Currently the most liquidly traded options on the crypto underlying are the so-called inverse options. An inverse option contract is quoted and traded in the units of the underlying cryptocurrency. The main economic reason for popularity of inverse contracts in the crypto exchanges (such as Deribit) is that inverse contracts enable to operate without maintaining fiat cash accounts. For the theoretical part, we show that inverse options are just regular vanilla options considered under the martingale measure using the forward of the underlying as the numéraire. This measure requires an adjustment to option delta. For the empirical part, we use Deribit options data of past four years to backtest delta-hedged option strategies. We introduce USD and Coin accounting of trading Profit&Loss (P&L) which is important for designing strategies in crypto options. We show empirically that USD and Coin accounting rules are equivalent when performance is measured is Coin and USD units, respectively. We establish that the risk-premia observed in options on Deribit is negative and significant so that strategies selling volatility are expected to generate positive risk-adjusted performance in the long-term.
This paper uses the wavelet coherence approach and the wavelet-based Granger causality test, to investigate the effect of the five waves of the COVID-19 pandemic on Bitcoin, Ethereum, BNB, Cardano, Ripple, Dogecoin, TRON, Litecoin, Stellar, and Bitcoin Cash in a time-frequency framework from 22 January 2020 to 22 February 2022. The results show the presence of correlation between the COVID-19 pandemic and cryptocurrencies in the short-medium term, and a positive impact on Bitcoin only during the first wave of the pandemic in the medium term. However, Cardano failed to act as a risk diversifier. In the long-term, our analysis shows that Ethereum, BNB, Ripple, Dogecoin, TRON, Litecoin, Stellar, and Bitcoin Cash proved their ability as strong safe haven assets, even during different periods of the COVID-19 crisis. Our results can provide helpful information for policymakers, and cryptocurrency market main and hedge funds managers during periods of uncertainty.
Richard De Freitas Pinto, Viviane Fagundes de [UNESP] Mattos, Luiz Ricardo Nakamura
Esse trabalho apresenta a modelagem do volume mensal de transações da criptmoeda Ethereum por meio da metodologia de Box-Jenkins, envolvendo as etapas: análise exploratória, identificação, estimação e validação, algumas das quais executadas com a utilização de diferentes técnicas. O modelo encontrado pela modelagem SARIMA (Modelo autoregressivo integrado de médias móveis sazonal) conseguiu descrever o comportamento linear dos dados de forma satisfatória, mas não foi suficiente para descrever o comportamento da série, composta por movimento linear e não linear, sendo melhor representada por um modelo híbrido.
Cryptocurrencies are considered to be among the most disruptive innovations done in the financial sector within the last decade. It is a digital asset that is designed to serve as a medium of exchange using cryptography. Financial modeling of cryptocurrencies is needed in order to determine the presence of dependence between currencies. Copulas functions assist in modeling dependency structure by making it possible to separate marginal distributions of a given multivariate distribution. The purpose of the study was to model dependencies of cryptocurrencies using copula Garch. The study proposed the use of copula Garch model to model the dependence of cryptocurrency price data. Bivariate copula was extended to Bivariate Copula Garch in order to model prices and measure the cryptocurrency dependence. Prices of the four cryptocurrencies (Bitcoin, Binance, Litecoin and Dogecoin) were analyzed to establish whether there exists any dependency. The results showed standard Garch (1,1) under the highly flexible ARMA-GARCH model was appropriate to identify the true patterns of index returns. Fitting the copula standard Garch (1,1) model to the currencies, it was observed that the pair Litecoin and Bitcoin has the highest tail dependence among the selected cryptocurrencies, which implies that change in prices of Litecoin will influence the prices of Bitcoin and vice versa is true. Optimization of the cryptocurrencies showed that Dogecoin has the best optimization. The results of this study indicate that investing on Dogecoin significantly reduces risk irrespective of significant correlation among Litecoin, Bitcoin and Binance. Standard Garch (1,1) is the best in identifying dependence between the cryptocurrencies.
In this paper, we studied the extreme connectedness between Bitcoin and crypto-mining stocks using the quantile connectedness approach of Ando et al. (2022). We estimated the connectedness (i.e., the direction and strength of spillover effects) at the median, extreme lower, and extreme upper quantiles. Our results revealed a highly interconnected system, with Bitcoin identified as a net transmitter of shocks. RIOT and MARA also emerged as major net transmitters in the system, while GREE and NILE were net receivers. The spillover effects were more pronounced during extreme market conditions compared to normal conditions. Moreover, the connectedness of the system progressively increased, peaking in 2021 when China banned crypto-mining. The extreme and dynamic connectedness identified in this study offers valuable insights for investors regarding hedging strategies and portfolio allocation, as well as for regulators focused on financial stability and systemic risk.
