Blockchain Papers

Dirk G. Baur, Lai T. Hoang

No abstract is available for this record.

Panayiotis Theodossiou, Polina Ellina, Christos S. Savva

No abstract is available for this record.

Minhao Leong, Simon Kwok

No abstract is available for this record.

Piotr Fiszeder, Marta Małecka

Research background: The Russian invasion on Ukraine of February 24, 2022 sharply raised the volatility in commodity and financial markets. This had the adverse effect on the accuracy of volatility forecasts. The scale of negative effects of war was, however, market-specific and some markets exhibited a strong tendency to return to usual levels in a short time. Purpose of the article: We study the volatility shocks caused by the war. Our focus is on the markets highly exposed to the effects of this conflict: the stock, currency, cryptocurrency, gold, wheat and crude oil markets. We evaluate the forecasting accuracy of volatility models during the first stage of the war and compare the scale of forecast deterioration among the examined markets. Our long-term purpose is to analyze the methods that have the potential to mitigate the effect of forecast deterioration under such circumstances. We concentrate on the methods designed to deal with outliers and periods of extreme volatility, but, so far, have not been investigated empirically under the conditions of war. Methods: We use the robust methods of estimation and a modified Range-GARCH model which is based on opening, low, high and closing prices. We compare them with the standard maximum likelihood method of the classic GARCH model. Moreover, we employ the MCS (Model Confidence Set) procedure to create the set of superior models. Findings & value added: Analyzing the market specificity, we identify both some common patterns and substantial differences among the markets, which is the first comparison of this type relating to the ongoing conflict. In particular, we discover the individual nature of the cryptocurrency markets, where the reaction to the outbreak of the war was very limited and the accuracy of forecasts remained at the similar level before and after the beginning of the war. Our long-term contribution are the findings about suitability of methods that have the potential to handle the extreme volatility but have not been examined empirically under the conditions of war. We reveal that the Range-GARCH model compares favorably with the standard volatility models, even when the latter are evaluated in a robust way. It gives valuable implication for the future research connected with military conflicts, showing that in such period gains from using more market information outweigh the benefits of using robust estimators.

Νikolaos Kyriazis, Stephanos Papadamou, Panayiotis Tzeremes, Shaen Corbet

No abstract is available for this record.

Ivana PRISLUPČÁKOVÁ

the phenomenon of cryptocurrencies has become one of the most controversial topics in the last few years, both among the professional public in economics and finance and among ordinary people who trust and invest in them.The main goal of the work is to find out the correlation between stablecoins that failed and did not maintain the promised stability around their peg and prominent cryptocurrencies with a large market capitalization, namely Bitcoin and Ethereum.The task is to find out the connection of stablecoins to significant cryptocurrencies.With a high correlation, these stablecoins cannot be stable if they are connected to a highly volatile asset.Price movement data of selected cryptocurrencies are used with a daily resolution from freely available portals. the correlation is calculated based on the primary return indicator and the Pearson correlation coefficient.The calculations show that the returns of Qcash and nuBits cryptocurrencies are correlated with Bitcoin and ethereum, and this correlation was not confirmed for the other studied failed stablecoins.

Carol Alexander, Arben Imeraj

We analyse robust dynamic delta hedging of bitcoin options using a set of smile-implied and other smile-adjusted deltas that are either model-free, in the sense that they are the same for every scale-invariant stochastic and/or local volatility model, or they are based on simple regime-dependent parameterisations of local volatility. These deltas are popular with option market makers in traditional assets because they are very easy to implement. Previous empirical research on dynamic delta hedging is based solely on equity index options, but analysis of our unique data on hourly historical bitcoin option prices reveals that bitcoin implied volatility curves behave very differently from those of equity index options. For call and put options with a wide range of moneyness and with synthetic constant maturities of 10, 20 and 30 days, we compare the dynamic hedging performance of different smile-adjusted deltas over two one-year periods. We also examine the use of the perpetual contract rather than the standard futures as hedging instrument because the basis risk for the perpetual is very much smaller than it is for calendar futures. Results are presented as testable statistics of hedging error variance ratios. In certain periods the use of smile-implied hedge ratios can significantly out-perform the simple Black–Scholes delta hedge, especially when using the perpetual swap as hedging instrument, where efficiency gains can exceed 30% for out-of-the-money puts, and reach an average of 15% when hedging short-term out-of-the money calls during periods when the implied volatility curve slopes upwards. The advantage of using the perpetual contract is especially evident during 2021, for the longer-term contracts for which the basis is still rather large.

