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.
This paper features an analysis of cryptocurrencies and the impact of the COVID-19 pandemic on their effectiveness as a portfolio diversification tool and explores the correlations between the continuously compounded returns on Bitcoin, Ethereum and the S&P500 Index using a variety of parametric and non-parametric techniques. These methods include linear standard metrics such as the application of ordinary least squares regression (OLS) and the Pearson, Spearman and Kendall’s tau measures of association. In addition, non-linear, non-parametric measures such as the Generalised Measure of Correlation (GMC) and non-parametric copula estimates are applied. The results across this range of measures are consistent. The metrics suggest that, whilst the shock of the COVID-19 pandemic does not appear to have increased the correlations between the cryptocurrency series, it appears to have increased the correlations between the returns on cryptocurrencies and those on the S&P500 Index. This suggests that investments in cryptocurrencies are not likely to offer key diversification strategies in times of crisis, on the basis of evidence provided by this crisis.
Aamir Aijaz Syed, Farhan Ahmed, Muhammad Abdul Kamal, Assad Ullah · 5 authors
The environmental degradation and the concern for sustainable development have garnered extensive attention from researchers to evaluate the prospects of green bonds over other traditional assets. Against this backdrop, the current study measures the asymmetric relationship between green bonds, U.S. economic policy uncertainty (EPU), and bitcoins by employing the Nonlinear Autoregressive Distribution Lag (NARDL) estimation technique recently developed by Shin et al. The outcome of the empirical analysis confirms an asymmetric cointegration between EPU, bitcoins, the clean energy index, oil prices, and green bonds. The NARDL estimation substantiates that positive shock in EPU exerts a negative impact on green bonds, whereas a negative shock in EPU increases the performance of green bonds. It implies, in the long run, a 1 percent increase (decrease) in EPU decreases (increases) the performance of green bonds by 0.22 percent and 0.11 percent, respectively. Likewise, the study also confirms a bidirectional relationship between bitcoins and green bonds. A positive shock in bitcoin increases the performance of green bonds and vice versa. In addition, our study also reveals a direct co-movement between clean energy, oil prices, and green bonds. This outcome implies that green bonds are not a different asset class, and they mirror the performance of other asset classes, such as clean energy, oil prices, and bitcoins. The findings offer several implications to understand the hedging and diversification properties of bitcoins, and assist in understanding the role of U.S. economic policy uncertainty on green bonds.
In this paper, we build an empirical specification that helps to explain bitcoin volatility and to characterize phases of the bitcoin bubble using information derived from investors’ emotions and sentiment that captures investment intentions and investors’ aversion to risk. To this end, we investigated the bilateral relations between bitcoin volatility and investor emotions between 2018 and 2021, a period characterized by significant changes in bitcoin prices as well as wide disparities in investor emotions, especially in the context of the ongoing COVID-19 pandemic. The study was based on a linear and nonlinear Vector Autoregressive (VAR) model that we applied to data related to bitcoin prices and market sentiment as expressed by the Fear and Greed index. Overall, our results evince the key role played by collective emotions in the formation and collapse of the bitcoin bubble. Two findings in particular stand out. First, our model shows significant time-varying lead-lag effects between bitcoin volatility and investor sentiment that come into play bilaterally and help to characterize the dynamics of bitcoin volatility. Second, these interactions exhibit asymmetry and nonlinearity as the sign and size of collective emotions (resp. bitcoin volatility) vary with the regime and market state under consideration (calm state versus period of bubble formation, etc.). In other words, the power of sentiment has a time-varying effect on the market. Indeed, in the first regime (“calm state”), where bitcoin volatility is relatively low and the market shows evidence of stability, collective emotions have a negative impact on bitcoin volatility, prompting a stabilizing strength. However, in the second regime (“bubble formation”), the effect of emotions turns significantly positive as investors gradually become less fearful and more reassured, which can simultaneously increase volatility and destabilize the market. Finally, in the third regime (“bubble collapse”), when bitcoin reaches a high level of value and experiences impressive volatility excess , the effect of emotions again turns negative, resulting in further switching behavior that pushes investor action to provoke a bitcoin price correction, moving it toward a new state of stability. Our conclusion helps improve predictions of bitcoin price dynamics informed by the information provided by investor emotions.
