After the seminal work of Liu et al. (2015) finds that the realized volatility (RV) using 5-minute intervals performs well, economists tend to use this simple measure in applications. Existing literature in the cryptocurrency already relies on 5-minute RV, but no paper has evaluated whether 5-minute RV performs well compared with other realized measures. Following Liu et al. (2015), we show that the 5-minute RV of Bitcoin performs well compared to other realized measures, meaning that this result justifies the existing literature that already uses this simple measure. This paper also indicates that realized measures with longer intervals such as 120-minute RV could provide inaccurate estimates.
Cryptocurrencies are virtual currencies employed in blockchain transactions. They are particularly worthy of theoretical examination, given the limited academic literature on the subject. This paper constructs valuation models of bitcoin and altcoins, both as single investments and components of mutliple-asset portfolios. As single investments, cryptocurrencies are valued at the confluence of Legendre utility functions, with Esscher transformed Geometric Levy pricing processes. As part of portfolios, cryptocurrencies are contained in traditional Markowitz portfolios which are varied by increasing the proportion of the riskless asset, shorting the risky asset, or adding currency options. Theoretical formulations show that Markowitz models combined with bitcoin, located on the Capital Market Line (which we term CML portfolios), have low returns, mainly due to the presence of the riskless asset. Such portfolios are appropriately suited to the investment goals of risk-averse traders, while overlooking the preferences of risk-takers. To satisfy less risk-averse investors, we propose a high-return portfolio with 9 asset choices, consisting of risky assets, cryptocurrencies, US dollars, soybean futures, Treasury bond futures, oil futures, currency options on the US dollar, currency options on the Mexican peso, and technology, or biotechnology stocks. Laplace transforms are employed to suppress volatility, skewness, or kurtosis of returns, which empirical studies have found to contribute to tail risk contained in outliers in fat-tailed distributions.
We suggest that flexible majority rules for currency issuance decisions foster the stability of a cryptocurrency. With flexible majority rules, the voteshare needed to approve a particular currency issuance growth is increasing with this growth rate. By choosing suitable parameters for these flexible majority rules, we show that optimal growth rates can be achieved in simple settings. Moreover, with flexible majority rules, changes in the composition of growth-friendly and growth-adverse agents only have a comparatively moderate impact on growth rates, and extreme growth rates are avoided. Finally, we show that optimal money growth rates are realized if agents entering financial contracts anticipate ensuing inflation rates determined by these flexible majority rules.
Stefano Bistarelli, G Figa' Talamanca, Francesco Lucarini, Ivan Mercanti
Although Bitcoin is a relatively new subject in Economics, contributions in this topic are growing very fast. Several papers evidenced a bubble behaviour in exchange rates between Bitcoin and traditional currencies. In this paper we explore and give validation to such conjecture, proving also that the bubble effect is due to confidence in Bitcoin future values. This means that Bitcoin price/exchange rate is influenced both by future and past events, but that the bubble behaviour is strictly connected to trust on the future of the Bitcoin system.
This paper examines the left and right tail dependence-switching structure between twelve MENA stock markets, and oil and other major global factors. We compare the MENA–oil tail dependence with that of Bitcoin, gold, and VIX. Using a recent combined wavelet and dependence-switching copula approach, we show evidence of significant tail dependence between MENA stock markets and oil and the other global factors. The dependence structure varies across the associated different regimes and under both the short- and long-term horizons. Moreover, the safe haven role of gold is more apparent in the long term than in the short term for all MENA markets, and this result is similar for Bitcoin but is less evident for VIX. We conclude by providing policy implications.
One of the important tasks of every multi-asset portfolio managers is to assess how different asset classes interact with each other. Historical findings indicate that tradition risk asset classes exhibited various degrees of correlation, be they positive or negative, among each other. With the raise of crypto assets, such as bitcoin, it appears that crypto assets have gradually been considering as new investment class, at least from institutional aspect. This study reveals that the correlation of the digital currency with the longest price history, bitcoin, with other traditional assets is close to zero. Thus, from diversification point of view, this makes cryptocurrencies or bitcoin a perfectly uncorrelated asset which would benefit almost any portfolio. Further study is performed to investigate the cointegration relations among bitcoin and other asset classes. It is found that the spreads between bitcoin and some major tradition risk asset classes exist a mean reversion phenomenon. This enables asset managers to develop quantitative approaches for active management strategies. Models of cointegrated time series are common place in the literature and application in financial series. Correlation and cointegration are time series modelling techniques that have applied to financial markets. They are related but with different concepts. Correlation indicates co-movements in returns which is a short run measure requires frequent rebalancing to minimize losses, while cointegration measures long run tandem movements in prices to ensure long term performance for achieving returns. Two pairs of asset prices are found to have a common stochastic trend with stationary cointegrating vector, they are in theory considered for cointegration. This stochastic process displays a mean reversion in long run. If there exists a divergence in spread due to temporary shocks, one can expects to profit from performing pairs trading strategy by creating a short position on the outperforming one, at the same time with a long position on the underperforming one. In this study, trading signal would be generated for our pairs trading with bitcoin. Largely, our results empirically support over various asset classes during the period of estimation.