Carlos Trucíos, James W. Taylor

Abstract Several procedures to forecast daily risk measures in cryptocurrency markets have been recently implemented in the literature. Among them, long‐memory processes, procedures taking into account the presence of extreme observations, procedures that include more than a single regime, and quantile regression‐based models have performed substantially better than standard methods in terms of forecasting risk measures. Those procedures are revisited in this paper, and their value at risk and expected shortfall forecasting performance are evaluated using recent Bitcoin and Ethereum data that include periods of turbulence due to the COVID‐19 pandemic, the third halving of Bitcoin, and the Lexia class action. Additionally, in order to mitigate the influence of model misspecification and enhance the forecasting performance obtained by individual models, we evaluate the use of several forecast combining strategies. Our results, based on a comprehensive backtesting exercise, reveal that, for Bitcoin, there is no single procedure outperforming all other models, but for Ethereum, there is evidence showing that the GAS model is a suitable alternative for forecasting both risk measures. We found that the combining methods were not able to outperform the better of the individual models.

Andrés García-Medina, Ester Aguayo-Moreno

No abstract is available for this record.

Huaigang Long, Ender Demir, Barbara Będowska-Sójka, Adam Zaremba · 5 authors

We examine the role of geopolitical risk in the cross-sectional pricing of cryptocurrencies. We calculate cryptocurrency exposure to changes in the geopolitical risk index and document that coins with the lowest geopolitical beta outperform those with high geopolitical beta. Our findings suggest that risk-averse investors require additional compensation as motivation to hold cryptocurrencies with low and negative geopolitical betas, and they are willing to pay a premium for assets with high and positive geopolitical betas. The effect cannot be explained by known return predictors and is robust to many considerations.

Nick James

This paper uses new and recently established methodologies to study the evolutionary dynamics of the cryptocurrency market, and compares the findings with that of the equity market. We begin by applying random matrix theory and principal components analysis (PCA) to correlation matrices of both collections, highlighting clear differences in the eigenspectra exhibited. We then explore the heterogeneity of both asset classes, studying the time-varying dynamics of underlying sector behaviours, and determine the collective similarity within each collection. We then turn to a study of structural break dynamics and evolutionary power spectra, where we quantify the collective affinity in structural breaks and evolutionary behaviours of underlying sector time series. Finally, we implement two algorithms simulating `portfolio choice' dynamics to compare the effectiveness of stock selection and sector allocation in cryptocurrency portfolios. There, we highlight the importance of both endeavours and comment on noteworthy implications for cryptocurrency portfolio management.

Jonathan Blackledge, Marc Lamphiere

This paper provides a review of the Fractal Market Hypothesis (FMH) focusing on financial times series analysis. In order to put the FMH into a broader perspective, the Random Walk and Efficient Market Hypotheses are considered together with the basic principles of fractal geometry. After exploring the historical developments associated with different financial hypotheses, an overview of the basic mathematical modelling is provided. The principal goal of this paper is to consider the intrinsic scaling properties that are characteristic for each hypothesis. In regard to the FMH, it is explained why a financial time series can be taken to be characterised by a 1/t1−1/γ scaling law, where γ>0 is the Lévy index, which is able to quantify the likelihood of extreme changes in price differences occurring (or otherwise). In this context, the paper explores how the Lévy index, coupled with other metrics, such as the Lyapunov Exponent and the Volatility, can be combined to provide long-term forecasts. Using these forecasts as a quantification for risk assessment, short-term price predictions are considered using a machine learning approach to evolve a nonlinear formula that simulates price values. A short case study is presented which reports on the use of this approach to forecast Bitcoin exchange rate values.