An Pham Ngoc Nguyen, Martin Crane, Marija Bezbradica
Abstract The Cryptocurrency Volatility Index (CVI index) has been introduced to estimate the 30-day future volatility of the cryptocurrency market. In this article, we introduce a new Deep Neural Network with an attention mechanism to forecast future values of this index. We then look at the stability and performance of our proposed model against the benchmark models widely used for time series prediction. The results show that our proposed model performs well when compared to popular methods such as traditional Long Short Term Memory, Temporal Convolution Network, and other statistical methods like Simple Moving Average, Random Forest and Support Vector Regression. Furthermore, we show that the well-known Simple Moving Average method, while it has its own advantages, has the weak spot when dealing with time series with large fluctuations.
In his paper we explore the dynamic time-frequency volatility spillover effects between Bitcoin and Chinese financial markets, covering several main events. Results show that the volatility spillovers are asymmetric, with the Bitcoin market being the net risk receiver. The total spillovers, driven by medium and low frequency components, peak before the China stock crash in 2015, increase since the trade disputes between China and the US in 2018, and peak since the COVID-19 pandemic. The pairwise spillovers related to Bitcoins mainly concentrate on stock, foreign exchange, and copper futures markets. Although the pairwise spillovers are weak, the Bitcoin market is the net receiver in medium and low frequencies under external shocks. Therefore, investors and regulators need to assess the potential risk of Bitcoin based on asset type with the perspectives of time and frequency.
Federico P. Cortese, Petter N. Kolm, Erik Lindström
Abstract We apply the statistical sparse jump model, a recently developed, interpretable and robust regime-switching model, to infer key features that drive the return dynamics of the largest cryptocurrencies. The algorithm jointly performs feature selection, parameter estimation, and state classification. Our large set of candidate features are based on cryptocurrency, sentiment and financial market-based time series that have been identified in the emerging literature to affect cryptocurrency returns, while others are new. In our empirical work, we demonstrate that a three-state model best describes the dynamics of cryptocurrency returns. The states have natural market-based interpretations as they correspond to bull, neutral, and bear market regimes, respectively. Using the data-driven feature selection methodology, we are able to determine which features are important and which ones are not. In particular, out of the set of candidate features, we show that first moments of returns, features representing trends and reversal signals, market activity and public attention are key drivers of crypto market dynamics.
S. Pourmohammad Azizi, Chien Yi Huang, Ti An Chen, Shu Chuan Chen · 5 authors
<abstract><p>In this article, an alternate method for estimating the volatility parameter of Bitcoin is provided. Specifically, the procedure takes into account historical data. This quality is one of the most critical factors determining the Bitcoin price. The reader will notice an emphasis on historical knowledge throughout the text, with particular attention paid to detail. Following the production of a historical data set for volatility utilizing market data, we will analyze the fundamental and computed values of Bitcoin derivatives (futures), followed by implementing an inverse problem modeling method to obtain a second-order differential equation model for volatility. Because of this, we can accomplish what we set out to do. As a direct result, we will be able to achieve our objective. Following this, the differential equation of the second order will be solved by an artificial neural network that considers the dataset. In conclusion, the results achieved through the utilization of the Python software are given and contrasted with a variety of other research approaches. In addition, this method is determined with alternative ways, and the outcomes of those comparisons are shown.</p></abstract>
Abstract We investigate the out-of-sample diversification benefits of cryptocurrencies from a generalised perspective, a cryptocurrency-factor level, with traditional and machine-learning-enhanced asset allocation strategies. The cryptocurrency factor portfolios are formed in an analogous way to equity anomalies by using more than 2000 cryptocurrencies. The findings indicate that a stock–bond portfolio incorporating size- and momentum-based cryptocurrency factors can achieve statistically significant out-of-sample diversification benefits for investors with different risk preferences. Additionally, machine-learning-enhanced asset allocation strategies can boost the traditional approaches by enriching (shrinking) the distributions of weights allocated to potentially effective cryptocurrency factors. Our findings are robust to (i) the inclusion of transaction costs, (ii) an alternative benchmark portfolio, and (iii) a rolling-window estimation scheme.
