. Low barriers to entry into the market could mean the balance favours users and low fees, rather than investment returns for liquidity providers.

(Lightning strikes the city of Singapore) (Pexels)

Please click here to download a PDF version of this report

Overview

We first wrote about the Lightning network back in January 2018, when it was mostly theoretical. Today, as the Lightning network transitions from abstract to experimental, we felt it was time to take another look. The primary focus of this report is to analyse the Lightning network from a financial and investment perspective, notably with respect to fees and the incentives for Lightning network providers.  We will not examine other aspects of the technology.

The routing problem

Critics of the Lightning network often point to routing as a major problem, typically making claims like its “an unsolved problem in computer science”. In general, we do not really agree with this characterization of the routing problem and do not see the computer science of routing to be a major challenge, finding paths between channels to make payments may be relatively straightforward and similar to other P2P networks, such as Bitcoin.

However, what we do think its a major challenge is the interaction or balance between the financial and economic aspects of liquidity provision and payment routing. Lightning network node operators need to be incentivised by routing fees to provide sufficient liquidity, such that payments can be made smoothly. Liquidity needs to be allocated specifically to the channels where there is demand and identifying these channels may be challenging, especially when new merchants enter the network. This balance between ensuring the network has low fees for users, while also ensuring fees are high enough to incentivise liquidity providers, is likely to be a significant issue. As we explain further in this article, the magnitude of this problem and the fee rates at which the market clears, may depend on economic conditions.

Lightning fee market dynamics

For onchain Bitcoin transactions, users (or their wallets) specify the fee for each transaction when making a payment and then miners attempt to produce blocks by selecting higher fee transactions per unit block weight, in order to maximise fee revenue. In contrast, Lightning currently appears to work the other way around, routing node operators set the fee and then users select a path for their payment, selecting channels in order to minimise fees. With Lightning, suppliers initially set fees rather than users. Lightning may therefore offer a superior fee architecture, as suppliers are providing a specialised service and it is more suitable that suppliers compete with each other over fee rates, rather than ordinary users, where the priority should be on simplicity.

In Lightning there are two types of routing fees node operators must specify, a base fee and a fee rate.

Two types of Lightning network fees

Fee type Description Convention
Base fee A fixed fee charged each time a payment is routed through the channelThis is expressed in thousandths of a Satoshi.

For example a base fee of 1,000 is 1 satoshi per transaction.
Fee rateA percentage fee charged on the value of the payment  This is expressed in millionths of a Satoshi transferred.

For example a fee rate of 1,000 is, 1,000/1,000,000, which is 0.1% of the value transferred through the channel. Equivalent to 10bps.

Investment capital

In order to provide liquidity for routing payments and to earn fee income, Lightning node operators need to lock up capital (Bitcoin) inside payment channels.

Two types of channel capacity

Description Creation
Inbound capacityInbound liquidity, are funds inside the node’s payment channels which can be used to receive incoming payments.

These funds are owned by other participants in the Lightning network.

If the payment channels are closed, these funds will not return to the node operator.
An inbound balance is created in one of two ways:

* When another network participant opens a payment channel with the node

* When the node operator makes a payment via an existing channel
Outbound capacityOutbound liquidity, are funds inside the node’s payment channels which can be used to make outbound payments.

These funds are owned by the node operator and part of their investment capital. The node operator may consider the opportunity costs of other investments, while considering the total outbound balance.

If the payment channels are closed, these funds will return to the node operator.
An outbound balance is created in one of three ways:

* When the node operator opens a payment channel with another network node

* When the node operator receives a payment via an existing channel

* When payments are routed through the node and fees are received

Graphical illustration of a channel’s inbound and outbound capacity

(Source: Bitcoin Lightning Wallet)
(Note: The orange balance is the inbound capacity, while the blue balance is the outbound capacity)

The operation of the Lightning fee market

Becoming a successful routing node is harder than one may think. At the time of writing, according to 1ml.com, there are 7,615 public Lightning nodes. However, it is likely that only a few hundred of these nodes are doing a good job providing liquidity, by managing the node, rebalancing channels and setting fees in an appropriate manner.

