Despite the rise in markets for cryptocurrencies at an outstanding pace, with consistently high trading volume and market capitalization, the increasing volatility of the virtual currencies raise various concerns. One of the major concerns is regarding (in)efficiency, viz. whether there exist opportunities of making excess returns based on out-performing the market or merely a game of chance. In this study, the authors investigate the weak-form efficiency of the top-ten cryptocurrencies using non-parametric and parametric random walk testing methods that are robust to unknown structural breaks and asymmetric effects. The findings do not support the random walk hypothesis, hence validating the weak-form inefficiency for daily cryptocurrencies returns. This can be attributed to the presence of asymmetric volatility clusters. This study has significant implications for portfolio managers, market participants and regulators of leading cryptocurrency markets.
Abstract Investors commonly exhibit the disposition effect—the irrational tendency to sell their winning investments and hold onto their losing ones. While this phenomenon has been observed in many traditional markets, it remains unclear whether it also applies to atypical markets like cryptoassets. This paper investigates the prevalence of the disposition effect in Bitcoin using transactions targeting cryptoasset exchanges as proxies for selling transactions. Our findings suggest that investors in Bitcoin were indeed subject to the disposition effect, with varying intensity. They also show that the disposition effect was not consistently present throughout the observation period. Its prevalence was more evident from the boom and bust year 2017 onwards, as confirmed by various technical indicators. Our study suggests irrational investor behavior is also present in atypical markets like Bitcoin.
This research is to assess cryptocurrencies with the conditional beta,\ncompared with prior studies based on unconditional beta or fixed beta. It is a\nnew approach to building a pricing model for cryptocurrencies. Therefore, we\nexpect that the use of conditional beta will increase the explanatory ability\nof factors in previous pricing models. Besides, this research is also a pioneer\nin placing the uncertainty factor in the cryptocurrency pricing model. Earlier\nstudies on cryptocurrency pricing have ignored this factor. However, it is a\nsignificant factor in the valuation of cryptocurrencies because uncertainty\nleads to investor sentiment and affects prices.\n
Lewis Gudgeon, Sam M. Werner, Daniel Pérez, William J. Knottenbelt
We coin the term Protocols for Loanable Funds (PLFs) to refer to protocols which establish distributed ledger-based markets for loanable funds. PLFs are emerging as one of the main applications within Decentralized Finance (DeFi), and use smart contract code to facilitate the intermediation of loanable funds. In doing so, these protocols allow agents to borrow and save programmatically. Within these protocols, interest rate mechanisms seek to equilibrate the supply and demand for funds. In this paper, we review the methodologies used to set interest rates on three prominent DeFi PLFs, namely Compound, Aave and dYdX. We provide an empirical examination of how these interest rate rules have behaved since their inception in response to differing degrees of liquidity. We then investigate the market efficiency and inter-connectedness between multiple protocols, examining first whether Uncovered Interest Parity holds within a particular protocol and second whether the interest rates for a particular token market show dependence across protocols, developing a Vector Error Correction Model for the dynamics.
This paper provides a comprehensive state-of-the-art investigation of the recent advances in data science in emerging economic applications. The analysis is performed on the novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a broad and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, is used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which outperform other learning algorithms. It is further expected that the trends will converge toward the evolution of sophisticated hybrid deep learning models.
Trading cryptocurrencies (digital currencies) are currently performed by applying methods similar to what is applied to the stock market or commodities; however, these algorithms are not necessarily well-suited for predicting cryptocurrency prices. Unlike stock exchanges, which shut down for several hours or days at a time, digital currency prediction and trading seem to be of a more consistent and predictable nature. In this work, we benefit from sentiment analysis of tweets using both an existing sentiment analysis package and a manually tailored “objective analysis,” to calculate one impact value for each analysis every 15[Formula: see text]min. We then select the most appropriate training method by applying evolutionary techniques and discover the best subset of the generated features to include, as well as other parameters. One of the unique contributions of this work is the analysis of both English and Japanese tweets with a tailored “objective analysis” tool. This resulted in implementation of predictors which yielded 28% to 122% profit in a four-week simulation, much more than simply holding a digital currency for the same period of time.