Emre Çevik, Hande ÇALIŞKAN, Emrah İsmail Çevik

Bu çalışmanın amacı, Bitcoin ile Euro/Dolar, İngiliz Sterlini/Dolar, Kanada Doları/Dolar, Japon Yeni/Dolar ve Çin Yuanı/Dolar gibi önemli döviz kurları arasındaki dinamik ilişkiyi incelemektir. Bu bağlamda, Bitcoin ve döviz kurları arasında ortalamada ve volatilitede yayılım etkisinin varlığını incelemek için Hong (2001) tarafından önerilen ortalamada ve varyansta nedensellik testi kullanılmıştır. Ayrıca, Bitcoin ve döviz kurları arasındaki kuyruk bağımlılığının varlığını araştırmak için Hong vd. (2009) tarafından önerilen risk durumlarında nedensellik testi kullanılmıştır. 19 Ağustos 2011 ile 6 Ağustos 2021 tarihleri arasında günlük verileri kullanarak, Euro, Pound ve Kanada Dolar’ından Bitcoin’e yönelik tek yönlü ortalamada nedensellik ilişkisi tespit edilmiştir. Öte yandan, varyansta nedensellik testi sonuçları, Bitcoin ile Euro ve Pound arasında çift yönlü bir oynaklık yayılım etkisinin olduğunu göstermektedir. Ayrıca, Yuan ve Kanada Dolar'ın Bitcoin'in varyansta Granger nedeni olduğu belirlenmiştir. Risk durumlarındaki nedensellik testi sonuçları, Euro ve Pound’dan Bitcoin’e yönelik nedensellik ilişkisine dair kanıt sunmaktadır. Bununla birlikte Bitcoin’deki beklenmedik kayıplar, Yen’deki beklenmedik kayıpların Granger nedenidir. Genel olarak, ampirik sonuçlar Çin para biriminin Bitcoin ile daha az entegre olduğunu göstermektedir.

Danai Likitratcharoen, Nopadon Kronprasert, Karawan Wiwattanalamphong, Chakrin Pinmanee

Since late 2019, during one of the largest pandemics in history, COVID-19, global economic recession has continued. Therefore, investors seek an alternative investment that generates profits during this financially risky situation. Cryptocurrency, such as Bitcoin, has become a new currency tool for speculators and investors, and it is expected to be used in future exchanges. Therefore, this paper uses a Value at Risk (VaR) model to measure the risk of investment in Bitcoin. In this paper, we showed the results of the predicted daily loss of investment by using the historical simulation VaR model, the delta-normal VaR model, and the Monte Carlo simulation VaR model with the confidence levels of 99%, 95%, and 90%. This paper displayed backtesting methods to investigate the accuracy of VaR models, which consisted of the Kupiec’s POF and the Kupiec’s TUFF statistical testing results. Finally, Christoffersen’s independence test and Christoffersen’s interval forecasts evaluation showed effectiveness in the predictions for the robustness of VaR models for each confidence level.

Stephen Zhang, Ganesh Mani

Cryptoassets have experienced dramatic volatility in their prices, especially during the COVID-19 pandemic era. This pilot study explores the volatility asymmetry and correlations among three popular cryptoassets (Bitcoin, Ethereum, and Dogecoin) as well as Gold. Multiple Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models are analyzed. We find that positive shocks have a greater impact on the volatility of these financial assets than negative shocks of the same magnitude, perhaps a manifestation of the fear of missing out (FOMO) effect. Our research is one of the first to use COVID-19-period volatility of financial assets (in-sample data) to forecast their later COVID-19-period volatility (out-of-sample data). This forecast accuracy is compared to that produced by forecasts using the same out-of-sample data and a longer in-sample data. Our results indicate that generally, the larger in-sample dataset gives a higher forecast accuracy though the smaller in-sample dataset is from the same regime as the out-of-sample data. We also evaluate the correlations among the assets using the Dynamic Conditional Correlation (DCC) framework and find that there is an elevated positive correlation between Gold and Bitcoin during the past two years. The Gold-Bitcoin correlation hit its peak during the peak of the COVID-19 pandemic and then fell back to around zero in July 2021 when the pandemic crisis eased. Unsurprisingly, there is a strong positive correlation among the cryptocurrencies. Pairwise correlation among all four assets was stronger during the COVID-19 pandemic. Such continuing analysis can inform portfolio asset allocation as well as general financial policy decisions.

