Cryptocurrencies are risky currencies due to their extreme price volatilities and requires an estimation of coherent risk measures for an effective portfolio optimization and risk management. We focus on seven cryptocurrencies (Bitcoin, Ethereum, Litecoin, Ripple, Das, Monero, and Steller) and provide empirical application of Fissler and Ziegel joint loss dynamic models (FZL) for joint Value-at-Risk (VaR) and Expected Shortfall (ES) in a cryptocurrency context at α= 0.01 and α= 0.025 risk levels. Results show Ethereum and Steller as less risky currencies followed by Monero, Das, Litecoin, Bitcoin, and largest for Ripple suggesting that Ethereum and Steller requires the least capital to absorb losses. Following this result, we argue that market participants interested in cryptocurrencies can follow the rankings in this study to hedge, calculate margins, and capital requirement to maximize utility whiles minimizing risk to ensure financial stability in the global economy.
The paper examines whether an unanticipated event like the COVID-19 crisis has strengthened the contagion in the cryptocurrency market utilizing samples of data representing the pre-crisis and post-crisis periods. Employing the wavelet coherence and DCC-GARCH(1,1) models, we identify that the cryptocurrency market started integrating from 2018 as volatility within the market reduced. Our main finding is that the cryptocurrency market is highly interconnected and that the contagion strengthened during the crisis period. We draw appropriate policy implications from these findings.
The intensity of volatility persistence is sensitive to time scales, market returns and data regimes. Investors who acquire intangible digital assets in the form of "cryptocurrencies" should consider that they may or may not receive a fiat currency. Sometimes there is a possibility of a loss of the entire investment due to volatility of prices in digital currency/cryptocurrency. Several empirical studies are conducted to measure the volatility behavior of cryptocurrencies using different mathematical models like: i) Autoregressive Distributed Lag (ARDL) Model, ii) Heterogeneous Autoregressive (HAR) Model, iii) Autoregressive Conditional Heteroskedasticity (ARCH) Model, and iv) Generalized Autoregressive Conditional Heteroscedastic (GARCH) Models. This paper focuses on the review of various GARCH Models studied during 1980-2020.
Inzamam Ul Haq, Paulo Ferreira, Derick Quintino, Nhan Huynh · 5 authors
The purpose of the research is to explore the dynamic multiscale linkage between economic policy uncertainty, equity market volatility, energy and sustainable cryptocurrencies during the COVID-19 period. We use a multiscale TVP-VAR model considering level (EPUs and IDEMV) and returns series (cryptocurrencies) from 1 December 2019 to 30 September 2022. The data are then decomposed into six wavelet components, based on the wavelet MODWT method. The TVP-VAR connectedness approach is used to uncover the dynamic connectedness among EPUs, energy and sustainable cryptocurrency returns. Our findings reveal that CNEPU (USEPU) is the strongest (weakest) NET volatility transmitter. IDEMV is the most consistent volatility NET transmitter among all uncertainty indices across the original returns and wavelet scales (D1~D6). Energy cryptocurrencies, i.e., GRID, POW and SNC, are more likely to receive volatility spillovers than sustainable cryptocurrencies during a turbulent period (COVID-19). XLM (XNO) is least (most) affected by volatility spillover in system-wide connectedness, and XLM (ADA and MIOTA) showed a consistent (heterogeneous) non-recipient behavior across the six wavelet (D1~D6) scales and original return series. This study uncovers the dynamic connectedness across multiscale, which will support investors considering different investment horizons (D1~D6).
Arthur A. B. Pessa, Matjaž Perc, Haroldo V. Ribeiro
Cryptocurrencies are considered the latest innovation in finance with considerable impact across social, technological, and economic dimensions. This new class of financial assets has also motivated a myriad of scientific investigations focused on understanding their statistical properties, such as the distribution of price returns. However, research so far has only considered Bitcoin or at most a few cryptocurrencies, whilst ignoring that price returns might depend on cryptocurrency age or be influenced by market capitalization. Here, we therefore present a comprehensive investigation of large price variations for more than seven thousand digital currencies and explore whether price returns change with the coming-of-age and growth of the cryptocurrency market. We find that tail distributions of price returns follow power-law functions over the entire history of the considered cryptocurrency portfolio, with typical exponents implying the absence of characteristic scales for price variations in about half of them. Moreover, these tail distributions are asymmetric as positive returns more often display smaller exponents, indicating that large positive price variations are more likely than negative ones. Our results further reveal that changes in the tail exponents are very often simultaneously related to cryptocurrency age and market capitalization or only to age, with only a minority of cryptoassets being affected just by market capitalization or neither of the two quantities. Lastly, we find that the trends in power-law exponents usually point to mixed directions, and that large price variations are likely to become less frequent only in about 28\% of the cryptocurrencies as they age and grow in market capitalization.