Using intraday data, this study employs the VAR-DCC-GARCH model to examine return and volatility transmission among Bitcoin, Ethereum, and Litecoin during the pre-COVID-19 and COVID-19 periods. We find that the return spillovers differ across both periods for the Bitcoin-Ethereum, Bitcoin-Litecoin, and Ethereum-Litecoin pairs. The volatility transmission is not significant between cryptocurrencies during the pre-COVID-19 period. We also find that the volatility spillover is unidirectional from Bitcoin to Ethereum and bidirectional between Ethereum and Litecoin during the COVID-19 period. Moreover, volatility transmission is not significant between Bitcoin and Litecoin during the COVID-19 period. The dynamic conditional correlations between all pairs of cryptocurrencies are higher during the COVID-19 period than during the pre-COVID-19 period. Lastly, we compute the optimal portfolio weights, time-varying hedge ratios, and hedging effectiveness for all pairs of cryptocurrencies during the pre-COVID-19 and COVID-19 periods. Overall, our findings provide new insights into channels of information transmission, which may improve the investment decisions and trading strategies of portfolio investors during crisis and non-crisis periods.
Abstract This study examines the influences of different types of uncertainty, namely, World Uncertainty (WUI), Global Economic Policy Uncertainty (GEPU), and Geopolitical Uncertainty (GUI) on the returns and liquidity of 964 cryptocurrencies over the period from April 28, 2013 to July 14, 2018. Besides the full sample, three sub‐portfolios are separated by market capitalization. The principal findings are: (i) increased GEPU has significantly negative effect on the cryptocurrency portfolios' returns; (ii) higher WUI has significantly negative impact on the cryptocurrency portfolios' liquidity, especially for the medium sub‐portfolio; (iii) the effects of uncertainty proxied by GEPU and WUI on cryptocurrency portfolios' returns and liquidity, respectively, are asymmetric; and (iv) GUI is not found to have any significant impact on the returns and liquidity of cryptocurrency portfolios.
Abstract This study examines the informational efficiency of the bitcoin spot market by evaluating the predictive power of mechanical trading rules designed to exploit price continuation. Significant return predictability is found until the introduction of bitcoin futures in December 2017. The forecasting ability of trend‐chasing trading rules declines dramatically afterwards. Although evidence suggests that the introduction of bitcoin futures has increased the informational efficiency of the bitcoin spot market, no signs of improvement in informational efficiency are found in ethereum, the second‐largest cryptocurrency—following the introduction of bitcoin futures.
Steffen Günther, Christian Fieberg, Thorsten Poddig
<abstract xml:lang="eng"> Summary: We analyze the cross-section of more than 1200 cryptocurrencies derived from 350 exchanges in the time period from January 2014 to June 2020. Specifically, we investigate whether well-known cross-sectional characteristics like beta (Fama/MacBeth (1973)), size (Banz (1981)) or momentum (Jegadeesh/Titman (1993)) – which have been intensively investigated in the equities literature – explain the cross-section of cryptocurrency returns. We apply the monotonic relationship (Mr.) test developed by Patton and Timmermann (2010) to test for dependencies between characteristics and average portfolio returns and standard deviations. We extend the existing literature on cryptocurrencies showing that there are various characteristics which are able to explain cryptocurrency risk and return. Zusammenfassung: Wir untersuchen den Querschnitt von über 1200 Kryptowährungen, gesammelt von 350 Handelsplätzen, in der Zeitspanne von Januar 2014 bis Juni 2020. Im speziellen untersuchen wir, ob weit verbreitete Charakteristika, wie Beta (Fama/MacBeth (1973)), Size (Banz (1981)) oder Momentum (Jegadeesh/Titman (1993)) – die bereits intensiv in der Aktienliteratur untersucht werden – den Querschnitt der Kryptowährungsrenditen erklären können. Wir verwenden den Monotonic Relationship (MR) Test von Patton und Timmermann (2010) um auf Abhängigkeiten zwischen Charakteristika und durchschnittlichen Portfoliorenditen sowie Standardabweichungen zu testen. Wir erweitern die bestehende Literatur, indem wir zahlreiche Charakteristika identifizieren, die Risiko und Renditen von Kryptowährungen erklären können.
