Adedeji Daniel Gbadebo, Ahmed Oluwatobi Adekunle, Wole Adedokun, Adebayo-Oke Abdulrauf Lukman · 5 authors
This paper offers a plausible response to “what explains the sporadic volatility in the price of Bitcoin?” We hypothesized that market “fundamentals” and “information demands” are key drivers of Bitcoin’s unpredictable price fluctuation. We adopt the transfer-function [Autoregressive Distributed Lag, ARDL] model and its Bounds testing approach to verify how the volatility of the price of Bitcoin responds to its transaction volume, cryptocurrency market capitalisation, world market equity index and Google search. We found the existence of long-run cointegration relation and observed that all the variables except the equity index positively explain the volatility of Bitcoin price. The result established evidence that market fundamentals drive erratic swing in Bitcoin price than information.
Weihao Han, David Newton, Emmanouil Platanakis, Charles Sutcliffe · 5 authors
Cryptocurrency returns are highly non-normal, casting doubt on the standard performance metrics. We apply almost stochastic dominance (ASD), which does not require any assumption about the return distribution or degree of risk aversion. From 29 long-short cryptocurrency factor portfolios, we find eight that dominate our four benchmarks. Their returns cannot be fully explained by the three-factor coin model of Liu et al. (2022). So we develop a new three-factor model where momentum is replaced by a mispricing factor based on size and risk-adjusted momentum, which significantly improves pricing performance.
This dissertation is dedicated to the analysis of three superordinate economic principles in varying market environments: market efficiency, the behavior of market participants and information asymmetry. Sustainability and social responsibility have gained importance as investment criteria in recent years. However, responsible investing can lead to conflicting goals with respect to utility-maximizing behavior and portfolio diversification in efficient markets. Conducting a meta-analysis, this thesis presents evidence that positive (non-monetary) side effects of responsible investing can overcome this burden. Next, the impact of the EU-wide regulation of investment research on the interplay between information asymmetry, idiosyncratic risk, liquidity and the role of financial analysts in stock markets is investigated. An empirical analysis of the emerging primary and secondary market for cryptocurrencies yields further insights about the effects of information asymmetry between investors, issuers and traders. The efficient allocation of resources is dependent on the market microstructure, the behavior of market participants, as well as exogenous shocks. Against this background, this thesis is dedicated to the empirical analysis of limit order books, the rationality of traders and the impact of COVID-19. Due to its young history, the market for cryptocurrencies yields a suitable research subject to test classical financial theories. This doctoral thesis reveals parallels between the microstructure of cryptocurrency and stock markets and uncovers some previously unknown statistical properties of the cryptocurrency market microstructure. An initial examination of the impact of COVID-19 further shows that cryptocurrencies with a high market capitalization seem to react to macroeconomic shocks similar to stock markets. This cumulative dissertation comprises six stand-alone papers, of which three papers have already been published.
This study examines several asset pricing specifications applied for a sample of 72 cryptocurrencies. We extend the existing literature on asset pricing of cryptocurrencies by including higher co-moment factors, namely co-skewness and co-kurtosis. Our overall conclusion is that co-skewness and co-kurtosis are also priced in crypto-markets, but less pronouncedly than in equity/commodity/derivatives markets. Size and momentum factors further increase explanatory power, but their regression coefficients are insignificant.
Sahar Loukil, Mouna Aloui, Ahmed Jeribi, Anis Jarboui
This study examines the safe haven properties of top five crypto-currencies, oil and gold for the five gulf cooperation council countries in view of COVID-19 period through a nonlinear and asymmetric framework NARDL methodology to uncover short- and long-run asymmetries. Using daily data from January 2019 to April 2020, we find that Bitcoin and Ethereum are safe haven assets for GCC in instability; Bitcoin is a safe haven for Oman, Saudi Arabia and Abu Dhabi. Ethereum is a safe haven for Bahrain, Kuwait and Qatar. Further, for Kuwait, Qatar, Saudi Arabia and Abu Dhabi, oil is a safe haven asset in mitigated period. We also notice that the strategies of hiding differ interestingly for all countries except for Saudi Arabia that does not significantly change its strategies. Thus, portfolio managers may consider few eligible crypto-currencies and oil for their inclusion into the portfolio to hedge risk. While, speculators acting in both stock and crypto market may go for a spread strategy. Our research is useful for portfolio managers and financial advisors looking for the best of crypto's, gold and oil to hedge shocks in stock market indices.
In the current paper, we develop a methodology to price lookback options for cryptocurrencies. We propose a discretely monitored window average lookback option, whose monitoring frequencies are randomly selected within the time to maturity, and whose monitoring price is the average asset price in a specified window surrounding the instant. We price these options whose underlying asset is the CCI30 index of various Cryptocurrencies, as opposed to a single cryptocurrency, with the intention of reducing volatility, and thus, the option price. We employ the Normal Inverse Gaussian (NIG) and Rough Fractional Stochastic Volatility (RFSV) models to the cryptocurrency market and using the Black-Scholes as the benchmark model. In doing so, we intend to capture the extreme characteristics such as jumps and volatility roughness for cryptocurrency price fluctuations. Since there is no availability of a closed-form solution for lookback option prices under these models, we utilize the Monte Carlo simulation for pricing and augment it using the antithetic method for variance reduction. Finally, we present the simulation results for the lookback options and compare the prices resulting from using the NIG model, RFSV model with those from the Black-Scholes model. We found that the option price is indeed lower for our proposed window average lookback option than for a traditional lookback option. We found the Hurst parameter to be H = 0.09 which confirms that the cryptocurrencies market is indeed rough.
