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 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.
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.
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 $5,000. The anomaly is detected in the data starting from mid-to-late 2020, is potentially associated with algorithmic trading relying on the arrival of 15-min candle information, and its discovery contributes significantly to the understanding of cryptocurrency adaptive market efficiency.
In this study, we investigate the BTC price time-series (17 August 2010-27 June 2021) and show that the 2017 pricing episode is not unique. We describe at least ten new events, which occurred since 2010-2011 and span more than five orders of price magnitudes ($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.
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.
The price of cryptocurrencies is predicted in this paper based on their intrinsic interrelationship with Bitcoin. The Kaggle dataset is gathered, standardized, collated, and extracted. Convolutional Neural Network (CNN) is compared to other machine learning methods such as Linear Regression and K-Nearest Neighbor (KNN), and then parameter optimization is performed. The empirical results show that Linear Regression is less accurate than the other two models, whereas the CNN model employing end-to-end solutions outperforms other models with the best accuracy (overall above 0.95) forecasting the price quantitatively and directly of the majority of cryptocurrencies, despite the fact that forecasting takes a long time and tweaking its parameters is extremely time-consuming. This paper proposes using research object interrelationships rather than extrinsic relationships.
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.
Unlike traditional currencies that rely on centralized such as banks or governments, cryptocurrencies today have become popular due to its decentralized transactions. Decentralization takes advantage of no requirement for intermediaries, thus reducing transaction fees and processing time. However, investing in cryptocurrencies incurs risks and uncertainties due to price volatility and rapid changes. The fact that prediction of asset prices is complex due to the influence of multiple factors on price movements. This paper studied the technical factor to analyze the short-term returns of Ethereum in the periods of 1-10 days. The historical data containing Ethereum closing price are collected from CoinGecko. The twenty-two indicators are chosen from Momentum, Volatility, and Sentiment factors as candidates to provide valuable insights in market trends. The values of these indicators are calculated based on past Ethereum closing prices and then used for XGBoost learning to discover patterns in previous trading. The model performance is evaluated using the multi-class AUC-ROC metric, which measures the accuracy of predicting three types of Ethereum returns: Downtrend, Sideway, and Uptrend. The experimental results reported that the models achieved the values of micro-average ROC curve ranging from 0.65 to 0.67. Moreover, the study emphasizes the importance of considering momentum indicators when making investment decisions in Ethereum.
We mine the leaked history of trades on Mt. Gox, the dominant Bitcoin exchange from 2011 to early 2014, in order to detect the triangular arbitrage conducted on the platform. To this end, we exploit user identifiers per trade to identify and describe the individual trading patterns of 440 arbitrageurs. Moreover, we introduce proxies for expertise and document that the expert users' distribution of profits first-order stochastically dominates that of non-expert users. Most importantly, by including user fixed effects, we show that expert users make profits on arbitrage by reacting quickly to plausible exogenous variations on the official exchange rates. A small number of expert arbitrageurs are able to conduct the vast majority of the arbitrage actions and systematically yield higher profits: our results provide empirical evidence that arbitrageurs are few and sophisticated users, characterized by the ability to incorporate information and to quickly react to exogenous shocks within short time scale intervals.
Bitcoin Pricing Kernels (PKs) are estimated using a novel data set from Deribit, the leading Bitcoin options exchange. The PKs, as the ratio between risk-neutral and physical density, dynamically reflect the change in investor preferences. Thus, the PKs improve the understanding of investor expectations and risk premiums in a new asset class. Bootstrap-based confidence bands are estimated in order to validate the results. Investors are heterogeneous in their risk profiles and preferences with respect to volatility and investment horizon. The empirical PKs turn out to be U-shaped for short-dated instruments and W-shaped for long-dated instruments. We find that investors are willing to pay a substantial risk premium to insure themselves against short-term price movements. The risk premium is smaller for longer-dated instruments and their traders are risk averse. The shape of the empirical PKs reveals the existence of a time-varying risk premium. The similarity between the shape of empirical PKs for Bitcoin and other markets that represent aggregate wealth shows that Bitcoin is becoming an established asset class.
The wild swings in Bitcoinâs valuation keep attracting authoritiesâ and policy-makers interest. Thus at present, many researchers are focus on analyzing and forecasting. The existing studies on Bitcoin price prediction are mainly in two ways: (1) study how economic factors, market and investor sentiment indicators influence Bitcoin price; (2) apply machine learning and artificial neural networks to predict the value of Bitcoin. This paper aims to implement a scenario analysis method to generate various hypothetical events and then determine their effects on the value of Bitcoin price. Scenario analysis is normally used to measure financial risk. In this paper, we propose a method that combines scenario analysis with historical data. We further aim to find the correlations among scenarios and examine the relationship between the significant shocks and Bitcoin prices. Our findings suggest that what-if analysis is a good way to measure the risk exposure of Bitcoin. The method can also be used for worse-scenario analysis to check how Bitcoin performs during crisis periods.
Ashutosh Kolte, Avinash Pawar, Jewel Kumar Roy, Imre Vida ¡ 5 authors
Cryptocurrency is the blockchain financial technology used for transactions in financial institutions and exchanges. Bitcoin has attracted much coverage from investors and commentators as it represents the maximum market capitalization on a crypto-currency exchange. The study aims to determine the correlation between the daily log-returns and to understand the tendencies in the cryptocurrency market instability of Bitcoin, Litecoin, XRP, Nxt, Dogecoin, Vertcoin, DigiByte, DASH, Counterparty, and MonaCoin. The correlation among the selected cryptocurrencies exists in the study. The analysis is focused primarily upon reference information from the preserved servers of cryptocurrency websites and finance.yahoo.com. This research assesses regular details on the Logarithmic return of Bitcoin, Litecoin, XRP, Nxt, Dogecoin, Vertcoin, DigiByte, DASH, Counterparty, and MonaCoin for a timeframe spanning from October 01st, 2014, to April 30th, 2020. From 131 cryptocurrencies, we considered only 10 Cryptocurrencies due to the availability of data after October 2014. Where there was insufficient information, there were average results determined from preceding and succeeding data. Findings demonstrate that there is GARCH modelling of cryptocurrencies against Bitcoin. Litecoin, XRP, Nxt, Dogecoin, Vertcoin, DigiByte, DASH, Counterparty, and MonaCoin; variability values throughout the duration had a significant effect on the updates from Bitcoin returns. We believe that it helps create information and resources that are valuable to practitioners and scholars who research and form cryptocurrency markets in the future.