Blockchain and cryptocurrency are gradually going mainstream with new cryptocurrencies introduced every single day. The speculative nature of these digital assets expose their prices to large fluctuations. Trading these crypto-assets necessitate an adequate understanding of this emerging market as well as adequate tools to model the market risk and efficient allocation of funds. This may assist crypto investors in taking advantage of the highly volatile aspects of these assets. The portfolio consider in this study consists of six cryptocurrencies: four traditional cryptocurrencies (BTC, ETH, BNB and XRP) and two stablecoins (USDT and USDC). We examine the copula particle swarm optimization (CPSO) portfolio strategy against three other portfolio strategies, namely, the global minimum variance (GMV), the most diversified portfolio (MDP) and the minimum tail dependent (MTD). CPSO appears to be a promising strategy during extreme market conditions while GMV seem favorable during normal market conditions. Most importantly, hedge and safe-havens ability of the two stablecoins is clearly exhibited with CPSO, while their diversification property is inhibited.
H. M. Tenkam, Jules C. Mba, Sutene Mwambetania Mwambi
This paper focuses on the selection and optimisation of a cryptoasset portfolio, using the K-means clustering algorithm and GARCH C-Vine copula model combined with the differential evolution algorithm. This integrated approach allows the construction of a diversified portfolio of eight cryptocurrencies and determines an optimal allocation strategy making it possible to minimize the conditional value-at-risk of the portfolio and maximise the return. Our results show that stablecoins such as True-USD are negatively correlated to the other cryptoassets in the portfolio and could therefore be a safe haven for crypto-investors during market turmoil. Our findings are in line with previous studies exhibiting stablecoins as potential diversifiers.
Kwapie\'n, Jaros{\l}aw, W\k{a}torek, Marcin, Marija Bezbradica, Martin Crane · 6 authors
We analyse tick-by-tick data representing major cryptocurrencies traded on some different cryptocurrency trading platforms. We focus on such quantities like the inter-transaction times, the number of transactions in time unit, the traded volume, and volatility. We show that the inter-transaction times show long-range power-law autocorrelations. These lead to multifractality expressed by the right-side asymmetry of the singularity spectra $f(\alpha)$ indicating that the periods of increased market activity are characterised by richer multifractality compared to the periods of quiet market. We also show that neither the stretched exponential distribution nor the power-law-tail distribution are able to model universally the cumulative distribution functions of the quantities considered in this work. For each quantity, some data sets can be modeled by the former, some data sets by the latter, while both fail in other cases. An interesting, yet difficult to account for, observation is that parallel data sets from different trading platforms can show disparate statistical properties.
The objective of this study is to use the South African financial markets (Johannesburg Stock Exchange or JSE and USD/ZAR) as a case study to understand the volatility spillover dynamics of Bitcoin as a digital asset. Methodologically, the study applies the exponential generalized autoregressive conditional heteroskedastic (EGARCH) model, followed by a robustness check by applying the time-varying conditional correlation multivariate GARCH (VCC-MGARCH) model. The study utilizes the data set for the period 2011 to 2019, a period before the COVIDâ19 pandemic. The research outcome revealed three interesting observations. First, Bitcoin and the South African stock market are independent of each other. Second, there is a bidirectional shock transmission between Bitcoin and USD/ZAR in the mean returns only, but not variance. Lastly, results confirm the existence of a bidirectional volatility spillover in both the mean and variance between the JSE stock market and the USD/ZAR market. The study outcome should enlighten investors who may want to consider Bitcoin as a diversifier in their investment and portfolio strategies.
This paper aims to investigate and measure Bitcoin and the five largest stablecoin market volatilities by incorporating various range-based volatility estimators to the BEKK- GARCH and Copula-DCC-GARCH models. Specifically, we further measure Bitcoinsâ volatility related to five major stablecoins and examine the connectedness between Bitcoin and the stablecoins. Our empirical findings document that the connectedness between Bitcoin and stablecoin market volatility behaviors exhibits the presence of stable interconnection. This study is of particular importance since it is crucial for market participation in the ongoing crypto assets to be informed about both the volatility patterns of major cryptocurrencies and the relative volatility of Bitcoin against the stablecoin markets. Eventually, we find that there is no systematic evidence for the various parity deviations of the stablecoins that are profoundly impacted by Bitcoin volatility. Thus, Bitcoin and the largest stablecoin Tether could stabilize together. However, Bitcoin shall not be generalized to other stablecoins in terms of stability results.