Node operators may need to:

  • Adjust both fee rates and the base fee, monitor the impact of the adjustments and calibrate for the optimal income maximising settings
  • Analyse the network and look for poorly connected Lightning nodes with high payment demand, such as a new merchant
  • Analyse the fee market, not just for the network as a whole, but the high demand low capacity routes you are targeting
  • Constantly monitor and rebalance ones’ channels, to ensure there is sufficient two way liquidity
  • Implement a custom backup solution for the latest channel states, to protect funds in the event that the node machine crashes

Currently, there are no automated systems capable of doing the above functions. If this does not change, specialist businesses may need to be setup to provide liquidity for the Lightning network. However, just as with liquidity, the challenges in overcoming these technical issues do not necessarily mean payments will become difficult or expensive. These technical challenges may simply adjust the equilibrium market fee rate. The more difficult these problems are to overcome, the higher the potential investment returns will be to channel operators and the greater the incentive will be to fix the problems. It will be demand that drives Lightning’s success, not the challenges for node operators.

In order for Lightning fee markets to work, node operators may need to adjust fees based on the competitive landscape, this could be based on algorithms or be a manual process, aimed at maximising fee income. In an attempt to emulate what may eventually become standard practise, BitMEX Research experimented with modifying the fee rate on one of our nodes over a three month period, as the below section reveals.

Fee rate experimentation

BitMEX Research decided to conduct a basic experiment to try and evaluate the state of the fee market, even in the Lightning network’s current nascent state. We set up a Lightning node and regularly changed the fee rate to attempt to determine which rates would maximise fee revenue, just as node operators may eventually be expected to do as the network scales.

Our basic non-scientific analysis from one node is illustrated in the scatter chart below. It appears to indicate that fee rates do currently have an impact on a lighting node’s fee income. The daily fee income appears to quickly accelerate as one increases the fee rate from 0 till around 0.1 bps. Once the fee is increased above this rate, average daily fee income appears to gradually decline. Therefore, based on this experiment, it appears as if the revenue maximising fee rate is around 0.1 bps, which is certainly very low when compared to other payment systems. However, of course, this is only the fee for one hop, a payment may have multiple hops. At the same time, the current Lightning fee market is barely exists, indeed BitMEX Research may be one in only a handful of Lightning nodes that has significantly experimented with economic revenue maximising behaviour by changing fees. Once the network scales and other parties try to maximise revenue, fee market conditions are likely to be very different. This exercise should therefore only be considered as an illustrative experiment, rather than anything particularly revealing about lighting fee markets.

Lightning node daily fee income versus the feerate

(Source: BitMEX Research)
(Lightning fee income data charts – notes and caveats:
* Daily data from 31st December 2018 to 24th March 2019
* Data from one Lightning node
* The base fee was 0 across the period
* The investment return data excludes onchain Bitcoin transaction fees, when including the impact of fees all but the most optimal fee rate buckets would show a negative investment return
* The data includes both weekdays and weekends, in general Lightning network traffic is significantly lower at weekends
* The fee rate was changed every day at around 21:00 UTC. The fee rate was reduced each day and then jumped up to the top of the fee rate range after several days of declines, to begin the next fee rate downwards cycle. The reason for this was that some wallets (e.g. mobile wallets) did not always query the fee rate each time it attempted to route a payment through the node, therefore when increasing the fee rate, many payments would fail. For example, when opening a channel from a mobile wallet to the Lightning node, then increasing the fee rate and immediately attempting to make a payment, the payment often failed as the wallet attempted to pay with a fee which was too low. In our view, in order to Lightning network fee markets to work, node operators may need to regularly change fees and therefore wallets may need to query fee rates more often
* Channel rebalancing occurred manually, once every two weeks. Approximately 30 minutes was spent on each occasion

* The Lightning node was running LND and the software was updated to the master every two weeks
* Approximately 30% of the channels (by value) were opened using the autopilot, the other 70% were opened manually
* The investment return was calculated by taking the outbound channel capacity of the network each day, annualising the investment return based on the daily fee income and then calculating a simple average based on all the days with a fee rate inside the particular range
* The data is based on one node only and its particular set of channels, the experience for other node operators may be very different
* We tried to use our public node for this experiment, however the fee income was too sporadic, with some network participants regularly paying well above the advertised fee rates by considerable amounts, making the data unreliable

* Unfortunately we needed to use a log scale for both axis. With respect to the fee rate we were unsure of which rates to charge, even which order of magnitude to set, therefore we tried a wide range of fees, from 0.0001% to 0.5% and a log scale was appropriate. At the same time, the daily fee income was highly volatile, ranging from 0 satoshis to over 3,000 satoshis. Therefore a log scale was deemed most appropriate. As the network develops and fee market intelligence improves, a linear scale may be more appropriate)

Fee incomes and investment returns

In addition to daily fee income, one can also consider the annualized investment return associated with running a lighting node and the various fee rates. This is calculated by annualising the daily fee income and dividing this number by the daily outbound liquidity.