This research examines the correlations between the return volatility of cryptocurrencies, global stock market indices, and the spillover effects of the COVID-19 pandemic. For this purpose, we employed a two-stage multivariate volatility exponential GARCH (EGARCH) model with an integrated dynamic conditional correlation (DCC) approach to measure the impact on the financial portfolio returns from 2019 to 2020. Moreover, we used value-at-risk (VaR) and value-at-risk measurements based on the Cornish–Fisher expansion (CFVaR). The empirical results show significant long- and short-term spillover effects. The two-stage multivariate EGARCH model’s results show that the conditional volatilities of both asset portfolios surge more after positive news and respond well to previous shocks. As a result, financial assets have low unconditional volatility and the lowest risk when there are no external interruptions. Despite the financial assets’ sensitivity to shocks, they exhibit some resistance to fluctuations in market confidence. The VaR performance comparison results with the assets portfolios differ. During the COVID-19 outbreak, the Dow (DJI) index reports VaR’s highest loss, followed by the S&P500. Conversely, the CFVaR reports negative risk results for the entire cryptocurrency portfolio during the pandemic, except for the Ethereum (ETH).
Grzegorz Dudek, Piotr Fiszeder, Paweł Kobus, Witold Orzeszko
Forecasting cryptocurrency volatility can help investors make better-informed investment decisions in order to minimize risks and maximize potential profits. Accurate forecasting of cryptocurrency price fluctuations is crucial for effective portfolio management and contributes to the stability of the financial system by identifying potential threats and developing risk management strategies. The objective of this paper is to provide a comprehensive study of statistical and machine learning methods for predicting daily and weekly volatility of the following four cryptocurrencies: Bitcoin, Ethereum, Litecoin, and Monero. Several models and forecasting methods are compared in terms of their forecasting accuracy, i.e., HAR (heterogeneous autoregressive), ARFIMA (autoregressive fractionally integrated moving average), GARCH (generalized autoregressive conditional heteroscedasticity), LASSO (least absolute shrinkage and selection operator), RR (ridge regression), SVR (support vector regression), MLP (multilayer perceptron), FNM (fuzzy neighbourhood model), RF (random forest), and LSTM (long short-term memory). The realized variance calculated from intraday returns is used as the input variable for the models. In order to assess the predictive power of the models considered, the model confidence set (MCS) procedure is applied. Our experimental results demonstrate that there is no single best method for forecasting volatility of each cryptocurrency, and different models may perform better depending on the specific cryptocurrency, choice of the error metric and forecast horizon. For daily forecasts, the method that is always found in a set of best models is linear SVR, while for weekly forecasts, there are two such methods, namely FNM and RR. Furthermore, we show that simple linear models such as HAR and ridge regression, perform not worse than more complex models like LSTM and RF. The research provides a useful reference point for the development of more sophisticated models.
With the advent of 2022, the impact of the COVID-19 pandemic has weakened, the US labor market has recovered, and inflation has been severe, creating the conditions for the Fed to tighten its policies. At the same time, cryptocurrencies as a hot topic in recent years; ETH is one of the most popular cryptocurrencies in the market; this article aims to assess the impact of the Fed's raised interest rates on the yield and volatility of cryptocurrency Ethereum (ETH) based on data on the ETH price and the US dollar/CNY exchange rate since 2022. And further, simulate the impact on the overall cryptocurrency market. This paper constructs VAR and ARMA-GARCH models to analyze ETH returns and volatility variations. The results of these models suggest that the exchange rate rise triggered by the Fed's rate hike has had a negative impact on ETH yields and increased the volatility of its returns. Further, this article recommends that investors should adjust their portfolios according to their risk appetite in an uncertain market environment.
In this study we investigate possible long-range trends in the cryptocurrency market. We employed the Hurst exponent in a sample covering the period from 1 January 2016 to 26 March 2021. We calculated the Hurst exponent in three non-overlapping consecutive windows and in the whole sample. Using these windows, we assessed the dynamic evolution in the structure and long-range trend behavior of the cryptocurrency market and evaluated possible changes in their behavior towards an efficient market. The innovation of this research is that we employ the Hurst exponent to identify the long-range properties, a tool that is seldomly used in analysis of this market. Furthermore, the use of both the R/S and the DFA analysis and the use of non-overlapping windows enhance our research’s novelty. Finally, we estimated the Hurst exponent for a wide sample of cryptocurrencies that covered more than 80% of the entire market for the last six years. The empirical results reveal that the returns follow a random walk making it difficult to accurately forecast them.