This paper proposes a two-stage approach to parametric nonlinear time series modelling in discrete time with the objective of incorporating uncertainty or misspecification in the conditional mean and volatility. At the first stage, a reference or approximating time series model is specified and estimated. At the second stage, Bayesian nonlinear expectations are introduced to incorporate model uncertainty or misspecification in prediction via specifying a family of alternative models. The Bayesian nonlinear expectations for prediction are constructed from closed-form Bayesian credible intervals evaluated using conjugate priors and residuals of the estimated approximating model. Using real Bitcoin data including some periods of Covid 19, applications of the proposed method to forecasting and risk evaluation of Bitcoin are discussed via three major parametric nonlinear time series models, namely the self-exciting threshold autoregressive model, the generalized autoregressive conditional heteroscedasticity model and the stochastic volatility model. Supplementary Information: The online version contains supplementary material available at 10.1007/s00181-022-02255-z.
Mustafa Disli, Fatima Abd Rabbo, Thibault Leneeuw, Ruslan Nagayev
Binance, the largest cryptocurrency exchange by traded value, relocated from Hong Kong (origin market) to Malta (destination market). This study exploits this relocation event by examining the comovement of Binance's native token with the native tokens of other cryptocurrency exchanges in the origin and destination markets. Using multivariate regression analysis, our results show that Binance experienced a significant decline in comovement with its origin market after moving to Malta. The results are less evident for the destination market; however, an increase in comovement immediately after the relocation of Binance is notable.
Most financial signals show time dependency that, combined with noisy and extreme events, poses serious problems in the parameter estimations of statistical models. Moreover, when addressing asset pricing, portfolio selection, and investment strategies, accurate estimates of the relationship among assets are as necessary as are delicate in a time-dependent context. In this regard, fundamental tools that increasingly attract research interests are precision matrix and graphical models, which are able to obtain insights into the joint evolution of financial quantities. In this paper, we present a robust divergence estimator for a time-varying precision matrix that can manage both the extreme events and time-dependency that affect financial time series. Furthermore, we provide an algorithm to handle parameter estimations that uses the "maximization-minimization" approach. We apply the methodology to synthetic data to test its performances. Then, we consider the cryptocurrency market as a real data application, given its remarkable suitability for the proposed method because of its volatile and unregulated nature.
The Bitcoin mining process is energy intensive, which can hamper the much-desired ecological balance. Given that the persistence of high levels of energy consumption of Bitcoin could have permanent policy implications, we examine the presence of long memory in the daily data of the Bitcoin Energy Consumption Index (BECI) (BECI upper bound, BECI lower bound, and BECI average) covering the period 25 February 2017 to 25 January 2022. Employing fractionally integrated GARCH (FIGARCH) and multifractal detrended fluctuation analysis (MFDFA) models to estimate the order of fractional integrating parameter and compute the Hurst exponent, which measures long memory, this study shows that distant series observations are strongly autocorrelated and long memory exists in most cases, although mean-reversion is observed at the first difference of the data series. Such evidence for the profound presence of long memory suggests the suitability of applying permanent policies regarding the use of alternate energy for mining; otherwise, transitory policy would quickly become obsolete. We also suggest the replacement of 'proof-of-work' with 'proof-of-space' or 'proof-of-stake', although with a trade-off (possible security breach) to reduce the carbon footprint, the implementation of direct tax on mining volume, or the mandatory use of carbon credits to restrict the environmental damage.