The highest annualised investment investment return achieved in the experiment was 2.75%, whilst the highest fee bucket investment return was almost 1%. This seems like a reasonably attractive return for what should in theory be a relatively low risk investment, at least once the ability to backup lighting channels in real time becomes implemented. Existing Bitcoin investors could be tempted by these returns and provide liquidity to the Lightning network, or alternatively US dollar based investors could buy Bitcoin, hedge the Bitcoin price exposure using leverage and then attempt to earn Lightning network fee income.

Lightning node annualised investment return by fee bucket

(Source: BitMEX Research)

Of course, liquidity providers in the current Lightning network are not likely to be motivated by investment returns.  Current node operators are likely hobbyists, with the overwhelming majority of node operators making losses when considering the onchain fees required to open and rebalance Lightning channels. Although this hobbyist based liquidity probably can sustain the network for a while, in order to meet the ambitious scale many have for the Lightning network, investors will need to be attracted by the potential investment returns.

Lightning network fees and economic conditions

A 1% investment yield may seem attractive in the current low yield environment, however the Lightning network may initially have difficulty attracting the right commercial liquidity providers. Investors in this space are typically looking for a high risk high return investment, which appears to be the opposite end of the spectrum for the relatively low risk low return investment on offer for Lightning liquidity providers. Therefore a new type of investor, one that fits this profile, may be needed.

If the Lightning network reaches a large scale, it is possible that the highly liquid investment product, with stable low risk returns, is sensitive to economic conditions.

Consider the following scenario:

  1. The federal reserve base rate is 1.0%
  2. Lightning node operators are typically earning an annualised investment yield of 1.5% on their outbound balance
  3. Due to robust economic conditions and inflationary pressure, the federal reserve open market committee increase interest rates from 1% to 3%.
  4. Due to the more attractive investment returns, Lightning network node operators withdraw capital from the Lightning network and purchase government bonds
  5. Due to the lower levels of liquidity in the Lightning network, users are required to pay higher fees to route payments and the Lightning network becomes more expensive

However, if Lightning network liquidity is large enough for the above logic to apply, Lightning would have already been a tremendous success anyway.

The risk free rate of return

In some ways, if the Lightning network matures, one can even think of the investment returns from running a Lightning node as Bitcoin’s risk free rate of return, or at least a rate of return free from credit risk. In traditional finance this is often the rate investors earn by holding government bonds, where the government has a legal obligation to pay the principal and coupon and a means to create new money to pay the holders of the bonds, such that the risks are near zero. In theory, all other investment projects or loans in the economy should have a higher return than this risk free rate. The same could apply to Bitcoin, with Lightning node liquidity providers return rates being considered as the base rate within the Bitcoin ecosystem.

In the future, if most of the technical challenges involved in running nodes have been overcome and there are competitive fee setting algorithms, this Lightning network risk free rate could ultimately be determined by:

  • Conditions in wider financial markets – higher interest rates could mean a higher Lightning network risk free rate
  • The demand for Lighting network transactions – more demand or a higher velocity of money, should increase the Lightning network risk free rate

Conclusion

Whether specialist hedge funds and venture capital investors will have the same enthusiasm about becoming Lightning network liquidity providers, as they did for the “staking as a service” business model for proof of stake based systems in mid 2018 remains to be seen. While the investment returns for Lightning network liquidity providers do not yet look compelling, with the network in its formative stages, we do see potential merit in this business model.

In our view, the Lightning network can easily scale to many multiples of Bitcoin’s current onchain transaction volume without encountering any economic fee market cycles or issues, all based purely on hobbyist liquidity providers. However, if the network is to reach the scale many Lightning advocates hope, it will need to attract liquidity from yield hungry investors seeking to maximise risk adjusted investment returns. Should that occur, unfortunately the network may experience significant changes in fee market conditions as the investment climate changes over time.

However, it is relatively easy to set up a node, provide liquidity and try to earn fee income by undercutting your peers. Where the balance is ultimately struck between the operational channels of running nodes, the extent of liquidity provision and the investment returns, we obviously do not know. However, if we are forced to guess, based on the architecture and design of the Lightning network, we would say the system is somewhat rigged towards users and low fees, rather than liquidity providers.

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 研究、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 研究、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 研究、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 研究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 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。)

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 (www.bitmex.com)

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?)

Please click here to download a PDF version of this report

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.