The predictability of asset prices works against the notion of an efficient market where asset prices reflect all available and relevant information. This paper examined the predictability of Bitcoin and 51 other cryptocurrencies that have been classified into the following five categories: Application, Payment, Privacy, Platform, and Utility. Two market efficiency tests (Ljung-Box autocorrelation and Runs tests) were run on the daily returns of the 52 unique cryptocurrencies and the MSCI World index from 28 April 2013 to 30 June 2019. The results showed that Bitcoin was consistently efficient, whereas most of the other cryptocurrencies and even the MSCI World index were not, implying that their prices were predictable. Categorically, Payment altcoins were the most consistent in showing inefficiency. Since altcoins in this category also recorded the third highest risk-adjusted returns, investors with advanced technical trading strategies had a great chance of exploiting the market information to make extremely high abnormal returns.
This paper examines the time-varying conditional correlations between Bitcoin future market and five FOREX future markets. A sixvariate dynamic conditional correlation (DCC) GARCH model is applied in order to capture potential contagion effects between the markets for the period 2017-2019. Empirical results reveal contagion during the under investigation period regarding the one sixvariate model, showing potential volatility transmission channels among the future markets. Findings have crucial implications for policymakers who provide regulations for the above derivative markets.
We highlight the considerable recent research that investigates how cryptocurrencies have been impacted by COVID-19. We highlight common threads of investigation and consider common findings and conclusions. We also provide suggestions for future research. Additionally, we provide an overview of a recent paper in which we report on a study that applies wavelet methods to daily data of COVID-19 world deaths and daily Bitcoin prices,
This study considers a market-based economy that is composed of two asset classes: one is a digital, cryptocurrency, and the other is real, gold. We demonstrated that coins like (BTC, LTC, and DASH) can substitute a traditional safe haven “gold” in an intertemporal investment portfolio to become a new form of safe haven. The cryptocurrency follows a Jump-diffusion process. However, gold prices follow an Ornstein-Uhlenbek process to characterize the stochastic nature of the market. The stochastic optimal control approach, combined with the strategic asset allocation and the intertemporal utility theory, are used through the derivation of a Hamilton-Jacobi-Bellman (HJB) equation to determine an explicit solution of the optimal allocation problem for investors with CRRA utility function. We considered the Gamma Lévy process to solve the optimization problem. By using the secant method, we determined numerically the optimal percentage invested in the two asset classes at each time over the holding period. Our results showed that an investor can substitute gold by coins (BTC, LTC, DASH) from an investment portfolio perspective. Although Gold is supposed to be the traditional safe-haven asset, the digital currency seems to emerge as a new form of safe-haven value in a risky environment.
This paper presents an empirical verification of the effectiveness and usefulness of investment diversification using the main stock exchange indices and Bitcoin. The objective is to determine the effects applying the Markowitz portfolio optimization theory, i.e., the advantages of applying the modern portfolio theory for institutional investors. The research offers an answer to the following question: what are the advantages and disadvantages of using Bitcoin in portfolio optimization? The paper contributes to the representation of the reach and limitations of the modern portfolio theory for institutional investors. The conclusion is that rational behaviour of institutional investors requires consideration of portfolio optimization using the Markowitz model, because it is possible to create portfolios which, on the basis of historical returns, provide desired returns alongside certain risks. The methodology includes the analysis of high frequency data, i.e., daily trading data were used. The results indicate that the use of the Markowitz portfolio selection method, with all its limitations, is desirable, possible and applicable, but that it entails serious flaws in the sense of neglecting transaction costs, foreign exchange differences and the real value in the stock market. The results of the research show that Bitcoin is a good source of diversification in a portfolio that contains traditional financial instruments both for the risk-averse investor as well as for those investors who have a greater appetite for risk. The conclusion is that rational behavior of institutional investors requires consideration of investing in Bitcoin using the Markowitz model. However, given the high degree of volatility, investors should be very careful when making decisions about including Bitcoin in the portfolio.
A growing literature has employed the existing generalized spillover measures to measure the connectedness – or market integration – of cryptocurrencies. This method, while useful, does not properly control for the cross-correlations of the cryptocurrencies when computing aggregate spillovers from all others to any given cryptocurrency, whereas the joint spillover method of Lastrapes and Wiesen (2021) does. This paper further describes the novel multivariate conditioning sets employed by the joint spillover method. By employing these two techniques and evaluating the differences in the results, we demonstrate that controlling for the cross-correlations of cryptocurrencies matters for measuring aggregate spillovers and the overall connectedness of the cryptocurrency market. Using data on ten of the most traded cryptocurrencies, we find that the generalized spillover index overestimates overall connectedness by over nine percentage points relative to the new joint spillover index. This difference varies temporally and across cryptocurrencies.
This paper surveys the capacity of simple macroeconomic models - 'three easy pieces' - to account for persistent and positive valuations of privately issued assets based on the blockchain. Each of these three models - transactions demand for a means of payment, consumption-based capital asset pricing, and search and matching - highlights important aspects of digital payments. The mutual interference of these jointly produced features may impede widespread use of cryptocurrencies until technological innovations have been developed to separate them.
Yunchuan Sun, Xiangyi Kong, Tongrui Chen, Hang Su · 6 authors
Compared with stock market, cryptocurrency market is more susceptible to investor sentiment at the lack of substantial asset support. This study develops a proxy to measure the investor sentiment of cryptocurrency market by using textual analytics on millions of posts in Chain Node, which is the most active online community for Chinese cryptocurrency investors. We investigate the correlation between the sentiment and the market return from Jan. 2018 to Aug. 2020. The study argues that the proposed proxy could well reflect the investor sentiment of the cryptocurrency.