This paper examines the dynamic conditional correlations among 10 cryptocurrencies and the possibility of hedging investment strategies among multiple cryptocurrencies over the period affected by COVID-19 from 2017 to 2022. After studying the relationship between Bitcoin, Ethereum, and the other eight cryptocurrencies, four main results were obtained in this paper: first, from the pre-COVID-19 period to the COVID-19 period, almost all of the cryptocurrenciesâ return growth rates increased, and COVID-19 had a positive effect on the returns of cryptocurrencies. Second, all of the cryptocurrenciesâ return indices had features of volatility clustering and memory persistence in the long run; from pre-COVID-19 to COVID-19, these cryptocurrenciesâ GARCH values decreased, but the correlations among the varying GARCH values increased. Third, the varying correlations between the return indices of Bitcoin, Ethereum, and the other cryptocurrencies were very strong; from pre-COVID-19 to COVID-19, the average dynamic correlations between Bitcoin and the others increased. Fourth, Tether can be used as a hedge cryptocurrency against the other cryptocurrencies as COVID-19 enhanced its hedging feature.
Rebekka Buse, Konstantin Görgen, Melanie Schienle
We study the prediction of Value at Risk (VaR) for cryptocurrencies. In contrast to classic assets, returns of cryptocurrencies are often highly volatile and characterized by large fluctuations around single events. Analyzing a comprehensive set of 105 major cryptocurrencies, we show that Generalized Random Forests (GRF) (Athey, Tibshirani & Wager, 2019) adapted to quantile prediction have superior performance over other established methods such as quantile regression, GARCH-type and CAViaR models. This advantage is especially pronounced in unstable times and for classes of highly-volatile cryptocurrencies. Furthermore, we identify important predictors during such times and show their influence on forecasting over time. Moreover, a comprehensive simulation study also indicates that the GRF methodology is at least on par with existing methods in VaR predictions for standard types of financial returns and clearly superior in the cryptocurrency setup.
Value at risk and expected shortfall are increasingly popular tail risk measures in the financial risk management field. Both academia and financial institutions are working to improve tail risk forecasts in order to meet the requirements of the Basel Capital Accord; it states that one purpose of risk management and measuring risk accuracy is, since extreme movements cannot always be avoided, financial institutions can prepare for these extreme returns by capital allocation, and putting aside the appropriate amount of capital so as to avoid default in times of extreme price or index movements. Forecast combination has drawn much attention, as a combined forecast can outperform the individual forecasts under certain conditions. We propose two methodology, one is a semiparametric combination framework that can jointly produce combined value at risk and expected shortfall forecasts, another one is a parametric regression framework named as Quantile-ES regression that can produce combined expected shortfall forecasts. The favourability of the semiparametric combination framework has been presented via an empirical study - application in cryptocurrency markets with high-frequency data where the necessity of risk management application increases as the cryptocurrency market becomes more popular and mature. Additionally, the general framework of the parametric Quantile-ES regression has been presented via a simulation study, whereas it still need to be improved in the future. The contributions of this work include but are not limited to the enabling of the combination of expected shortfall forecasts and the application of risk management procedures in the cryptocurrency market with high-frequency data.
Abstract This study investigates the dynamic mechanism of financial markets on volatility spillovers across eight major cryptocurrency returns, namely Bitcoin, Ethereum, Stellar, Ripple, Tether, Cardano, Litecoin, and Eos from November 17, 2019, to January 25, 2021. The study captures the financial behavior of investors during the COVID-19 pandemic as a result of national lockdowns and slowdown of production. Three different methods, namely, EGARCH, DCC-GARCH, and wavelet, are used to understand whether cryptocurrency markets have been exposed to extreme volatility. While GARCH family models provide information about asset returns at given time scales, wavelets capture that information across different frequencies without losing inputs from the time horizon. The overall results show that three cryptocurrency markets (i.e., Bitcoin, Ethereum, and Litecoin) are highly volatile and mutually dependent over the sample period. This result means that any kind of shock in one market leads investors to act in the same direction in the other market and thus indirectly causes volatility spillovers in those markets. The results also imply that the volatility spillover across cryptocurrency markets was more influential in the second lockdown that started at the beginning of November 2020. Finally, to calculate the financial risk, two methodsânamely, value-at-risk (VaR) and conditional value-at-risk (CVaR)âare used, along with two additional stock indices (the Shanghai Composite Index and S&P 500). Regardless of the confidence level investigated, the selected crypto assets, with the exception of the USDT were found to have substantially greater downside risk than SSE and S&P 500.