Bu çalışmada, Bitcoin ile gelişmiş ve gelişmekte olan ülkelerin hisse senedi piyasaları arasındaki volatilite yayılım ilişkisinin incelenmesi ve bulguların finansal piyasaları etkileyen küresel olaylar bağlamında değerlendirilmesi amaçlanmıştır. Bu amaçla 03.01.2017-25.03.2022 dönemi, Bitcoin, MSCI ABD, MSCI Avrupa ve MSCI gelişmekte olan piyasalar endeksi günlük verilerine zamanla değişen parametre vektör otoregresif (TVP-VAR) modeli uygulanmıştır. Uygulama sonucunda Bitcoin’in MSCI ABD ve MSC Avrupa karşsısında net volatilite alıcısı olduğu ve MSCI gelişmekte olan piyasalar karşısında net volatilite yayıcısı olduğu gözlenmiştir. MSCI ABD’nin net volatililite yayıcısı ve MSCI gelişmekte olan piyasaların ise net volatilite alıcısı olduğu tespit edilmiştir. Ayrıca Bitcoin’in gelişmiş ve gelişmekte olan piyasalarla zayıf bağlantılı olduğu gözlenmiştir. Bulgular, volatilite yayılımının aşırı artış-azalış gösterdiği dönemlerde tüm dünyayı etkileyen küresel olaylar olduğunu göstermiştir.
In this paper, we propose a novel approach for forecasting cryptocurrency portfolios, harnessing modified versions of the N-BEATS deep learning architecture, integrated with convolutional network layers, Transformer mechanisms, and the Mish activation function. Our thorough evaluation, featuring an extensive sample size exceeding 4 million portfolio test samples, shows these variations outperforming traditional and other deep learning forecasting methods across various metrics. Particularly noteworthy is our N-BEATS Perceiver model, a Transformer-based variation, which not only delivers superior forecast accuracy but also exhibits a robust risk profile with less downside. Furthermore, the model performs exceptionally well under the TOPSIS method across a broad spectrum of portfolio evaluation parameters, making it a valuable asset for both portfolio selection and risk management in the dynamic cryptocurrency market.
According to past research utilizing Bitcoin and other cryptocurrencies, Bitcoin has been shown to lead most other cryptocurrencies in terms of price movements. However, existing studies tend to focus on the direction of the lead-lag relationship instead of the duration of the lead-lag time. Furthermore, they are handicapped by the reliance on low-frequency data such as daily prices. This paper showcases the measurement of the lead-lag duration between cryptocurrencies using ultra-high-frequency tick-by-tick data, via the pair of Bitcoin and Cardano. Tick-by-tick data bring unique challenges in terms of methodology. The vast majority of time series econometrics methods are designed for use with data collected at regularly spaced time intervals, such as every hour, every day, etc. Tick-by-tick data, on the other hand, are not synchronized in any way and do not arrive at consistently spaced time intervals. Consequently, an asynchronous data integration methodology is utilized to estimate the Bitcoin price lead over Cardano price for each month beginning in January 2019 and continuing through May 2021. The length of the lead time ranges from 16 seconds to 118 seconds, with an average of around 57 seconds. Throughout the study period, the lengths of the lead time manifest a general trend of decline, which is shown to be statistically significant via non-parametric tests. Testing of seasonal patterns turns out to be not significant. The methodology and the findings of this paper have implications for both academics and practitioners, for example, when studying and implementing statistical arbitrage with cryptocurrencies.