The popularity of cryptocurrency in recent years has gained a lot of attention among researchers and in academic working areas. The uncontrollable and untraceable nature of cryptocurrency offers a lot of attractions to the people in this domain. The nature of the financial market is non-linear and disordered, which makes the prediction of exchange rates a challenging and difficult task. Predicting the price of cryptocurrency is based on the previous price inflations in research. Various machine learning algorithms have been applied to predict the digital coins' exchange rate, but in this study, we present the exchange rate of cryptocurrency based on applying the machine learning XGBoost algorithm and blockchain framework for the security and transparency of the proposed system. In this system, data mining techniques are applied for qualified data analysis. The applied machine learning algorithm is XGBoost, which performs the highest prediction output, after accuracy measurement performance. The prediction process is designed by using various filters and coefficient weights. The cross-validation method was applied for the phase of training to improve the performance of the system.
The current study has examined the informational efficiency of market leader of cryptocurrency i.e, Bitcoin. The daily, weekly and monthly prices of Bitcoin have been used for analysis from 2013 to 2017. The information efficiency has been investigated by using different tests of random walk both parametric and non-parametric. The results indicate the Bitcoin returns are not weak form efficient and the element of random walk is not there. Hence, the investors have an opportunity to beat the market by using technical trading and get abnormal returns from the predictability of Bitcoin prices.
Bu çalışmanın amacı COVID-19 pandemi döneminde kripto para fiyatlarında balon oluşup oluşmadığının araştırılmasıdır. Bu amaçla piyasa değeri en yüksek 3 kripto para olan Bitcoin (BTC), Ethereum (ETH) ve Binance Coin (BNB) fiyatlarına ilişkin, 10/03/2020-06/07/2021 tarihlerini kapsayan veri seti GSADF testiyle analiz edilmiştir. Yapılan analizler sonucunda incelenen her üç kripto paranın da fiyatlarında balon olduğu tespit edilmiştir. Buna ek olarak verileri analiz edilen kripto paralarda tespit edilen fiyat balonlarının dönemlerinin benzer olması, balon tespit edilen dönemlerde piyasanın tamamını etkileyen fiyat hareketleri olduğu yönünde güçlü kanıtlar sunmuştur. Çalışmanın düzenleyici otoriteler ve yatırımcılar açısından önemli sonuçlar ortaya koyduğu düşünülmektedir. COVID-19 pandemisi ya da piyasalar üzerinde benzer etkiler yapabilecek finansal kriz ortamlarında yatırımcılar oluşabilecek fiyat balonlarına dikkat etmeli ve yatırım kararlarında bu durumu göz önünde bulundurmalıdır. Son olarak finansal piyasaları düzenleyici taraflar söz konusu dönemlerde yatırımcıları oluşabilecek olumsuz durumlardan korumak adına gerekli adımları atmalıdırlar.
This paper explores price effects caused by the expiration of derivatives in the cryptocurrency market. Applying different statistical tests (ANOVA, Mann–Whitney, and t-tests) and econometric methods (the modified cumulative abnormal return approach, regression analysis with dummy variables, and the trading simulation approach) to daily and weekly Bitcoin data over the period 2018–2021, the following hypotheses are tested: (H1) Expiration days create patterns in price behavior in the cryptocurrency market; and (H2) Price patterns can be exploited to generate abnormal profits from trading. The results suggest that expiration effects are only nominally present in the cryptocurrency market. There are differences in returns between expiration-related periods and average returns, but these differences are statistically insignificant. The only case in which an anomaly was detected was related to abnormally high returns during the week of expiration: returns during such weeks were positive in 65% of cases, and were on average 5 times higher than during usual weeks. Trading strategies based on this fact were able to generate results different from those of random trading, with a Sharpe ratio above 1. This is evidence in favor of the existence of a real price anomaly, which contradicts the efficient market hypothesis, and this could be implemented in the practice of traders and investors by creating trading strategies based on detected price effects or special technical analysis indicators to generate trading signals. For academics, these results might provide an opportunity to improve time series forecasting analysis in the case of Bitcoin.
