Calendar anomalies as the seasonal tendencies in stock returns are the signal of irregular behaviour of stock markets. These anomalies have been comprehensively studied in many matured as well as emerging stock markets. But there is lack of exploration of calendar anomalies in the cryptocurrency market. So, the present treatise is an attempt to fill this lacuna by studying day of the week effect on cryptocurrencies' returns and volatility. This study is based on the prices of eight cryptocurrencies (viz. Bitcoin, EOS, Ethereum, Bitcoin Cash, Litecoin, Tether, XRP and Stellar) for a period starting from July 2017 and up to March 2020. The series of daily and day-wise returns were initially studied for stationarity using Ng-Perron tests and augmented Dickey–Fuller test. The results from these tests confirmed that the cryptocurrencies' return series are stationary. The day of the week effect on cryptocurrencies returns was studied by introducing the dummies for each day of the week in the ordinary least square regression equation. The residuals from the ordinary least square regression equation were tested for ARCH effect using Engle's ARCH test. The results from the test confirmed the presence of ARCH effect in all series. The GARCH (1,1) model and PARCH model were further applied to account for ARCH effect and these models confirmed the presence of the day of the week effect in all the cryptocurrencies' returns and volatility except for day of week effect in Bitcoin and Tether returns. So, the significant day of the week effect was present in all cryptocurrencies' returns and volatility but the significant day of the week effect was absent in Bitcoin's returns and Tether's returns. These findings of significant day effect may help the existing and potential investors in taking investment decision in contemporary scenario of no ban in cryptocurrency market in India.
Bill X. Hu, Joon Ho Hwang, Chinmay Jain, Jim Washam
We analyse 519.4 million Bitcoin orders placed on Gemini Exchange during January 2016-August 2019 and find limit orders dominate at 99.92%. We document order-based evidence of price manipulation during the Bitcoin bubble in late 2017, when the daily number of market orders during the bubble period more than triples the overall daily average. The changes in both prices and liquidity satisfy two criteria specified in Kyle and Viswanathan (2008) for the price manipulation definition. Moreover, we find a significant increase in market order imbalance associated with price manipulations modelled in Jarrow, Protter and Roch (2012).
Abstract This paper briefly overviews several challenging dimensions pertaining to cryptocurrencies with respect to their valuation, legitimacy, design, consensual acceptance and market‐based stylized facts with a view to understanding whether this new asset class indeed has the potential to become an alternative, or a replacement, to traditional fiat currencies. Our survey indicates that public embrace of cryptocurrencies continues to lag as the masses currently show reluctance in embracing cryptocurrencies as a complement, let alone a substitute to fiat counterparts. Governments have also successfully defended their sovereignty in preserving legal tender status, structural seigniorage and exclusivity. Market‐based studies hint at consistent inefficiencies across the spectrum. Furthermore, whether fundamental and mining factors determine cryptocurrencies' values remain unsettled. The most promising areas of research for crypto‐financial intelligentsia would be delving into establishing trial runs for central bank‐backed cryptocurrencies. In addition, we highlight that several methodological and data‐based obstacles remain in assessing the link between cryptocurrencies and their traditional rivals. This avenue remains a fertile ground for potential future research.
Çalışmanın amacı; kripto para birimlerinden Bitcoin ve Litecoin piyasalarının etkinliğini ölçerek haftanın günü etkisinin varlığını 29.04.2013- 29.02.2020 tarihleri arasında günlük kapanış fiyatları kullanılarak incelenmesidir. İlgili dönemlerde her iki para birimine ait piyasaların etkinliğini incelemede ARMA, haftanın günü etkisinin olup olmadığının tespitinde ise Kruskal Wallis H testinden faydalanılmıştır. Çalışmanın sonunda her iki kripto para biriminin getirilerinin bir önceki zamandan bağımsız hareket ettiği yani ilgili dönemde bu kripto para piyasalarının etkin piyasaya benzer özellik taşıdığı ve haftanın günü etkisinin de varlığına rastlanılmadığı tespit edilmektedir.
Purpose In this paper, the authors examine the interconnectedness of four blockchain exchange-traded funds (ETFs) with other financial markets, such as stocks and cryptocurrencies. Design/methodology/approach A multivariate dynamic conditional correlation model is used to model the relationship of blockchain ETFs with equity and cryptocurrency markets. Risk-minimizing hedge ratios are calculated following the methods used in studies by Kroner and Sultan (1993) and Sadorsky (2012). Findings The empirical results show a high degree of correlation of blockchain ETF returns with returns of the NASDAQ Composite Index, while the level of comovement with Bitcoin is relatively low. Research limitations/implications The results imply that blockchain ETFs may be suitable for hedging purposes in a portfolio holding Bitcoin. Furthermore, investing in blockchain ETFs appears similar to investing in NASDAQ. Originality/value To the best of the authors’ knowledge, no studies have investigated the dynamic relationship of blockchain ETFs and other financial assets.