Leonardo H.S. Fernandes, Werner Kristjanpoller, Benjamin Miranda Tabak
This paper performs the asymmetric multifractal cross-correlation analysis to examine the COVID-19 effects on three relevant high-frequency fiat currencies, namely euro (EUR), yen (YEN) and the Great Britain pound (GBP), and two cryptocurrencies with the highest market capitalization and traded volume (Bitcoin and Ethereum) considering two periods (Pre-COVID-19 and during COVID-19). For both periods, we find that all pairs of these financial assets are characterized by overall persistent cross-correlation behavior [Formula: see text]. Moreover, COVID-19 promoted an increase in the multifractal spectrum’s width, which implies an increase in the complexity for all pairs considered here. We also studied the Generalized Cross-correlation Exponent, which allows us to verify that there is no asymmetric behavior between Bitcoin and fiat currencies and between Ethereum and fiat currencies. We conclude that investing simultaneously in major fiat currencies and leading cryptocurrencies can reduce the portfolio risk, leading to improvement in the investment results.
The emergence of cryptocurrencies futures market is an innovative platform for prudent investors to hedge risk contained in their portfolio. However, the dicey environment of cryptocurrencies and mandatory requirement of Ind AS 39 has aroused the need for estimating hedging effectiveness of their futures market. This treatise is an attempt to investigate the hedging effectiveness of Bitcoin, Ethereum, XRP and Bitcoin Cash covering the period from June 2018 to May 2022. The interconnectedness of their spot and futures markets is initially studied using Johansen cointegration test, dynamic conditional correlation model, vector error correction model and block exogeneity Wald test. Their empirical results indicate interconnectedness in these markets having significant long-term relationship; persistent volatility correlations; significant unidirectional long-term causality from futures to spot; and bidirectional short-term causality in all cryptocurrencies. So, investors can hedge their risk by engaging position in cryptocurrencies’ futures. The OLS, VECM, GARCH and TARCH methodologies are applied to estimate static optimal hedge ratios and their estimates indicate that all cryptocurrencies have negative and significant ratios except XRP. The symmetric as well as asymmetric diagonal VECH and diagonal BEKK methodologies are applied to estimate dynamic-hedge ratios and their estimates depict negative mean dynamic-hedge ratios of all cryptocurrencies except XRP. These estimations imply that investors having long position in spot contracts of Bitcoin, Ethereum and Bitcoin Cash should hedge by taking short position in their futures contracts, respectively. However, XRP investors should hedge by taking long position in XRP future contracts. The empirical results clearly indicate the outperformance of static hedge strategies over dynamic hedge strategies as variance reduction framework of Ederington favours static OLS hedge strategy and the risk–return framework of Howard and D’Antonio favours static VECM hedge strategy for all cryptocurrencies. So, the long-run considerations have played a more crucial role as compared to short-run information. These findings may guide investors having different objective functions in understanding the effectiveness of different hedge strategies and their usage for achieving their objective functions. Policymakers, treasurers and auditors may also be benefitted from the insights provided in the present treatise on different estimation methodologies for hedging effectiveness.
Bitcoin establishes itself as an investment asset and is often named the New Gold. This study, however, shows that the two assets are different in univariate and multivariate aspects. First, we construct GARCH, APARCH and APARCH-in-Mean models to analyze and compare conditional variance properties of Bitcoin and Gold, and find Bitcoin does not have the significant inverse leverage effect as Gold. Then we apply the BEKK-GARCH model to estimate time-varying conditional correlations between Bitcoin and Gold with other major market indexes. The results show that Bitcoin can not hedge the market risk, especially when a crash occurs. So we conclude that Bitcoin and Gold feature fundamentally different properties as assets and linkages to equity markets.
This study estimates the effects of double long memory and structural breaks on the persistence level of six major cryptocurrency markets. We apply the Bai and Perron’s structural break test, Inclán and Tiao’s iterated cumulative sum of squares (ICSS) algorithm, and the fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model with different distributions. The results show that long memory and structural breaks characterize the conditional volatility of cryptocurrency markets and confirm our hypothesis that ignoring structural breaks leads to an underestimation of the persistence of volatility modelling. The ARFIMA-FIGARCH model with structural breaks and a skewed Student–t distribution fits the cryptocurrency market’s price dynamics well.
Given the skyrocketing returns earned by bitcoin, it has received widespread attention as an investment asset. The shocks experienced by stock and bond markets over time and especially during the COVID-19 pandemic has led to an evaluation of bitcoin as a wealth protection asset, a role that gold has played until now. The current paper tests the hedging and safe haven properties of bitcoin in a broad portfolio of both developed and emerging markets stocks, bonds and real estate over a period of 10 years and during COVID-19 pandemic. Using a DCC-GARCH method, the study finds weak hedge and safe haven benefits of bitcoin. The results of the study establish that there is still a long way to go before bitcoin displays a strong safe haven behavior. However, there is a need for portfolio managers to become more cognizant about bitcoin given its potential to protect their portfolios.