Inferring the heterogeneous connection pattern of a networked system of multivariate time series observations is a key issue. In finance, the topological structure of financial connectedness in a network of assets can be a central tool for risk measurement. Against this, we propose a topological framework for variance decomposition analysis of multivariate time series in time and frequency domains. We build on the network representation of time–frequency generalized forecast error variance decomposition (GFEVD), and design a method to partition its maximal spanning tree into two components: (a) superhighways, i.e. the infinite incipient percolation cluster, for which nodes with high centrality dominate; (b) roads, for which low centrality nodes dominate. We apply our method to study the topology of shock transmission networks across cryptocurrency, carbon emission and energy prices. Results show that the topologies of short and long run shock transmission networks are starkly different, and that superhighways and roads considerably vary over time. We further document increased spillovers across the markets in the aftermath of the COVID-19 outbreak, as well as the absence of strong direct linkages between cryptocurrency and carbon markets.
This paper studies the monthly expiration effect in the bitcoin markets. The emergence of trading in bitcoin futures in regulated markets is an ideal occasion to test this effect on an asset with singular characteristics. Our results with intraday data show that around the time of maturity there are significant changes in the trading volume, volatility and return of bitcoin, an asset that is traded in many exchanges simultaneously. Therefore, there is a clear expiration effect related to bitcoin futures. The closer to the expiration time (shortly beforehand or afterwards), the more intense these effects are. However, in spite of these general results, the expiration effect is not homogeneous across exchanges and depends on the characteristics of the futures contract in question. Robustness tests are also applied to confirm the results. The increasing participation of institutional investors is consistent with our findings, particularly in relation to the expiration effects of cash-settled futures, as these contracts are more appealing for sophisticated investors who could be interested in arbitrage or speculative processes.
Marcin Wątorek, Jarosław Kwapień, Stanisław Drożdż
In this study the cross-correlations between the cryptocurrency market represented by the two most liquid and highest-capitalized cryptocurrencies: bitcoin and ethereum, on the one side, and the instruments representing the traditional financial markets: stock indices, Forex, commodities, on the other side, are measured in the period: January 2020--October 2022. Our purpose is to address the question whether the cryptocurrency market still preserves its autonomy with respect to the traditional financial markets or it has already aligned with them in expense of its independence. We are motivated by the fact that some previous related studies gave mixed results. By calculating the $q$-dependent detrended cross-correlation coefficient based on the high frequency 10 s data in the rolling window, the dependence on various time scales, different fluctuation magnitudes, and different market periods are examined. There is a strong indication that the dynamics of the bitcoin and ethereum price changes since the March 2020 Covid-19 panic is no longer independent. Instead, it is related to the dynamics of the traditional financial markets, which is especially evident now in 2022, when the bitcoin and ethereum coupling to the US tech stocks is observed during the market bear phase. It is also worth emphasizing that the cryptocurrencies have begun to react to the economic data such as the Consumer Price Index readings in a similar way as traditional instruments. Such a spontaneous coupling of the so far independent degrees of freedom can be interpreted as a kind of phase transition that resembles the collective phenomena typical for the complex systems. Our results indicate that the cryptocurrencies cannot be considered as a safe haven for the financial investments.
Phumudzo Lloyd Seabe, Claude Rodrigue Bambe Moutsinga, Edson Pindza
Highly accurate cryptocurrency price predictions are of paramount interest to investors and researchers. However, owing to the nonlinearity of the cryptocurrency market, it is difficult to assess the distinct nature of time-series data, resulting in challenges in generating appropriate price predictions. Numerous studies have been conducted on cryptocurrency price prediction using different Deep Learning (DL) based algorithms. This study proposes three types of Recurrent Neural Networks (RNNs): namely, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bi-Directional LSTM (Bi-LSTM) for exchange rate predictions of three major cryptocurrencies in the world, as measured by their market capitalization—Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC). The experimental results on the three major cryptocurrencies using both Root Mean Squared Error (RMSE) and the Mean Absolute Percentage Error (MAPE) show that the Bi-LSTM performed better in prediction than LSTM and GRU. Therefore, it can be considered the best algorithm. Bi-LSTM presented the most accurate prediction compared to GRU and LSTM, with MAPE values of 0.036, 0.041, and 0.124 for BTC, LTC, and ETH, respectively. The paper suggests that the prediction models presented in it are accurate in predicting cryptocurrency prices and can be beneficial for investors and traders. Additionally, future research should focus on exploring other factors that may influence cryptocurrency prices, such as social media and trading volumes.