Abstract While gaining more popularity both as a financial asset and a commodity, a number of cryptocurrencies are emerging with a loosely regulated market microstructure which is a challenge to their efficiency. We have ranked 6 out of the top 10 cryptocurrencies based on their inefficiency ratios, using a novel time‐varying generalised Hurst exponent methodology. All the six crypto markets exhibit a time‐varying efficiency throughout the studied period, thus indicating a varying degree of exploitable profitable trading opportunities. The inefficiency ratio indicates that Bitcoin is the third most inefficient market, while the first and second most inefficient markets are DASH and NEM, respectively, thus they provide the most abnormal profit opportunities. However, the most efficient crypto markets are Ethereum and Ripple according to the order of their rankings. Further research could be performed on the factors affecting the inefficiency index to understand the efficiency determination of these cryptocurrency markets.
The cryptocurrency market has received immense consideration in media and academia since the beginning of 2013 because of its huge price fluctuation. This study focuses on Arab investors who invest in the cryptocurrency market by investigating the influence of behavioral finance factors on investment decisions in the cryptocurrency market. A quantitative approach was used by employing a snowball sampling method through 112 questionnaires. The results show that herding theory, prospect theory, and heuristic theory have a significant effect on investors' investment decisions in the cryptocurrency market. This emphasizes the significant role of the proposed behavioral factors as determinants of the investors' investment decisions. This study contributes to the existing research by consolidating the results of different researches in this study. It also contributes to the investors' understanding of the dynamics of the cryptocurrency market and it enhances the ability to make informed decisions based on their understanding. The implication of the findings will prepare hit and run investors to be progressively prepared to stay in the cryptocurrency market and develop their abilities on the most proficient method to settle on sound venture choices. Furthermore, the findings of this study will encourage financial specialists to realize that information on the traditional finance theory is not adequate to excel in the cryptocurrency market.
This paper empirically examines the convergence of cryptocurrency markets with particular attention to top 30 cryptocurrencies. The study applies the novel Phillips and Sul panel convergence technique to daily closing price data of 30 cryptocurrencies for the period October 4, 2017 to May 31, 2020. The empirical findings suggest the evidence of divergence and the existence of club convergence across the cryptocurrency markets. The study finds the existence of five clubs in the top 30 cryptocurrency markets. The outcome of the study helps the investors and crypto lovers to diversify their portfolio by seeing the common transition path of the group of currencies.
This paper investigates the potential portfolio diversification benefits by introducing the Bitcoin to the traditional diversified financial portfolio. Using a bootstrap-based stochastic dominance (SD) test and daily prices of the commodity and stock market indices, we find that the introduction of Bitcoin improves the performance of the traditional financial portfolio and the optimal portfolio choice changes according to the market regime. Principally, results show that the optimal portfolio diversification combining Bitcoin, US stock market and commodities indices can be a good hedge, offering risk-averse, more performing portfolio investments during any financial crisis.
Abstract We examine how investors arbitrage the Bitcoin spot and futures markets. Using intraday data of the Chicago Board Options Exchange, we reconstruct the actual arbitrage condition that investors confront. We find that there are few arbitrage profit opportunities in “normal” markets, but large arbitrage profit opportunities arise during Bitcoin market “crashes.”
This paper aims to enrich the understanding and modelling strategies for cryptocurrency markets by investigating major cryptocurrencies’ returns determinants and forecast their returns. To handle model uncertainty when modelling cryptocurrencies, we conduct model selection for an autoregressive distributed lag (ARDL) model using several popular penalized least squares estimators to explain the cryptocurrencies’ returns. We further introduce a novel model averaging approach or the shrinkage Mallows model averaging (SMMA) estimator for forecasting. First, we find that the returns for most cryptocurrencies are sensitive to volatilities from major financial markets. The returns are also prone to the changes in gold prices and the Forex market’s current and lagged information. Then, when forecasting cryptocurrencies’ returns, we further find that an ARDL(p,q) model estimated by the SMMA estimator outperforms the competing estimators and models out-of-sample.
Florin Aliu, Artor Nuhiu, Besni̇k A. Krasniqi, Gent Jusufi
Purpose This study aims to compare the diversification risk of the crypto portfolio with those of equity portfolios. For this purpose, the hypothetical index was constructed with 20 cryptocurrencies that hold the highest market capitalization in the Coin Market Cap database, named as the Crypto-Index 20. Design/methodology/approach The portfolio diversification techniques were used to identify risk linked with the six largest European equity indexes and compared with the Crypto-Index 20. Indexes were considered as an independent portfolio while analysis was completed separately for each of them. Data concerning stock prices and their trade volume were collected from the Thomson Reuters Eikon database while crypto prices and their trade volume from the Coin Market Cap database. The diversification risk of the stock indexes was measured separately for each portfolio with the same risk techniques and the same methodological process. Findings Research results indicate that Crypto-Index 20 on average was 76 times riskier than FTSE 100, 55 times riskier than FTSE MIB, 44 times riskier than IBEX 35, 10 times riskier than CAC 40 and 9 times riskier than DAX and MDAX. Crypto-Index 20 comprises a stronger positive correlation and is exposed to higher volatility than six selected European equity indexes. Originality/value This research provides practical implications for the investors on the diversification benefits and risks attached to the cryptocurrencies portfolio by comparing it with the traditional equity portfolios. From a policy perspective, regulators might obtain information on the risk properties involved into cryptocurrencies and the possibility of creating an optimal portfolio.
