Abstract. This research employs Capital Asset Pricing Model and foreign exchange exposure theory to explain how the value of financial stocks is affected by the home country cryptocurrency. Previous literature proposed that financial stocks were related to the economic or individual financial ratio, but rarely discussed the impact of a cryptocurrency variable in the digital economy. This paper presents specific findings to prove that cryptocurrency development causes structural change in the financial industry, by examining 67,166 panel data observations from China and Taiwan markets. We offer the following important conclusions: 1. Financial stocks in the China market suffer significantly higher impacts from home country cryptocurrency exposure than the Taiwan market. 2. Financial stocks in the China market are more greatly shocked by the CAPM three factors variables than the Taiwan market. 3. There are significant differences between the two financial markets. 4. The dynamics of the adjustment process of cryptocurrency evolution and the monetary system are key solutions for both markets. Keywords: cryptocurrency, Fin-Tech, Exchange rate Exposure. JEL Classification A14, D82, F65, G12, F3 Formulas: 2; fig.: 0; tabl.: 4; bibl. 31.
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.
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.
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 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.
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.
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
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.
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.