Cryptocurrencies show tremendous growth by market capitalization, however Bitcoin cross-country holdings are still in question. The purpose of the paper is to show that inflation discontent with the rule of law failures can explain why residents of different countries are prone to cryptocurrency holdings. The level of financial development is also considered. A hypothesis is proposed for more complex and segmented motives of Bitcoin holdings, tested by the OLS method. Single- and multi-factor regressions with independent variables are used, which can validate cross-country Bitcoin holdings in terms of inflation discontent, quality of institutions and financial development. Regression results confirm the idea of more segmented motives to hold Bitcoins. First, the hedge against inflation motive is rooted in the institutional weakness of central banks, and the regression results show that inflation variables are the most significant. Second, the hedge against institutional risks of asset ownership motive, based on the lack of rule of law and the relevant variable, is best performing among other institutional variables. Third, it is wrong to neglect financial development. However, it only plays a role in interaction with better innovation performance, meaning that crypto investors try not only to diversify their portfolios, but also to profit from involving in a sector with promising technological perspectives. The main takeaway is that institutional factors help explain why people in countries with worsened inflation and institutional performance tend to hold a large fraction of Bitcoins in assets. Obviously, monetary and institutional fragility is underestimated in the general discussion about the nature of digital money.
In this chapter structures that generate yield in cryptofinance will be analyzed and related to leverage. While the majority of crypto-assets do not have intrinsic yields in and of themselves, similar to cash holdings of fiat currency, revolutionary innovation based on smart contracts, which enable decentralised finance, does generate return. Examples include lending or providing liquidity to an automated market maker on a decentralised exchange, as well as performing block formation in a proof of stake blockchain. On centralised exchanges, perpetual and finite duration futures can trade at a premium or discount to the spot market for extended periods with one side of the transaction earning a yield. Disparities in yield exist between products and venues as a result of market segmentation and risk profile differences. Cryptofinance was initially shunned by legacy finance and developed independently. This led to curious and imaginative adaptions, reminiscent of Darwin's finches, including stable coins for dollar transfers, perpetuals for leverage, and a new class of exchanges for trading and investment.
The globally acknowledged accelerating crypto hype has put a lot of work on researchers and analysts. This new marvel of digital currencies requires a lot of attention, for it to become conventional worldwide. Virtual coins or cryptographic forms of money utilize encryption framework, so called the Block Chain technology, that manage the formation and supply of coins and exchanges must be recognized from a financial examination point of view. Subsequently, it is essential to inspect which social, money related & macroeconomic components decide its cost with a specific end goal to know the degree and outcomes of the economy. This paper aims to study different internal and external factors that affect cryptocurrencies’ prices. A sample of four digital coins with largest market capitalization has been selected. Daily price data from the years 2015 to 2020 of Bitcoin with other altcoins such as Ethereum, Ripple and Litecoin has been taken. Internal factors consist of demand and supply variables and also the attractiveness associated with its increasing hype. Other factors include KSE-100 Index (Karachi Stock Exchange), USD-PKR (Dollar to Pakistani Rupee) exchange rate and oil prices from PSO (Pakistan State Oil). ARDL analysis has been done to study the effect of these factors on the prices of crypto coins. Our analysis shows that circulating supply has a significant effect on Ethereum and Ripple prices in the long run. Attractiveness has been significant on the prices of Ethereum only.
