In this study, the RiskMetrics method is used to estimate Value at Risk for two exchange rates: BitCoin/dollar and the South African Rand/dollar. Value at Risk is used to compare the riskiness of the two currencies. This is to help South Africans and investors understand the risk they are taking by converting their savings/investments to BitCoin instead of the South African currency, the Rand. The Maximum Likelihood Estimation method is used to estimate the parameters of the models. Seven statistical error distributions, namely Normal Distribution, skewed Normal Distribution, Student’s T-Distribution, skewed Student’s T-Distribution, Generalized Error Distribution, skewed Generalized Error Distribution, and the Generalized Hyperbolic Distributions, were considered when modelling and estimating model parameters. Value at Risk estimates suggest that the BitCoin/dollar return averaging 0.035 and 0.055 per dollar invested at 95% and 99%, respectively, is riskier than the Rand/dollar return averaging 0.012 and 0.019 per dollar invested at 95% and 99%, respectively. Using the Kupiec test, RiskMetrics with Generalized Error Distribution (p > 0.07) and skewed Generalized Error Distribution (p > 0.62) gave the best fitting model in the estimation of Value at Risk for BitCoin/dollar and Rand/dollar, respectively. The RiskMetrics approach seems to perform better at higher than lower confidence levels, as evidenced by higher p-values from backtesting using the Kupiec test at 99% than at 95% levels of significance. These findings are also helpful for risk managers in estimating adequate risk-based capital requirements for the two currencies.
Danai Likitratcharoen, Pan Chudasring, Chakrin Pinmanee, Karawan Wiwattanalamphong
In recent years, the cryptocurrency market has been experiencing extreme market stress due to unexpected extreme events such as the COVID-19 pandemic, the Russia and Ukraine war, monetary policy uncertainty, and a collapse in the speculative bubble of the cryptocurrencies market. These events cause cryptocurrencies to exhibit higher market risk. As a result, a risk model can lose its accuracy according to the rapid changes in risk levels. Value-at-risk (VaR) is a widely used risk measurement tool that can be applied to various types of assets. In this study, the efficacy of three value-at-risk (VaR) models—namely, Historical Simulation VaR, Delta Normal VaR, and Monte Carlo Simulation VaR—in predicting market stress in the cryptocurrency market was examined. The sample consisted of popular cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Cardano (ADA), and Ripple (XRP). Backtesting was performed using Kupiec’s POF test, Kupiec’s TUFF test, Independence test, and Christoffersen’s Interval Forecast test. The results indicate that the Historical Simulation VaR model was the most appropriate model for the cryptocurrency market, as it demonstrated the lowest rejections. Conversely, the Delta Normal VaR and Monte Carlo Simulation VaR models consistently overestimated risk at confidence levels of 95% and 90%, respectively. Despite these results, both models were found to exhibit comparable robustness to the Historical Simulation VaR model.
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.
Abstract For this paper, the relationship between seventeen popular cryptocurrencies was analyzed by multivariate Granger causality tests and simple linear regression, using data spanning the period 1 September 2020 to 8 December 2021. The novelty of this work is that it studies the effects of sampling interval and sample size in cryptocurrency markets, which can yield significantly different results. Minute-by-minute, hourly and daily data were collected to examine the Granger causality relationship between cryptocurrencies. It was found that all the currencies demonstrated a significant causality relationship when high frequency (such as minute-by-minute) data was used, in contrast to hourly and daily data. The bigger the sample size, the higher the probability of rejecting the null hypothesis. Hence, the null hypothesis for the Granger causality test can be rejected for minute-by-minute time series data because of too large a sample size. Granger causality test results for hourly and daily data indicated that Bitcoin, Ethereum Classic, and Neo were leading indicators among the cryptocurrencies included in the research. In addition, according to simple linear regression analysis, the short term marginal effect of Bitcoin plays an important role by creating significant impacts on other cryptocurrencies.
Investing in the unregulated cryptocurrencies has showed a rising tendency since 2020, despite uncertainty surrounding its future in India. There is no ban on the use of cryptocurrencies in India, nor is there any regulation governing their actual use. This uncertainty is disturbing Indian startups developing blockchain-based products. But this hasn't altered investors' positions in investing in cryptocurrencies; they continue to be positive. For longer-term investments, investors have historically employed fundamental analysis. By analyzing the underlying company's operations and the state of its industry or the overall economy, fundamental analysis seeks to find stocks with high growth potential at fair prices. The issue with conducting a fundamental analysis of cryptocurrencies is that they cannot be evaluated using the same criteria as conventional businesses. Therefore, the authors must focus on several frameworks. Finding good metrics is the first step in that approach. This chapter examines the emergence of cryptocurrency in India and its implications on investments.
