Abstract The paper provides a comparative empirical study of predictability of cryptocurrency returns and prices using econometrically justified robust inference methods. We present robust econometric analysis of predictive regressions incorporating factors, which were suggested by Liu, Y., & Tsyvinski, A. (2018). Risks and returns of cryptocurrency. NBER working paper no. 24877 ; Liu, Y., & Tsyvinski, A. (2021). Risks and returns of cryptocurrency. The Review of Financial Studies , 34 (6), 2689â2727, as useful predictors for cryptocurrency returns, including cryptocurrency momentum, stock market factors, acceptance of Bitcoin, and Google trends measure of investorsâ attention. Due to inherent heterogeneity and dependence properties of returns and other time series in financial and crypto markets, we provide the analysis of the predictive regressions using both heteroskedasticity and autocorrelation consistent (HAC) standard-errors and also the recently developed <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi>t</m:mi> </m:math> t -statistic robust inference approaches, Ibragimov, R., & MĂźller, U. K. (2010). t-statistic based correlation and heterogeneity robust inference. Journal of Business and Economic Statistics , 28 , 453â468; Ibragimov, R., & MĂźller, U. K. (2016). Inference with few heterogeneous clusters. Review of Economics and Statistics , 98 , 83â96. We provide comparisons of robust predictive regression estimates between different cryptocurrencies and their corresponding risk and factor exposures. In general, the number of significant factors decreases as we use more robust t -tests, and the t -statistic robust inference approaches appear to perform better than the t -tests based on HAC standard errors in terms of pointing out interpretable economic conclusions. The results in this paper emphasize the importance of the use of robust inference approaches in the analysis of economic and financial data affected by the problems of heterogeneity and dependence.
The increasing interest in digital currencies, their exceptional price rise, and the continuous discussions about their benefits raise the question: to what extent can it be an alternative to the traditional currencies in the future? It has become a prominent topic of discussion among several investments and market stakeholders seeking to enhance the growth of their wealth. This paper aims to bring answers to the nature of relationships between four major cryptocurrencies (Bitcoin, Ethereum, Litecoin, and Ripple), and the stock market return (S&P500). This study applies the wavelet method to daily data from 1 June 2017 to 15 November 2021, in COVID-19 sanitary crisis time. According to the results, this study shows a positive co-movement in the medium and long run between the four studied cryptocurrencies and S&P500 during different periods, especially in times of uncertainty. These findings have practical implications as they can be used strategically to make optimal investment decisions and build portfolio diversification strategies with the conventional financial market asset.
The purpose of this study is to examine the synchronism between the US capital markets (DJ, S&P 500), the United Kingdom (FTSE 100), Canada (S&P/TSX), Germany (DAX 30), France (CAC 40), Japan (Nikkei 225), Italy (Italy Ds Market and major cryptocurrencies such as Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), and the Crypto 10 index, from February 2018 to November 2021. Based on the findings, we found that BTC and ETH cryptocurrencies drastically reduced their level of integration with their peers over the 2020 worldwide pandemic era, whereas LTC maintained. We also discovered that the Dow Jones, S&P 500, and DAX 30 stock indexes lowered their level of integration when compared to the pre-covid subperiod. For the UK capital market (FTSE 100), Canada (S&P/TSX), Japan (Nikkei 225), France (CAC 40), and Italy (Italy Ds Market) the level of integration increased significantly. These findings support, in part, our research question, that during periods of stress and uncertainty in the global economy capital markets tend towards integration, thus calling into question the hypothesis of efficient portfolio diversification.
Jan 1, 2022¡Proceedings of the International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2022, 17-19 June 2022, Qingdao, China
As a financial asset, bitcoin has attracted the attention of many financial financial advisors and investors. This paper aims to analyze the dynamic correlation between bitcoin and two important financial assets, i.e., crude oil and gold. This paper selects weekly data from January 2014 to April 202
New technologies have a significant role in modern financial markets. The application of new technologies, application and software solutions has enabled financial institutions and individual and institutional investors to use mathematical-statistical and econometric models, which are based on analysis and evaluation of investment portfolios, financial risk assessment and extrapolation, as well as predictability related to cyclical economic trends, which are directly reflected in the investment portfolio. Due to the impact of the financial crisis, and especially due to global negative economic trends caused by the COVID-19 pandemic, alternative forms of financial assets that are directly created by the application of new technologies are becoming increasingly important in the international financial market. These alternative forms of financial assets are presented as cryptocurrencies. Bitcoin is the first cryptocurrency in the global financial market, and Ethereum is the second cryptocurrency in terms of market turnover. In this regard, arises the question: "Do these two leading cryptocurrencies have a significant impact on the modern financial market, as well as on the decision of individual and institutional investors regarding a different structure of the investment portfolio?" Restructuring the investment portfolio by including cryptocurrencies, aims to achieve portfolio diversification in order to better manage market and financial risks. This paper will analyze the impact of cryptocurrencies, their volatility, turnover volume and the possibility of using them as alternative financial assets of an optimal investment portfolio.
