Cryptocurrencies have emerged about ten years ago as a new form of currency and have attracted much attention since they depend on a fully decentralized system, and so their transactions are very fast and have zero transaction cost. Therefore, character of cryptocurrencies and their volatility have been discussed widely by investors, policymakers and economists in recent years. From this point of view, this study aims to explain the price volatility of cryptocurrencies with macro-financial indicators, and thereby, the effects of S&P 500 stock market index, gold price, oil price, 2-year benchmark US Bond interest rate and US Dollar index on the prices of four major cryptocurrencies, Bitcoin, Litecoin, Ethereum, and Ripple, are investigated. The study comprises a panel data analysis applied to daily data over the period of August 2016 – April 2019, and analysis results show that increases in gold price, oil price and S&P 500 index raise the prices of cryptocurrencies, while increases in 2-year benchmark US Bond interest rate and US Dollar index cause to a fall. This adverse effects of the US Dollar index and US Bond interest rate on the prices of cryptocurrencies indicates that when the value of US Dollar and US Bond yield decrease investors prefer to invest in cryptocurrencies as alternative investment instruments. On the other hand, cryptocurrencies move with a similar trend of stock market index, gold price and oil price which are overall market indicators. Thereby, findings of this study show that cryptocurrencies behave more like an investment instrument than a currency, and prices of these financial assets interact with significant macro-financial indicators.
Bitcoin is a decentralised digital currency which attracts growing interest over recent years. Much research from different subjects emerged as bitcoin is a multidisciplinary product. Among all these studies, the interpretation of the drastic fluctuation of bitcoin price attracts a great attention. Many influence factors of bitcoin price were found. However, seldom research reveals the dependence structure between price and its influence factors. By selecting ten interpretable influence factors from the bitcoin network and using copula theory, we find that the bitcoin price has different correlation structures with its influence factors. These findings provide new insights into the behaviour of miners, users, and coins in the bitcoin system, thus leading to meaningful implications for policymakers, investors and risk managers dealing with bitcoin and other cryptocurrencies.
This study examines and compares the volatility in sample fit and out of sample forecast of four different heteroscedasticity models, namely ARCH, GARCH, EGARCH and GJR-GARCH applied to Bitcoin, Ethereum and Ripple. The models are fitted over the period from 2016-01-01 to 2019-01-01 and then used to obtain one day rolling forecasts during the period from 2018-01-01 to 2019-01-01. The study investigates three different themes consisting of the modelling framework structure, complexity of models and the relation between a good in sample fit and good out of sample forecast. AIC and BIC are used to evaluate the in sample fit while MSE, MAE and R2LOG are used as loss functions when evaluating the out of sample forecast against the chosen Parkinson volatility proxy. The results show that a heavier tailed reference distribution than the normal distribution generally improves the in sample fit, while this generality is not found for the out of sample forecast. Furthermore, it is shown that GARCH type models clearly outperform ARCH models in both in sample fit and out of sample forecast. For Ethereum, it is shown that the best fitted models also result in the best out of sample forecast for all loss functions, while for Bitcoin non of the best fitted models result in the best out of sample forecast. Finally, for Ripple, no generality between in sample fit and out of sample forecast is found.
We examine the significance of fourty-one potential covariates of bitcoin returns for the period 2010–2018 (2872 daily observations). The recently introduced principal component-guided sparse regression is employed. We reveal that economic policy uncertainty and stock market volatility are among the most important variables for bitcoin. We also trace strong evidence of bubbly bitcoin behavior in the 2017–2018 period.
Report presents analysis of empirical distribution of future returns of bitcoin (BTC) from BTUSD inverse option prices. Logistic pdf is chosen as underlying distribution to fit option prices. The result is satisfactory and suggests that these prices can be described with just three or even one parameter. Fitted Logistic pdf matches forward price movements upto a scaling factor. Nevertheless, this observation stands alone and does not allow stochastic description of underlying prices with logistic pdf in similar fashion as it is done within Black-Scholes modelling framework. Put-call parity relationship is derived connecting prices of vanilla inverse options and futures.