Purpose In this paper, our aim is to estimate the time varying correlations between Bitcoin, VIX futures and CDS indexes and to examine in what ways these assets can act as beneficial hedge and safe haven mechanisms, useful for facing, or attenuating, the major world equity markets related risks and volatilities. Design/methodology/approach Our methodology consists to model each pair equity/asset indices by bivariate symmetric and asymmetric dynamic conditional models (A) DCC to evaluate the portfolio design associated implications on both daily and weekly collected data base, with regard to the period ranging from July, 2010 to January 2018. To assess the extent to which the Bitcoin, VIX futures and sovereign CDS may stand as diversifiers, i.e. as hedging or safe haven instruments against the various stock indexes, we adopt the same method applied by Baur and Lucey (2010). Findings Empirical results show that the hedging and safe haven roles associated with the three hedging instruments tend to differ noticeably across time horizons and model used. The interest brought about by treating this issue is twofold. On the one hand, it should provide useful guidelines to investors through helping them opt for the most effective and beneficial strategies, whereby they could efficiently hedge the equity markets related extreme risks and volatilities. On the other hand, it is intended to highlight the applied models' specifications associated impacts. Research limitations/implications The interest brought about by treating this issue is twofold. On the one hand, it should provide useful guidelines to investors and financial advisors through helping them opt for the most effective and beneficial of the strategies, whereby they could efficiently hedge the equity markets related extreme risks and volatilities. On the other hand, it is intended to highlight the applied models' specifications associated impacts. Originality/value Study of Bitcoin can be considered as safe haven or hedge or diversifier instrument. Compare between Bitcoin, VIX and CDs.
Purpose This paper examines the impact of cryptocurrency market on the stock market performance in Middle East and North Africa (MENA) region. A comparative analysis is extended to distinguish this impact between Gulf countries and other economies in the region. Design/methodology/approach The analysis uses the information of cryptocurrencies and the stock market indices of the Gulf countries for the period 2014–2018 on a daily basis. Two strategies have been implemented to fulfill the goal of the study: first, the tests strategy, which is applied using the cointegration analysis and panel-specific forms of Granger causality; second, the regression strategy, which is applied mainly using the instrument variable with generalized method of moments (IV-GMM) method. Findings The results show that there is a significant relationship between the cryptocurrency market and the stock market performance in the MENA region. On the one hand, for the Gulf countries that claim full obedience to the Islamic Sharia rules, each 1% increase in the cryptocurrency returns reduces the stock market performance by 0.15%. On the other hand, for the non-Gulf (other MENA) countries that have flexibility in applying the Islamic Sharia rules or do not follow it, the stock market performance increases by 0.13%, for each 1% increase in the cryptocurrency returns. Originality/value The paper proposes two main contributions: First, the paper introduces the cryptocurrency returns as one of the determinants of the stock market performance in the MENA region. This impact is distinguished based on the degree of applying the Islamic Sharia rules and the vision of the government to the stock market. Second, the paper provides an empirical guideline for governments in the MENA region for efficient measures in their stock market, given the important expansion of the cryptocurrency market and the government type.
Gerson de Souza Raimundo Júnior, Rafael Baptista Palazzi, Ricardo de Souza Tavares, Marcelo Cabús Klötzle
Herding is a feature of investor behavior in financial markets, particularly in market stress. We apply an approach based on the cross-sectional dispersion of individual stocks' betas, which allows us to extract herding patterns, using two dynamic methodologies to measure the herding phenomenon over time with a state-space model for the Cryptocurrency Market. The results reveal that herding toward the market shows significant movement, and persistence regardless of the market condition, expressed through the market index, market volatility, and the volatility index. When analyzing path herding is possible to observe that herding was intense during the investigated period. We also identify a positive relationship between herding and market stress.
Ai Jun Hou, Ning Wang, Cathy Y. H. Chen, Wolfgang Karl Härdle
Cryptocurrencies, especially Bitcoin (BTC), which comprise a new digital asset class, have drawn extraordinary worldwide attention. The characteristics of the cryptocurrency/BTC include a high level of speculation, extreme volatility and price discontinuity. We propose a pricing mechanism based on a stochastic volatility with a correlated jump (SVCJ) model and compare it to a flexible co-jump model by Bandi and Renò (2016). The estimation results of both models confirm the impact of jumps and co-jumps on options obtained via simulation and an analysis of the implied volatility curve. We show that a sizeable proportion of price jumps are significantly and contemporaneously anti-correlated with jumps in volatility. Our study comprises pioneering research on pricing BTC options. We show how the proposed pricing mechanism underlines the importance of jumps in cryptocurrency markets.