In recent years, the digital cryptocurrency market has witnessed rapid development, but its asset allocation function has yet to be verified. In this paper, DCC-GARCH model is used to estimate the dynamic correlation between digital cryptocurrency and current mainstream assets, and the corresponding hedging effect of digital cryptocurrency is studied. Then, three mainstream asset allocation strategies are selected to analyze the risk and return of asset allocation portfolio after adding digital cryptocurrency, which verifies the asset allocation utility of digital cryptocurrency. This study can provide investment basis for investors and has certain theoretical and practical significance.
Guglielmo Maria Caporale, JosĂŠ Javier de Dios Mazariegos, Luis A. GilâAlana
Abstract This paper applies fractional integration and cointegration methods to examine respectively the univariate properties of the four main cryptocurrencies in terms of market capitalization (BTC, ETH, USDT, BNB) and of four US stock market indices (S&P500, NASDAQ, Dow Jones and MSCI for emerging markets) as well as the possible existence of long-run linkages between them. Daily data from 9 November 2017 to 28 June 2022 are used for the analysis. The results provide evidence of market efficiency in the case of the cryptocurrencies but not of the stock market indices considered. The results also indicate that in most cases there are no long-run equilibrium relationships linking the assets in question, which implies that cryptocurrencies can be a useful tool for investors to diversify and hedge when required in the case of the US markets.
This study discovers a statistically and economically significant intraday anomaly on Bitcoin markets. Positive returns of 0.58 bps per minute are disproportionately concentrated at the turn of 15-min candles (in minutes 0, 15, 30, and 45 of each trading hour). Average returns in other trading minutes are negative. The effect is consistent across Bitcoin exchanges, in quantile regression models, and TGARCH-M estimations with heavy tails, and persist in out-of-sample tests. A high-frequency strategy that exploits this "turn-of-the-candle" effect can be net-outperforming with initial investment as low as $5,000. The anomaly is detected in the data starting from mid-to-late 2020, is potentially associated with algorithmic trading relying on the arrival of 15-min candle information, and its discovery contributes significantly to the understanding of cryptocurrency adaptive market efficiency.
M. Schullitsch, M. Striedner, V. Mßhlbacher, D. Silian ¡ 11 authors
This paper gives a short overview of the financial world affected through COVID-19. To be more precise, it is about the changing (negative) interest rates of Europe, the US as well as India. At the same time, the paper discusses the Bitcoin exchange rate as well as the current situation regarding money laundering.
In this study, we investigate the BTC price time-series (17 August 2010-27 June 2021) and show that the 2017 pricing episode is not unique. We describe at least ten new events, which occurred since 2010-2011 and span more than five orders of price magnitudes ($US 1 -$US 60k). We find that those events have a similar duration of approx. 50-100 days. Although we are not able to predict times of a price peak, we however succeed to approximate the BTC price evolution using a function that is similar to a Fibonacci sequence. Finally, we complete a comparison with other types of financial instruments (equities, currencies, gold) which suggests that BTC may be classified as an illiquid asset.
This paper examines an essential methodology to evaluate the influence of the COVID-19 and Russia-Ukraine conflict surprises and conception statements employed for the dynamic conditional correlation between returns and volatilities of energy commodity indices and Bitcoin. To assess analytically the unexpected component of COVID-19 and Russia-Ukraine conflict surprises, we use GARCH-DCC (1,1) model as established by Engle (2002) by incorporating a dummy variable which measures the surprise factor during the period of study from January 04, 2016, to April 04, 2022. The experimental outcomes of this paper suggest significant and considerable dynamic conditional correlation between energy commodities indices and Bitcoin if COVID-19 pandemic and Russia-Ukraine conflict shocks are incorporated in variance assessments. Additionally, these outcomes demonstrate the financialization phenomena of energy commodities indices and Bitcoin. We find that the dynamic conditional correlation between energy commodities indices and Bitcoin start to respond considerably more in the situation of Russia-Ukraine conflict shocks than COVID-19 surprises. Our outcomes contribute and improve to the research in financial and economic impacts of the recent epidemic and war between Russia and Ukraine with offering an experimental impervious that COVID-19 and Russia-Ukraine conflict give a bidirectional spillover effect on energy commodities and cryptocurrencies assets. This investigation has an essential and considerable concern for the officials and legislators and the portfolio risk administrators and executives.