Zeyd Boukhers, Azeddine Bouabdallah, Cong Yang, Jan Jürjens
Since Bitcoin first appeared on the scene in 2009, cryptocurrencies have become a worldwide phenomenon as important decentralized financial assets. Their decentralized nature, however, leads to notable volatility against traditional fiat currencies, making the task of accurately forecasting the crypto-fiat exchange rate complex. In this study, we examine the various independent factors that affect the Bitcoin-Dollar exchange rate's volatility. To this end, we propose CoMForE, a multimodal AdaBoost-LSTM ensemble model, which not only utilizes historical trading data but also incorporates public sentiments from related tweets, public interest demonstrated by search volumes, and blockchain hash-rate data. Our developed model goes a step further by predicting fluctuations in the overall cryptocurrency value distribution, thus increasing its value for investment decision-making. We have subjected this method to extensive testing via comprehensive experiments, thereby validating the importance of multimodal combination over exclusive reliance on trading data. Further experiments show that our method significantly surpasses existing forecasting tools and methodologies, demonstrating a 19.29% improvement. This result underscores the influence of external independent factors on cryptocurrency volatility.
Robo-advisor is one of the most prominent innovation in the wealth management industry, and its success in Indonesia has been evident in the case of Bibit. Therefore, wealth management companies need to employ Robo-Advisor to overcome their competition. This research aims to give recommendation on asset allocation method and asset class selection for Robo-Advisors in Indonesia using Sharpe Ratio Analysis. Then, the author will analyze the robo-advisor’s performance during equity market downturn. Finally, The Robo-Advisor’s actual performance will be tested in 2018, 2019, and 2020. The Sharpe ratio analysis result showed that Robo-Advisors seeking higher risk-adjusted return should choose mean-variance optimization over risk parity for asset allocation method, and the inclusion of gold and bitcoin in a portfolio of stock mutual fund and bond mutual fund increases the risk-adjusted return of the portfolio. The proposed robo-advisor’s portfolio protected investors from equity market downturn in 2011-2010 in 83,3% of the case. Finally, the proposed robo-advisor’s portfolio generated better return for the conservative, moderate and aggressive investor during 2018, 2019, and 2020 when compared to LQ45.
Kripto paralar artan dijitalleşme ve merkeziyetsiz finans düşüncesinin bir ürünü olarak ortaya çıkmış ve yeni projelerle birlikte büyümeye devam etmektedir. Kripto paralar, alım veya satım gibi her türlü işlemlerde kullanılmasının yanı sıra değişim aracı ve yatırım aracı olarak da kullanılabilmektedir. Ayrıca madencilik yoluyla söz konusu para birimi üretimi yapılabilmektedir. Kripto para denilince ilk olarak ortaya çıkan ve tüm kripto paraların öncüsü olarak kabul edilen Bitcoin, piyasa hacmi açısından da en yüksek kripto paradır. Bu bağlamda çalışmamızın amacı, piyasanın öncül parası olan Bitcoin ve kısaca “Altcoin” diye ifade ettiğimiz diğer kripto paralar arasındaki ilişkiyi incelemektir. İlişkiyi incelemek için zaman serisi analiz yöntemi kullanılarak 01.01.2018-31.12.2020 dönemi günlük veriler Trandingview ve Coinmarketcup aracılığıyla toplanmış ve Johansen eşbütünleşme testi, Vektör Hata Düzeltme Modeli (VECM) ve Granger nedensellik testi yapılmıştır. Johansen eşbütünleşme sonucuna göre ele alınan dönemlerde kullanılan değişkenler arasında uzun dönemli bir ilişki bulunmaktadır. Granger nedensellik sonucuna göre ise Cardona'dan Bitcoin'e, Bitcoin'den Etheryum'a ve Cardano'dan Binance Coin' doğru tek yönlü nedensellik tespit edilmiştir.