Fulvia Pennoni, Francesco Bartolucci, Gianfranco Forte, Ferdinando M. Ametrano

Abstract A hidden Markov model is proposed for the analysis of time‐series of daily log‐returns of the last 4 years of Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash. These log‐returns are assumed to have a multivariate Gaussian distribution conditionally on a latent Markov process having a finite number of regimes or states. The hidden regimes represent different market phases identified through distinct vectors of expected values and variance–covariance matrices of the log‐returns, so that they also differ in terms of volatility. Maximum‐likelihood estimation of the model parameters is carried out by the expectation–maximisation algorithm, and regimes are singularly predicted for every time occasion according to the maximum‐a‐posteriori rule. Results show three positive and three negative phases of the market. In the most recent period, an increasing tendency towards positive regimes is also predicted. A rather heterogeneous correlation structure is estimated, and evidence of structural medium term trend in the correlation of Bitcoin with the other cryptocurrencies is detected.

Inés Jiménez, Andrés Mora‐Valencia, Javier Perote

This paper establishes a brand-new perspective of analyzing the risk of crypto assets through a semi-nonparametric approach, discussing its theoretical advantages and testing its performance compared to parametric approaches and in terms of backtesting techniques and different risk measures: Value-at-Risk, Expected Shortfall and Median Shortfall. Our comprehensive analysis for six cryptocurrencies shows that flexible semi-nonparametric approaches outperform risk measures of most crypto assets (particularly Bitcoin) and tend to provide the most conservative risk assessment. Furthermore, we propose the Median Shortfall as a robust-to-outliers and reliable risk measure for cryptocurrencies and discuss on the choice of the appropriate probability levels according to the assumed distribution. The evidence supports that Median Shortfall at 98.31 % and 98.51 % confidence levels as accurate alternatives to Value-at-Risk at 99 % and Expected Shortfall at 97.5 %.

Stephanie Danielle Subramoney, Knowledge Chinhamu, Retius Chifurira

Risk management and prediction of market losses of cryptocurrencies are of notable value to risk managers, portfolio managers, financial market researchers and academics. One of the most common measures of an asset’s risk is Value-at-Risk (VaR). This paper evaluates and compares the performance of generalized autoregressive score (GAS) combined with heavy-tailed distributions, in estimating the VaR of two well-known cryptocurrencies’ returns, namely Bitcoin returns and Ethereum returns. In this paper, we proposed a VaR model for Bitcoin and Ethereum returns, namely the GAS model combined with the generalized lambda distribution (GLD), referred to as the GAS-GLD model. The relative performance of the GAS-GLD models was compared to the models proposed by Troster et al. (2018), in other words, GAS models combined with asymmetric Laplace distribution (ALD), the asymmetric Student’s t-distribution (AST) and the skew Student’s t-distribution (SSTD). The Kupiec likelihood ratio test was used to assess the adequacy of the proposed models. The principal findings suggest that the GAS models with heavy-tailed innovation distributions are, in fact, appropriate for modelling cryptocurrency returns, with the GAS-GLD being the most adequate for the Bitcoin returns at various VaR levels, and both GAS-SSTD, GAS-ALD and GAS-GLD models being the most appropriate for the Ethereum returns at the VaR levels used in this study.

Lee A. Smales

The cryptocurrency market has experienced stunning growth, with market value exceeding USD 1.5 trillion. We use a DCC-MGARCH model to examine the return and volatility spillovers across three distinct classes of cryptocurrencies: coins, tokens, and stablecoins. Our results demonstrate that conditional correlations are time-varying, peaking during the COVID-19 pandemic sell-off of March 2020, and that both ARCH and GARCH effects play an important role in determining conditional volatility among cryptocurrencies. We find a bi-directional relationship for returns and long-term (GARCH) spillovers between BTC and ETH, but only a unidirectional short-term (ARCH) spillover effect from BTC to ETH. We also find spillovers from BTC and ETH to USDT, but no influence running in the other direction. Our results suggest that USDT does not currently play an important role in volatility transmission across cryptocurrency markets. We also demonstrate applications of our results to hedging and optimal portfolio construction.