The aim of the study investigates the return and volatility spillovers and conditional correlations between Borsa Istanbul Stock Exchange 100 Index (BIST100) and Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Litecoin (LTH) using daily data for the period between August 07, 2015 and May 20, 2021 with VAR-DCC-GARCH model. We find no bidirectional return spillovers between BIST100 and cryptocurrencies. In line with the volatility spillover results of the study, it has been determined that there is a unidirectional shock transmission from BIST100 to BTC, XRP and LTH, and a unidirectional volatility spillover from BIST100 to BTC and ETH. Also, in the study, it has been determined that the dynamic conditional correlations between BIST100 and four cryptocurrencies have a highly variable over time and their average is very close to zero. However, in possible panic periods, the situation is reversed
In equity trading, internalization is the predominant execution method for uninformed order flow, allowing retail brokers to realize cost savings and thereby offer price improvements to customers. In cryptocurrency trading, there are doubts as to whether informed and uninformed traders can be distinguished in the same way, leading brokers to seek cost savings through internal order matching instead. Using the historical order flow of the German cryptocurrency broker BISON, we present a prediction-based approach to internal order matching: Upon receiving a customer order, our model forecasts whether future order flow will be sufficient to neutralize the order before the settlement date. With a prediction accuracy of 85%, it enables brokers to match three-quarters of order volume internally, which is three times as much as a traditional static approach, and realize meaningful cost savings, even after accounting for common minimum price improvements.
Cryptocurrencies show some properties that differ from typical financial instruments. For example, dynamic volatility, larger price jumps, and other market participants and their associated characteristics can be observed (Pardalos, Kotsireas, Guo, & Knottenbelt, 2020). Especially high tail risk (Sun, Dedahanov, Shin, & Li, 2021; Corbet, Meegan, Larkin, Lucey, & Yarovaya, 2018; Borri, 2019) leads to the question of whether the methods and procedures established in risk management are suitable for measuring the resulting market risks of cryptos appropriately. Therefore, we examine the risk measurement of Bitcoin, Ethereum, and Litecoin. In addition to the classic methods of market risk measurement, historical simulation, and the variance-covariance approach, we also use the extreme value theory to measure risk. Only the extreme value theory with the peaks-over-threshold method delivers satisfactory backtesting results at a confidence level of 99.9%. In the context of our analysis, the highly volatile market phase from January 2021 was crucial. In this, extreme deflections that have never been observed before in the time series have significantly influenced backtesting. Our paper underlines that critical market phases could not be sufficiently observed from the short time series, leading to adequate backtesting results under the standard market risk measurement. At the same time, the strength of the extreme value theory comes into play here and generates a preferable risk measurement.
<abstract><p>The Bitcoin futures market is growing and, as such, becoming more sophisticated. A small change in price may therefore have a large impact on the market. This paper investigates the propensity of 18 different competing GARCH family models and error distributions to model and forecast the volatility of Bitcoin futures returns. The study employs two different time periods (from January 2, 2018 to June 14, 2021; and March 11, 2020 to June 14, 2021). From the results, iGARCH(1, 1)-Students't-distribution (STD) is selected as the best performing model among the constructed models for the first period. By fitting the best three models from the first period to the second period, the iGARCH(1, 1)-STD is again selected as the optimal model. However, the iGARCH(1, 1)-normal inverse Gaussian (NIG) provides a significant variance forecast when used for in-sample and out-of-sample forecasts before the financial crisis and during the financial crisis, respectively. Our results indicate the impacts of past squared shocks on squared returns of Bitcoin futures and the ability of iGARCH(1, 1)-STD to capture such innovations and the propensity of iGARCH(1, 1)-NIG to optimally forecast the variance of Bitcoin futures returns.</p></abstract>