The purpose of this study is to reveal whether cryptocurrency and non-cryptocurrency investors are different in terms of financial threats. In order to measure financial threat, 5-Item Financial Threat Scale (FTS) is used. It is found that the Turkish version of a 5-Item Financial Threat Scale (FTS) is highly reliable, unidimensional and a valid instrument for measuring the financial threat. According to the analysis, non-cryptocurrency investors have a more significant financial threat than cryptocurrency investors. Moreover, it is investigated that the working sector difference is not a distinguishing factor for financial threat. It is found that financial threat is associated with age, level of education, and monthly income. On the other hand, it is obtained that gender and marital status are not affecting factors for financial threat.
Bu çalışmada, kripto para birimleri arasında piyasada en yüksek hacime sahip olan Bitcoin para biriminin BİST 100, BİST Banka ve BİST Teknoloji endeksi arasında kısa ve uzun dönemde bir ilişkiye sahip olup olmadıkları zaman serisi analiz yöntemleri ile incelenmiştir. Bu amaçla 21/04/2011 ile 11/02/2020 tarihleri arası Bitcoin, BİST 100, BİST Banka ve BİST Teknoloji endeksi değişlerin günlük verileri kullanılmıştır. Çalışmada elde edilen bulgulara göre %5 anlamlılık seviyesinde uzun dönemde Bitcoin fiyatı ile BİST 100 endeksi arasında denge ilişkisine sahipken BİST Banka ve BİST Teknoloji endeksi ile bir ilişkiye rastlanılmamıştır. Buna ilaveten Bitcoin fiyatı ile BİST 100, BİST Banka ve BİST Teknoloji endeksleri kısa dönemde %5 anlamlılık seviyesinde değerlendirildiğinde herhangi bir nedensellik ilişkisine rastlanılmamıştır. Bu bulgular doğrultusunda BİST 100 ile Bitcoin fiyatları arasında uzun dönemde bir ilişkiye sahip olmasından dolayı yatırımcılar açısından Bitcoin’in portföy çeşitlendirilmesinde şu an için riskli bir yatırım tercihi olduğu söylenebilirken bunun yanında uzun dönemde Bitcoin fiyatları ile BİST Banka ve BİST Teknoloji endeksi arasında uzun dönemde ilişkinin olmaması, Bitcoin’in portföy çeşitlendirilmesinde risksiz bir yatırım tercihi olabileceği söylenebilmektedir.
Abstract Deep reinforcement learning is gaining popularity in many different fields. An interesting sector is related to the definition of dynamic decision-making systems. A possible example is dynamic portfolio optimization, where an agent has to continuously reallocate an amount of fund into a number of different financial assets with the final goal of maximizing return and minimizing risk. In this work, a novel deep Q-learning portfolio management framework is proposed. The framework is composed by two elements: a set of local agents that learn assets behaviours and a global agent that describes the global reward function. The framework is tested on a crypto portfolio composed by four cryptocurrencies. Based on our results, the deep reinforcement portfolio management framework has proven to be a promising approach for dynamic portfolio optimization.
While relevant stylized facts are observed for Bitcoin markets, we find a distinct property for the scaling behavior of the cumulative return distribution. For various assets, the tail index $μ$ of the cumulative return distribution exhibits $μ\approx 3$, which is referred to as "the inverse cubic law." On the other hand, that of the Bitcoin return is claimed to be $μ\approx 2$, which is known as "the inverse square law." We investigate the scaling properties using recent Bitcoin data and find that the tail index changes to $μ\approx 3$, which is consistent with the inverse cubic law. This suggests that some properties of the Bitcoin market could vary over time. We also investigate the autocorrelation of absolute returns and find that it is described by a power-law with two scaling exponents. By analyzing the absolute returns standardized by the realized volatility, we verify that the Bitcoin return time series is consistent with normal random variables with time-varying volatility.