Cryptocurrencies have become a trendy topic recently, primarily due to their disruptive potential and reports of unprecedented returns. In addition, academics increasingly acknowledge the predictive power of Social Media in many fields and, more specifically, for financial markets and economics. In this paper, we leverage the predictive power of Twitter and Reddit sentiment together with Google Trends indexes and volume to forecast the log returns of ten cryptocurrencies. Specifically, we consider $Bitcoin$, $Ethereum$, $Tether$, $Binance Coin$, $Litecoin$, $Enjin Coin$, $Horizen$, $Namecoin$, $Peercoin$, and $Feathercoin$. We evaluate the performance of LASSO-VAR using daily data from January 2018 to January 2022. In a 30 days recursive forecast, we can retrieve the correct direction of the actual series more than 50% of the time. We compare this result with the main benchmarks, and we see a 10% improvement in Mean Directional Accuracy (MDA). The use of sentiment and attention variables as predictors increase significantly the forecast accuracy in terms of MDA but not in terms of Root Mean Squared Errors. We perform a Granger causality test using a post-double LASSO selection for high-dimensional VARs. Results show no "causality" from Social Media sentiment to cryptocurrencies returns
We investigate the dynamic correlation between the Bitcoin price (BTC) and the U.S. economic policy uncertainty index (USEPU) from the perspective of multifractality. Utilizing the multifractal detrended crossâcorrelation analysis (MFâDCCA), we confirm a longârange crossâcorrelation between BTC and USEPU. Moreover, the empirical results of MFâDCCA show that the powerâlaw properties and multifractal characteristics between BTC and USEPU are significant. We further examine the longârange dependency of crossâcorrelation between BTC and USEPU series via the Hurst exponent test and confirm the durable crossâcorrelation. Finally, we introduce another multifractal indicator and examine the extent of multifractality among time series. The empirical results indicate that the BTC series, USEPU series, and the crossâcorrelation of BTCâUSEPU present apparent multifractality, where BTC shows the strongest degree of multifractality.
Abstract Changing patterns of risk aversion may follow a non-linear counter-cyclical process. However, the evidence so far has not considered developing cryptocurrency markets. Given some unique features of cryptocurrencies, it is interesting to distinguish how these assets differ from traditional products. This paper investigates the time effects of periodicity on risk aversion for a selection of major cryptocurrencies compared to major financial assets. Significant periodic time-varying patterns are identified when analysing risk aversion. Further, bilateral and bidirectional Granger causalities are identified within cryptocurrencies, as well as between cryptocurrencies and traditional financial assets. Bitcoin is identified as a leading information transmitter of the spillover of risk aversion upon other cryptocurrencies, while estimated risk aversion of traditional financial markets plays a dominant role in the spillover processes upon the cryptocurrency cluster. The latter finding presents further evidence of developing cryptocurrency market maturity. The COVID-19 pandemic is found to have significantly influenced the connectedness of risk aversion among cryptocurrency and traditional financial markets.
Jan 1, 2022¡Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
The rapid spread of information over social media influences quantitative trading and investments. The growing popularity of speculative trading of highly volatile assets such as cryptocurrencies and meme stocks presents a fresh challenge in the financial realm. Investigating such "bubbles" - periods of sudden anomalous behavior of markets are critical in better understanding investor behavior and market dynamics. However, high volatility coupled with massive volumes of chaotic social media texts, especially for underexplored assets like cryptocoins pose a challenge to existing methods. Taking the first step towards NLP for cryptocoins, we present and publicly release CryptoBubbles, a novel multi-span identification task for bubble detection, and a dataset of more than 400 cryptocoins from 9 exchanges over five years spanning over two million tweets. Further, we develop a set of sequence-to-sequence hyperbolic models suited to this multi-span identification task based on the power-law dynamics of cryptocurrencies and user behavior on social media. We further test the effectiveness of our models under zero-shot settings on a test set of Reddit posts pertaining to 29 "meme stocks'', which see an increase in trade volume due to social media hype. Through quantitative, qualitative, and zero-shot analyses on Reddit and Twitter spanning cryptocoins and meme-stocks, we show the practical applicability of CryptoBubbles and hyperbolic models.