Kuo‐Shing Chen, Yu‐Chuan Huang

In this paper, we conduct a fast calibration in the jump-diffusion model to capture the Bitcoin price dynamics, as well as the behavior of some components affecting the price itself, such as the risk of pitfalls and its ambiguous effect on the evolution of Bitcoin’s price. In addition, in our study of the Bitcoin option pricing, we find that the inclusion of jumps in returns and volatilities are significant in the historical time series of Bitcoin prices. The benefits of incorporating these jumps flow over into option pricing, as well as adequately capture the volatility smile in option prices. To the best of our knowledge, this is the first work to analyze the phenomenon of price jump risk and to interpret Bitcoin option valuation as “exceptionally ambiguous”. Crucially, using hedging options for the Bitcoin market, we also prove some important properties: Bitcoin options follow a convex, but not strictly convex function. This property provides adequate risk assessment for convex risk measure.

Boubaker TOUIJRAT, Brahim Benaid, Hassane Bouzahir

This paper studied the mean and volatility transmission among Bitcoin as the most prominent cryptocurrency, exchange rates from developed countries/regions, and exchange rates from emerging countries/regions. Using daily returns between January 1, 2015, and December 31, 2018, and Bivariate VAR - Diagonal VECH models. The empirical results suggest there was no mean transmission between USD/EUR and USD/BTC. However, there was a unidirectional mean shock transmission link from USD/CNH, USD/MAD, and USD/IDR to USD/BTC. The results also suggested the existence of a bidirectional cross-volatility persistence link between bitcoin and all the exchange rates, except for USD/IDR and a bidirectional cross-volatility spillover link between USD/BTC and USD/CNH. A critical implication of these results is that they will be of use to investors, speculators, risk managers, and policymakers in understanding the degree of integration in terms of volatility and return among Bitcoin, currencies from developed, and currencies from emerging countries.

Keaton Manwaring

In this paper, I examine the effect of the May 18th, 2021 Chinese ban of cryptocurrency transactions on the overall volatility of the cryptocurrency market. To do this, I analyze, in both univariate and multivariate settings, range-based volatility in various event windows surrounding the event. I find clear economic and statistical change in volatility in the five days after the ban. In the ten-day period after the ban, there is a moderate economic change in volatility. In the forty-day period after the ban, there is little economic change in volatility. I conclude that the Chinese ban had a clear short-term impact on the volatility of the cryptocurrency marketplace, but the effects wore off shortly thereafter.

Ethem KILIÇ, Samet Gürsoy, Enes Burak ERGÜNEY

Bu çalışmada, Bitcoin elektrik tüketiminin, Bitcoin üretiminde önde gelen seçili ülkelerin enerji piyasaları ile arasındaki ilişki araştırılmıştır. Bu amaç doğrultusunda 22.05.2017-10.02.2021 dönemleri arasında haftalık veriler kullanılarak; Cambridge Bitcoin Elektrik Tüketim Endeksi (CBECI) ile S&P 500, MOEX ve SSE enerji endeksleri arasındaki volatilite hareketleri incelenmiştir. CCC-GARCH modeliyle kurgulanan analizlerden elde edilen bulgular CBECI endeksinin; MOEX enerji endeksi ile arasında çift yönlü volatilite ilişkisi olduğunu, S&P 500 ve SSE enerji endeksleri ile arasında tek yönlü bir volatilite ilişkisi olduğunu göstermektedir. Bulgular Bitcoin elektrik tüketiminin, Rusya ve Çin’in enerji şirketi değerlemelerini etkilediği; ABD ve Rusya’nın enerji şirketi değerlemelerinden etkilendiği sonucuna ulaşılmaktadır.

Abootaleb Shirvani, Stefan Mittnik, W. Brent Lindquist, Svetlozar T. Rachev

We propose a doubly subordinated Levy process, NDIG, to model the time series properties of the cryptocurrency bitcoin. NDIG captures the skew and fat-tailed properties of bitcoin prices and gives rise to an arbitrage free, option pricing model. In this framework we derive two bitcoin volatility measures. The first combines NDIG option pricing with the Cboe VIX model to compute an implied volatility; the second uses the volatility of the unit time increment of the NDIG model. Both are compared to a volatility based upon historical standard deviation. With appropriate linear scaling, the NDIG process perfectly captures observed, in-sample, volatility.