In the post-pandemic era, two issues including the currency competition between BTC and the US dollar and the competition between the commodity and monetary medium functions of BTC are critical. By applying the Markov switching model, the cyclical nature of the numbers of additional confirmed COVID-19 cases and deaths are verified on daily basis. So, these two factors are assumed to follow the Ornstein–Uhlenbeck process. Then, we estimate parameters to establish the structural characteristics of post-pandemic era and the start of post-pandemic era. In order to clarify these two issues, we use vector autoregression for testing the related macrocosmic and financial variables and BTC. Systematic evidences are provided regarding the relationships among BTC, related macrocosmic, related financial variables, related COVID-19 variables. Our findings provide a useful insight into currency competition and commodity competition on the basis of the impulse response of BTC to US dollar fluctuation and the impulse response of BTC to expected inflation and volatility in the post-pandemic era. These findings indicate increased currency competition between Bitcoin and the US dollar in the post-pandemic era. Therefore, currency competition should be more valued than Commodity Competition in the post-pandemic era. This provides a useful guideline for Bitcoin’s management.
The cryptocurrency market has enormous growth potential. In this study, the aim is to investigate how the news (shocks) affects cryptocurrency market volatility. This is significant because, while cryptocurrencies are gaining popularity among investors, the market’s extreme volatility discourages some prospective buyers, while also causing large losses for inexperienced investors. From 8 March 2019 to 30 November 2022, data from Bitcoin, Binance Coin, Ethereum, Dogecoin, and XRP were collected for the current study. The E-GARCH model was applied to the framed dataset to achieve the research aim. We discovered that the value of the size factor for all currencies was statistically significant, indicating that the news (shocks) significantly impacts volatility. Furthermore, volatility persistence in all cryptocurrencies is found to be very high and statistically significant. These study findings can help investors understand the impact of the news (shocks) on volatility in cryptocurrency returns.
António Portugal Duarte, Fátima Sol Murta, Nuno Baetas da Silva, Beatriz Rodrigues Vieira
This paper analysis and compares the volatility of seven cryptocurrencies – Bitcoin, Dogecoin, Ethereum, BitcoinCash, Ripple, Stellar and Litecoin – to the volatility of seven centralized currencies – Yuan, Yen, Canadian Dollar, Brazilian Real, Swiss Franc, Euro and British Pound. We estimate GARCH models to analyze their volatility. The results point to a considerably high volatility of cryptocurrencies when compared to that of centralized currencies. Therefore, we conclude that cryptocurrencies still fall far short of fulfilling all the requirements to be considered as a currency, specifically regarding the functions of store of value and unit of account.
Twitter sentiment has been found to be useful in predicting whether the price of Bitcoin will rise or fall will climb or decline. Modelling market activity and hence emotion in the Bitcoin ecosystem gives insight into Bitcoin price forecasts. We take into account not just the emotion retrieved not just from tweets, but also from the quantity of tweets. With the goal of optimising time window within which expressed emotion becomes a credible predictor of price change, we provide data from research that examined the link among both sentiment and future price at various temporal granularities. We demonstrate in this study that not only can price direction be anticipated, but also the magnitude of price movement with same accuracy, and this is the study's major scientific contribution. Non-Fungible Token (NFT) has gained international interest in recent years as a blockchain-based application. The most prevalent kind of NFT that can be stored on many blockchains is digital art. We did studies on CryptoPunks, the most popular collection on the NFT market, in examine and depict each and every major ethical challenges. We investigated ethical concerns from three perspectives: design, trade transactions, and relevant Twitter topics. Using Python libraries, a Twitter crawler, and sentiment analysis tools, we scraped data from Twitter and performed the analysis and prediction on bitcoin and NFTs.
