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Review of Quantitative Finance and Accounting

Erschienen in:

27.03.2023 | Original Research

Nowcasting bitcoin’s crash risk with order imbalance

verfasst von: Dimitrios Koutmos, Wang Chun Wei

Erschienen in: Review of Quantitative Finance and Accounting | Ausgabe 1/2023

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Abstract

The spectacular nature of bitcoin price crashes baffles market spectators and prompts routine warnings from regulators cautioning that cryptocurrencies behave in contra to the fundamental properties that traditionally define what constitutes money. Arguably most concerning to the public is, first, bitcoin’s unprecedented price volatility relative to other asset classes and, second, its seemingly detached price behavior relative to time-honored economic and market fundamentals. In an attempt to create an early warning system of bitcoin price crash risk using generalized extreme value (GEV) and logistic regression modeling, this study integrates order flow imbalance, along with several control factors which reflect blockchain activity and network value, in order to nowcast bitcoin’s price crashes. From a data analysis perspective, and despite their dissimilar distributional underpinnings, the GEV and logistic models perform comparably. When evaluating the type I and type II errors which these models yield, it is shown that their performance is comparable in terms of accuracy. In addition, it is also shown how the proportion of type I and type II errors can shift dramatically across probability cutoff tolerances. Towards the end of this study, time varying probabilities of a price crash are shown and evaluated. The sample range in this study encompasses the SARS-CoV-2 (Covid-19) time period as well as the recent scandal and collapse of the FTX cryptocurrency exchange.

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See for example Forbes' 2019 Fintech 50 list here: https://www.forbes.com/fintech/2019/. In 2010, the multinational consulting firm Accenture launched a “Fintech Innovation Lab” that is designed to bring together Fintech startups with financial institutions: https://www.accenture.com/us-en/service-fintech-innovation-lab.

On January 3, 2009, the bitcoin network was born when Satoshi Nakamoto, the mystery creator, mined the genesis block of bitcoin (the first block in the blockchain—block 0). The coinbase parameter contained the following encoded text message: “The Times 03/Jan/2009 Chancellor on brink of second bailout for banks” (see https://en.bitcoin.it/wiki/Genesis_block). This message is in reference to an article headline in The Times for that day (Duncan and Elliott 2009) and serves as a time stamp for proof that the block was created on this date. Apart from serving as a time stamp, it is arguably a manifesto decrying the instability of big banks and the social costs they impose.

See https://coinmarketcap.com.

While economists and policymakers view monetary instruments whose supply cannot be regulated by a central bank as potentially hazardous (Lo and Wang 2014), proponents of bitcoin argue that this very characteristic is what gives bitcoin its value and protects it from inflationary forces (Athey et al. 2016; Bolt and van Oordt 2016; Dwyer 2015; Pagnotta and Buraschi 2018).

Metcalfe's Law is founded on the observation that in some communication network consisting of $n$ nodes (participants), there are $n(n-1)/2$ possible pairwise connections that can be made in total. Thus, and if we make the blanket assumption that all such pairwise connections are equally valuable, the value of the whole network grows by approximately ${n}^{2}$. Derived from the same assumption that all connections are equally valuable, Reed's Law argues that with the advent of the internet, nodes can form groups in addition to connecting as pairs (Reed 2001). Given $n$ nodes, there can exist ${2}^{n}$ groups. Thus, the value of the network grows by approximately ${2}^{n}$, which is a greater estimate relative to Metcalfe's Law.

See Paul Krugman's web article: http://web.mit.edu/krugman/www/metcalfe.htm.

This report from the Library of Congress details the approach which government officials have taken to eliminate the circulation and mining of cryptocurrencies from mainland China: https://www.loc.gov/law/help/cryptocurrency/china.php.

This observation point is associated with April 11, 2013. The reasons for why bitcoin crashed during this day and April 12, 2013 are still being debated. During these days, Mt. Gox halted trading and went offline in order to perform network maintenance following distributed denial-of-service (DDoS) attacks. This stirred uncertainty among cryptocurrency traders and whereby public attention on bitcoin peaked; news outlets suggested bitcoin has reached the point where it will crash (historical news articles can be accessed via Google News) while the Bitcoin subreddit became one of the most viewed around the world). While possibly unrelated, Satoshi Nakamoto's final words to the bitcoin community in the volatile month of April 2013 were, “…I've moved on to other things…it's in good hands now…”.