Jeremy Eng‐Tuck Cheah, Di Luo, Zhuang Zhang, Ming‐Chien Sung
This paper comprehensively examines the performance of a host of popular variables to predict Bitcoin returns. We show that time-series momentum, economic policy uncertainty, and financial uncertainty outperform other predictors in all in-sample, out-of-sample, and asset allocation tests. Bitcoin returns have no exposure to common stock and bond market factors but rather are affected by Bitcoin-specific and external uncertainty factors.
Diese Masterarbeit greift die Theorie der Portfoliooptimierung auf: die mathematische Formulierung des Problems, seine Ableitungen (Risikominimierungsformulierung) und Annahmen, seine Einschränkungen sowie einige Verbesserungen und Erweiterungen des bestehenden Frameworks. Ziel der Arbeit ist es auch, in Python zu simulieren und zu implementieren: Markowitz (Global Varianzminimal, Maximum Sharpe), Hierarchical Risk Parity und drei naiv Portfolios: gleichgewichtete, inverse Volatilität und inverse Varianz in der neuartigen Anlageklasse der Kryptowährungen. Als Benchmark wird die CRyptocurrency IndeX, CRIX, verwendet. Die Portfoliooptimierung wird anhand von 120 Tagen täglicher historischer Daten berechnet, wobei die Portfolio-Anpassung alle 7 Tage und 30 Tage erfolgt. Portfolios sind Long-Short Strategien ohne Hebelwirkung und Verbesserungen in der Kovarianzmatrix werden mithilfe von Eigenwert-Clipping der Zufallsmatrixtheorie angewendet.
In the finance sector, in general, a single VaR method is used for one single portfolio or for all similar portfolios and it hampers the opportunity for comparison. Such shortcoming deriving from trusting one single VaR method results in very incoherent results for the analysis as well as in untrustable transactions based upon those risk estimations. In order to overcome that, similar investments tools/portfolios should be analysed simultaneously by different VaR methods for comparison. Considering such overcome, this study is aimed to compare the VaR (value at risk) estimation methodologies for all 5 separated portfolios (which are similar considering their liquidity and investment process) holding USD, EUR, GOLD, BIST100 Index (Istanbul Stock Exchange Index) and BITCOIN considering their daily return on TRL (Turkish Lira). For performance measurement of different methodologies listed namely as extreme value VaR (GRPD-gnadenko theorem), ewma based volatility filtered historical simulation, historical simulation, delta normal, and bootstrapping; the 3 backtesting procedures and the related statistics are used.
The authors acquire a unique dataset of high-frequency traded prices for bitcoin call and put options from the Deribit cryptocurrency derivatives exchange, by 15-minute sampling via the application programming interface. They use these prices to construct a term structure of bitcoin implied volatility indices using a variance swap fair-value formula that is employed by the CBOE for the VIX, an index commonly referred to as the “investor fear gauge” for the US stock market. Employing over seven million option prices, they construct the bitcoin implied volatility indices with maturities from one week to three months, sampled every 15 minutes from March 2019 to March 2020. They discuss the features of the index and the associated bitcoin variance risk premia, with three different regular time partitions for realized variance, viz. 15-minutes, hourly, and daily. They also examine the relationship between bitcoin’s 30-day realized variance, volatility index, and variance risk premium, with their equivalent for US equities, oil, gold, the USD/EUR exchange rate, and the 10-year US Treasury note. <b>TOPICS:</b>Currency, mutual funds/passive investing/indexing, statistical methods, performance measurement <b>Key Findings</b> • The authors’ novel dataset of bitcoin option prices from Deribit is applied to quote bitcoin VIX indices with maturities from one-week to three months. The short-maturity bitcoin VIX exceeded 200% after the global outbreak of Covid-19. • The indices provide indicative fair values for bitcoin variance swaps, which are traded on-chain. Using realized volatility monitored at 15-minute, hourly, and daily frequencies, they examine the bitcoin variance risk premium at different maturities. • Bitcoin’s 30-day realized volatility, volatility index, and variance risk premium are correlated with their equivalent for US equities, oil, gold, the USD/EUR exchange rate, and the 10-year US Treasury note. Diversification potential decreased dramatically after the outbreak of Covid-19.