Cryptocurrency is a digital currency which works as a medium of exchange through a computer network. This study aims at modelling the volatility of selected cryptocurrencies by adopting different models of the GARCH family and providing empirical evidence on the fit of conditional volatility. The research is based upon daily U.S. dollar price indexes of Bitcoin (BTC/USD). The data were collected for a time period from October 2021 to April 2022. The every-day price data was further drilled down to arrive at OHLC (open-high-low-close) price for every quarter of the day. The dataset for the analyses were compiled from the website https://cryptowat.ch/ which is an open source and offers free downloadable dataset using Power BI. The first part of the article will focus on the introduction of topic matter concerned. The second part covers around a few literature reviews in the light of volatility in cryptos. The third part will focus on the methodology adopted followed by the results and discussion in the fourth part. Finally, the fifth and last part will provide concluding remarks.
This chapter scrutinizes the dynamic linkages between major cryptocurrencies and fiat currencies of developed and emerging countries. To do this, the authors estimate the Scalar-BEKK GARCH models from September 2017 to January 2022. To shed light on the effects of specific events, the authors also estimate the models for the sub-periods: the great crypto crash, the Covid-19 pandemic, and the vaccination. Empirical results suggest that the time-varying relationships between the crypto- and fiat currencies highly depend on the country- and crypto-specific dynamics. By the decentralized nature of cryptocurrencies, it is not an easy venture to define the stylized facts on those dynamic relationships. The most striking result shows a sharp and massive decline in the conditional covariances between the cryptos and the fiat currencies of developed countries except the Japanese Yen at the onset of the Covid-19 pandemic.
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.
These days, virtual currencies-the term used to describe cryptocurrencies-are more well-known and have aroused the interest of numerous people. As a result of using blockchain technology, which decentralizes banking, daily cryptocurrency trading has become quite popular among observers, investors, customers, and many other groups. However, due to the daily fluctuations in the value of cryptocurrencies, prediction techniques enable stakeholders to look into the future and identify potential threats to their crucial investment operations. The increasing popularity of cryptocurrencies has made price predictions more promising for investors along with researchers. As artificial intelligence (AI) has advanced to such a level, forecasting the price of cryptocurrencies has grown in significance. In this study, we develop an approach based on machine learning and deep learning as a form of AI to forecast the price of various cryptocurrency exchanges, such as Bitcoin (BTC), Ethereum (ETH), BinanceCoin (BNB), and FTX (FTT), based on their various pricing points. The results from the machine learning models demonstrated that linear regression performed better in forecasting all types of cryptocurrency, with a maximum R2 score of 0.9477, 0.9232, 0.9204, and 0.8925 for BTC, ETH, BNB, and FTT, respectively. However, our study found that the Gated Recurrent Unit (GRU), a deep learning-based model, was the most effective algorithm for predicting the prices of all four types of cryptocurrencies. With GRU, we were able to predict the price of ETH with an R2 score as high as 0.9983, and we were also able to predict the prices of BTC, BNB, and FTT with R2 scores of 0.9969, 0.9772, and 0.9873, respectively. The proposed approach demonstrated superior results with minimal prediction errors on estimating the price of all the different crypto exchanges when the outcomes of our research were dealt with those of the existing studies.
This paper discusses whether the Bitcoin exchange-traded fund (ETF), which tracks the value of Bitcoin, improves equity portfolios, by using a robust portfolio performance analysis. The equity portfolio is represented by an ETF that tracks the Standard & Poor’s 500. We use data from a turbulent investment period within the coronavirus pandemic, to study the diversification benefits of Bitcoin. We compare the performances of diverse portfolios composed of both ETFs, which include 40 classical dynamic volatility model-based portfolios and 900 score-driven portfolios. For the score-driven portfolios, the dynamic association is modelled by score-driven Clayton, rotated Clayton, Gumbel, rotated Gumbel and Student’s t copulas. We compare portfolio strategies using the model confidence set test. We find that score-driven portfolios outperform classical volatility model-based portfolios and the equity portfolio. Our results may provide suggestions for cryptocurrency investors on portfolio optimization and may also have policy implications for regulators and policymakers.
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 study conducted the econometric analysis to test the hedge and safe haven effects of Non-fungible Tokens (NFTs) on major traditional asset markets in the global financial system. We investigate the estimates of these effects in times of extreme market conditions and the COVID-19 crisis. Our empirical results show evidence of the hedge and safe haven properties of NFTs, confirming two main findings: (i) NFTs act as a hedge and safe haven for particular stock markets and oil, bond, and USD indices, even though the degree of effects varies across asset classes; and (ii) NFTs also serve as sheltering facilities for the markets mentioned above, with more substantial safe haven benefits for bond and USD indices during the recent pandemic crisis.