The speech is publicly available online: https://www.federalreserve.gov/newsevents/speech/brainard20180515a.htm.

VaR is estimated as: $\mathrm{VaR}=W\left(\mu \Delta t-n\sigma \sqrt{\Delta t}\right)$ whereby $\mu$ is the mean return $(ret)$; $W$ is the value of the portfolio; $n$ is the number of standard deviations depending on the confidence level; $\sigma$ is the standard deviation; $\Delta t$ is the time window. MVaR integrates skewness $(S)$ and excess kurtosis $(K)$ of bitcoin returns and is estimated as: $\mathrm{MVaR}=W\left[\mu -\{{z}_{c}+\frac{1}{6}\left({z}_{c}^{2}-1\right)S+\frac{1}{24}\left({z}_{c}^{3}-3{z}_{c}\right)K-\frac{1}{36}\left(2{z}_{c}^{3}-5{z}_{c}\right){S}^{2}\}\sigma \right]$. ${z}_{c}$ is the critical value for the probability $(1-\alpha )$ and − 1.96 for a 95% probability; $S=\frac{1}{T}\sum_{t=1}^{T}{\left(\frac{{ret}_{t}-\overline{ret}}{\sigma }\right)}^{3}$; $K=\frac{1}{T}\sum_{t=1}^{T}{\left(\frac{{ret}_{t}-\overline{ret}}{\sigma }\right)}^{4}-3$. More discussion on the estimation of such VaR and Sharpe models can be found in Gregoriou and Gueyie (2003) and Signer and Favre (2002). Iqbal et al. (2020) provide an discussion of some of their alternative distributional extensions.

The Sharpe ratio is estimated as $({ret}_{t}-{r}_{f})/\upsigma$ while the modified Sharpe ratio is estimated as $({ret}_{\mathrm{t}}-{r}_{\mathrm{f}})/\mathrm{MVaR}$. The holding period return for the 1-month treasury bill is used as a proxy for the risk-free rate, ${r}_{\mathrm{f}}$. For bitcoin returns (the entire sample which includes weekends and thus requires weekend data for ${r}_{f}$), a moving average is used to fit in the missing data. Treasury return data from Professor Kenneth French's data library are used in this study; see https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.

See Chavez-Demoulin and Davison (2005) and references therein on using the GEV distribution to model extreme temperatures. From an engineering view, Toshkova et al. (2020) discuss how it can be used as a warning system to detect failures in a mechanical system.

Smith (1985) shows that an iid assumption is not a necessary requirement for modeling probabilistic outcomes of extrema with limit distributions. See Coles et al. (2001) as well as the references in Footnote (12) for discussions on modeling maxima.

See https://www.bitstamp.net/api/#order-book. Trading days with suspensions or other such trading frictions are omitted. Consequently, and in all, less than 1% of observations are omitted. One such particular example is on January 6, 2015 (and several trading days thereafter), whereby Bitstamp temporarily suspended service due to a hack.

An example of a live order book can be found here: https://www.bitstamp.net/s/webapp/examples/order_book_v2.html.

The Bitstamp exchange enables investors to trade a range of cryptocurrencies (https://coinmarketcap.com/exchanges/bitstamp/).

See blockchain.com/charts for market data, block details, mining information, and network activity, respectively.

See updated data from the World Gold Council here https://www.gold.org/about-gold/gold-supply/gold-mining/how-much-gold.

See https://www.buybitcoinworldwide.com/fee-calculator/. More description of the mining process, its importance and the role of fees can be found here: https://support.blockchain.com/hc/en-us/articles/360000939903-Transaction-fees.

See https://www.blockchain.com/charts/difficulty.

Major companies such as Microsoft, Overstock, and Newegg are beginning to accept bitcoins for some (or all) of their goods and services. A list of such companies can be found here: https://paybis.com/blog/companies-that-accept-bitcoin/.

See https://www.sec.gov/oiea/investor-alerts-and-bulletins/ia_initialexchangeofferings.

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Titel: Nowcasting bitcoin’s crash risk with order imbalance
verfasst von: Dimitrios Koutmos
Wang Chun Wei
Publikationsdatum: 27.03.2023
Verlag: Springer US
Erschienen in: Review of Quantitative Finance and Accounting / Ausgabe 1/2023
Print ISSN: 0924-865X
Elektronische ISSN: 1573-7179
DOI: https://doi.org/10.1007/s11156-023-01148-1

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