This paper studies cryptocurrency. Firstly, this paper discusses the currency attribute of cryptocurrency. Secondly, this paper analyzes the advantages and disadvantages of cryptocurrency. Thirdly, this paper discusses the impact of cryptocurrency on the currency structure. Finally, this paper constructs the returns according to the daily price and statistically analyzes the yield difference of Bitcoin, Ethereum and Dogecoin. The ARIMA model is used to predict the return of cryptocurrency. This paper also gives the corresponding investment suggestions.
Adedeji Daniel Gbadebo, Ahmed Oluwatobi Adekunle, Wole Adedokun, Adebayo-Oke Abdulrauf Lukman · 5 authors
This paper offers a plausible response to âwhat explains the sporadic volatility in the price of Bitcoin?â We hypothesized that market âfundamentalsâ and âinformation demandsâ are key drivers of Bitcoinâs 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.
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 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 â or market integration â 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.
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.
We look at the association between the price of a cryptocurrency and the secondary market prices of the hardware used to mine it. We find the prices of the most efficient Graphical Processing Units (GPUs) for Ethereum mining are significantly positively correlated with the daily price returns to that cryptocurrency.