We employed linear and nonlinear error correction models (ECMs) to predict the log returns of Bitcoin (BTC). The linear ECM is the best model for predicting BTC compared to the neural network and autoregressive models in terms of RMSE, MAE, and MAPE. Using a linear ECM, we are able to understand how BTC is affected by other coins. In addition, we performed Granger-causality tests on fourteen cryptocurrencies.
This study attempts to answer the question: “Is the Cryptocurrency Policy Uncertainty (UCRY Policy) a determinant of the Bitcoin’s price (BTC)?”. Besides, this study uses these factors as explanatory variables for the BTC movements alongside the UCRY Policy and control variables such as the velocity of the Bitcoin in circulation (BC), the computational power of Bitcoin (HR), popularity (PO), and exchange rate (EX). In the study, December 30, 2013- February 21, 2021, was determined as the term and weekly data were investigated. The ARDL bounds testing method was used to determine the relationship between the variables. According to empirical findings, this study suggests that the UCRY Policy is essential to the BTC. There is a negative relationship between UCRY Policy and BTC. When UCRY Policy increases, BTC decreases, holding other variables constant. Besides, this study shows that control variables can be used as determinants of BTC. In long run, BC and HR have a significant, positive relationship with BTC. The EX has a significant, negative relationship with BTC. The PO has a significant, positive relationship with BTC in the short run. In addition, this study demonstrates that UCRY Policy can be used as a type of uncertainty index for Bitcoin.
This paper proposes a novel asymmetric jump model for modeling interactions in discontinuous movements in asset prices. Given the jump behavior and high volatility levels in cryptocurrency markets, we apply our model to cryptocurrencies to study the impact of various types of jumps occurring in one cryptocurrency’s price process on the discontinuity component of the realized volatility of other cryptocurrencies. Our model also allows us to assess the impact of co-jumps. Using high-frequency data to compute the daily realized volatility, we show that downside, upside, and small jumps observed in cryptocurrencies negatively affect the jump component of other cryptocurrencies’ realized volatility, while large jumps have the opposite effect. We further find significant asymmetric effects between small and large as well as between downside and upside jumps for several cryptocurrencies. Moreover, we find evidence of co-jumping behavior, which can trigger future jumps. The practical implications of our findings are also discussed. Finally, we extend our analysis to study the effects of jumps in mainstream financial assets on cryptocurrencies’ jump behavior and find that upside and downside jumps observed in the S&P 500 index negatively impact cryptocurrency jumps.
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 methods—namely, value-at-risk (VaR) and conditional value-at-risk (CVaR)—are 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.
In the past few years, along with the crypto assets market, a new term has appeared: stablecoins. Unlike cryptocurrencies, however, not so much research has been devoted to this topic. The emergence of global stablecoin projects, a significant increase in the volume of investment initiatives, and growth in the number of transactions have forced central banks to seriously pay attention to these in order to ensure financial stability as one of their functions. This topic is undoubtedly relevant due to the novelty of the concept which has appeared. The purpose of this article is to study the economic essence of stablecoins, their types, and the current state of this market. The methods of comparative analysis as well as critical and systematic approach to the study of information are used in the work. Existing ways to define the concept of stablecoins are investigated. The classifications of stablecoins and the main types of the most reliable coins on the market are examined. The current state of the stablecoin market is analyzed. As a result of the study, a number of conclusions have been made. Despite the lack of a legally fixed and generally accepted definition of stablecoins, in general, stablecoins are tokens secured by different types of assets. The economic essence of stablecoins is revealed through the goals of their creation, types of security and stabilization mechanisms, as well as the nature of the relationship between the issuer and the owner of the stablecoin. Over the past three years, the stablecoin market has grown almost fivefold. Such growth means significant penetration into the payment system, and then into the global financial system, which requires the development of international regulatory standards to minimize possible risks and preserve financial stability. The prospects for the development of stablecoins are associated with the creation and promotion of digital currencies of central banks (central securities) and cross-border payments in one or more central securities.