High Resolution Image Download MS PowerPoint Slide Reduction of greenhouse gas emissions has been a top priority for activists, scientists, and policy makers across the globe, and it is one of the main drivers for the transition to renewable energy generation. Bitcoin is a decentralized global transaction network of an eponymous digital currency. It has been praised for its openness, decentralization, and censorship resistance, as well as criticized for its inefficiency, criminal use, and enormous electricity consumption. We discuss the challenges in the renewable energy transition, properties of the bitcoin network, and the role of bitcoin mining operations in the global energy production and consumption. Although the adoption path for bitcoin is likely to be volatile with an uncertain outcome, the opportunities offered by bitcoin mining in reduction of the greenhouse gas emissions and renewable energy transition are greater than generally assumed.
This paper examines return spillovers within and between different DeFi, cryptocurrency, stock, and safe-haven assets. For the period January 2019 to March 2022, we find that DeFi and cryptocurrency asset markets exhibit strong within-market and between-market return spillovers, that stock and safe-haven markets show weak connectedness, and that safe-haven assets are minor receivers and transmitters of between-market spillover effects. The connectedness between markets is time-varying and reveals structural changes in early 2020. Furthermore, we document that financial conditions shape the dynamics of return spillover effects between markets.
This paper applies the multivariate GARCH models to investigate the role of Bitcoin as a hedge and safe haven for ASEAN+6 stock markets compared to gold. We used daily data for the dates 2 January 2017–20 January 2023, covering the recent COVID-19 pandemic. The empirical findings provide compelling evidence of cross-market shock and volatility transmission between stock returns and Bitcoin returns in both directions. Therefore, the dynamics of Bitcoin returns significantly influence the volatility of stock returns, and the relationship also holds in reverse. All diagonal element estimations are statistically significant for both periods, as shown by the findings of the return and volatility spillovers between the returns of gold and the ASEAN+6 stock market. For most ASEAN+6 equity markets evaluated, Bitcoin and gold are not safe havens, and their inclusion increases the portfolio downside risk.
Previous research has shown volatility jumps and co-jumping behaviours in cryptocurrency markets. Motivated by these findings, we employ the herding effect and financial contagion channel to outline a theoretical framework of volatility-state-dependent correlations in cryptocurrency markets. We show that digital currency markets are more strongly correlated when experiencing an identical volatility regime, which echoes co-jumping behaviours addressed by the literature. Moreover, the strong correlation that occurs when the paired cryptocurrencies simultaneously experience a high volatility regime results in the least effectiveness of diversification in terms of a minimum portfolio risk reduction. Last but not least, the proposed state-dependent approach in this study proves effective at the task of risk forecasting and risk reduction for cryptocurrency portfolios, beyond the bivariate GARCH-based models, which are a pure and simple time-dependent approach.
Zaheer Anwer, Saqib Farid, Ashraf Khan, Noureddine Benlagha
In the wake of proliferation of cryptocurrencies and growing concerns regarding their environmental impact, we investigate the dynamic co-movement of digital assets and environmentally sustainable assets. We use daily data of five global indices from 01 March, 2017 to 15 May, 2022. The results suggest that environmentally sustainable indices and cryptocurrency indices demonstrate co-movements during pandemic. However, in the normal times, they mostly remain detached from each other. Therefore, it can be argued that both the asset classes can serve as hedge against each other. The findings carry important implications for the investment industry and regulators.
Muhammad Irfan, Mubeen Abdur Rehman, Sarah Nawazish, Yu Hao
This study aims to investigate the performance and behavior of fiat- and gold-backed cryptocurrencies to support stakeholders through the preparation of a portfolio from 1 January 2021 to 30 June 2022. Moreover, while searching for a hedge or a diversifier to construct a less risky portfolio with handsome returns, the prices of fiat-backed cryptocurrencies report high fluctuation during the sample period. ARIMA-EGARCH models have been employed to examine the volatile behavior of these cryptocurrencies. The empirical results are mixed as Bitcoin has been highly volatile during the economic recession. Due to its volatility, investors seek a safe haven. Ripple, on the other hand, shows low risk compared to Bitcoin. The results further reveal that PAX gold is more volatile than PM gold, while Bitcoin, being a highly traded cryptocurrency, is significantly correlated to other cryptocurrencies. The implications of this research showing the volatility of gold- and fiat-backed cryptocurrencies are equally important to stakeholders, such as investors, and policymakers.