This paper studies the forecasting ability of cryptocurrency time series. This study is about the four most capitalized cryptocurrencies: Bitcoin, Ethereum, Litecoin and Ripple. Different Bayesian models are compared, including models with constant and time-varying volatility, such as stochastic volatility and GARCH. Moreover, some crypto-predictors are included in the analysis, such as S\&P 500 and Nikkei 225. In this paper the results show that stochastic volatility is significantly outperforming the benchmark of VAR in both point and density forecasting. Using a different type of distribution, for the errors of the stochastic volatility the student-t distribution came out to be outperforming the standard normal approach.
Anthony Ngunyi, Simon Mundia, Cyprian Ondieki Omari
Cryptocurrencies have become increasingly popular in recent years attracting the attention of the media, academia, investors, speculators, regulators, and governments worldwide. This paper focuses on modelling the volatility dynamics of eight most popular cryptocurrencies in terms of their market capitalization for the period starting from 7th August 2015 to 1st August 2018. In particular, we consider the following cryptocurrencies; Bitcoin, Ethereum, Litecoin, Ripple, Moreno, Dash, Stellar and NEM. The GARCH-type models assuming different distributions for the innovations term are fitted to cryptocurrencies data and their adequacy is evaluated using diagnostic tests. The selected optimal GARCH-type models are then used to simulate out-of-sample volatility forecasts which are in turn utilized to estimate the one-day-ahead VaR forecasts. The empirical results demonstrate that the optimal in-sample GARCH-type specifications vary from the selected out-of-sample VaR forecasts models for all cryptocurrencies. Whilst the empirical results do not guarantee a straightforward preference among GARCH-type models, the asymmetric GARCH models with long memory property and heavy-tailed innovations distributions overall perform better for all cryptocurrencies.
This paper considers the development of attractive strategies featuring cryptocurrency assets, considering their costs and potential risks. The object of analysis in this paper is cryptocurrency as an investment instrument. The main hypothesis of the research is that modern portfolio theory can be applied to cryptocurrency investments to design an investment portfolio with appropriate risk and profitability characteristics. The authors of the paper: (i) place cryptocurrencies in the context of modern financial market and financial technology development;
This study applies threshold regression model in a bivariate framework to explore the nonlinear and long-term relationship among daily Bitcoin and gold prices over the period April 2010 to December 2018. Our empirical results are threefold: first, we show that gold is a significant predictor of Bitcoin prices. Second, we find evidence of a non-linear relationship between Bitcoin and gold prices characterized rather by a two-regime relationship with a structural break occurring in October 2017. Third, we explain the existence at before the break, there is statistically significant, negative but weak causality indicating that Bitcoin is a speculative asset. However, after the break, the relationship becomes positive and strong revealing the diversifier and hedge properties of Bitcoin.
Daniel Cahill, Dirk G. Baur, Zhangxin Liu, Joey Yang
We investigate the price reaction of listed companies in response to blockchain-related announcements. The average abnormal return based on a global sample of 713 firm announcements is approximately 5% on the announcement day, with significantly higher returns for U.S. firms, smaller firms and announcements in late 2017 and early 2018. We show that abnormal returns are linked to the performance of bitcoin. Additionally, speculative announcements exhibit higher returns than non-speculative announcements, and blockchain- related Form 8-K disclosures have negligible difference in performance compared to their U.S. peers. Whilst we acknowledge the possibility of a latent variable that affects both the abnormal returns and the performance of bitcoin, we hypothesise that investors have confused bitcoin and blockchain, and used the performance of bitcoin as an indicator of the expected success of the blockchain technology.
A total of 1.1 million bitcoins were stolen in the 2013–2017 period. Noting that the average price for a Bitcoin in 2018 was $7572 the corresponding monetary equivalent of losses is $8.9 billion highlighting the societal impact of this criminal activity. Investigating the response of the uncertainty of Bitcoin returns when hacking incidents occur, the results of this study point toward two different responses. After experiencing a contemporaneous effect at day t=0, the volatility increases significantly again at day t+5. Hacking incidents that occur in the Bitcoin market also affect the uncertainty in the Ethereum market with a time delay of five days. Notably, neither Bitcoin nor Ethereum appear to exhibit asymmetric responses to negative innovations.