This present paper investigates day-of-the-week effect in some notable cryptocurrency in terms of pricing and market capitalizations. We applied fractional integration regression approach with dummies. We found non-significance of day-of-the-week effect in returns, while there is possible evidence of Monday and Friday effects in volatility of Bitcoin only. Non-significance of day-of-the-week effect in returns of Bitcoin and some other cryptocurrencies further support market efficiency of these markets.
In this paper, we revisit the stylized facts of cryptocurrency markets and propose various approaches for modeling the dynamics governing the mean and variance processes. We first provide the statistical properties of our proposed models and study in detail their forecasting performance and adequacy by means of point and density forecasts. We adopt two loss functions and the model confidence set (MSC) test to evaluate the predictive ability of the models and the likelihood ratio test to assess their adequacy. Our results confirm that cryptocurrency markets are characterized by regime shifting, long memory and multifractality. We find that the Markov switching multifractal (MSM) and FIGARCH models outperform other GARCH-type models in forecasting bitcoin returns volatility. Furthermore, combined forecasts improve upon forecasts from individual models.
Yu-Min Lian, Chi-Hung Cheng, Shih-Hsun Lin, Jui-Hsuan Lin
In this study, we make use of both the specific method of Monte Carlo simulation and the spot-futures parity with the cost of carry to establish a dynamic price model of Bitcoin futures and to conduct the appraisals and numerical analyses. More specifically, the electricity fees and equipment costs are taken into account and the proposed model is thereby built. Numerical results show that various cost factors have significant effects on the Bitcoin futures price. We employ Monte Carlo simulation to approximate the Bitcoin futures price and we use Python to program the computations.
This paper deals with cryptocurrency bubbles. First, it points out that a number of recent papers on cryptocurrency bubbles are awed due to an insufficient consideration of the fundamental value of cryptocurrencies. As even fiat money is said to exhibit features of bubbles, the same applies to cryptocurrencies. Thus, any empirical investigation into either the presence of cryptocurrency bubbles or the fundamental value of cryptocurrencies is needless. Second, the paper conducts a short empirical analysis into the relationship of the prices of Etherum and Bitcoin. Evidence of explosive periods is found in the price of Etherum even if this price is expressed in terms of Bitcoin rather than US Dollars. These periods, however, are found to be in the first half of 2016 and 2017, respectively, but not during the price peak period of Bitcoin witnessed end of 2017 and beginning of 2018.
This chapter builds on the body of work that has depicted cryptocurrency as a model for science and higher education funding. To that end, this work examines the degree to which one or more cryptocurrencies would need to be adopted and achieve a network effect prior to implementation of such a funding model. Empirical data from three different cryptocurrencies were examined. The current work deploys generalized autoregressive conditional heteroskedasticity (GARCH) to analyze stochastic volatility. This work contends that the examined coins are likely overdistributed and too volatile, thereby limiting the wealth generation possibilities for funding science or higher education. Additionally, based on the GARCH analysis, this work highlights that cryptocurrency pricing metrics and valuation models, to this point, may be insufficiently complex to persuade institutional investors to seriously allocate capital to this ecosphere.
Ingolf Gunnar Anton Pernice, Georg Gentzen, Hermann Elendner
The velocity of money is central to the quantity theory of money, which relates it to the general price level. While the theory motivated countless empirical studies to include velocity as price determinant, few find a significant relationship in the short or medium run. Since the velocity of money is generally unobservable, these studies were limited to using proxy variables, leaving it unclear whether the lacking relationship refutes the theory or the proxies. Cryptocurrencies on public blockchains, however, visibly record all transactions, and thus allow one to measure-rather than approximate -velocity. This paper evaluates most suggested proxies for velocity and also proposes a novel measurement approach. We introduce velocity measures for UTXO-based cryptocurrencies, focused on the subset of the money supply effectively in use for the processing of transactions. Our approach thus explicitly addresses the hybrid use of cryptocurrencies as media of exchange and as stores of value, a major distinction in recently-proposed theoretical pricing models. We show that each of the velocity estimators is approximated best by the simple ratio of on-chain transaction volume to total coin supply. Moreover, "coin days destroyed," if used as an approximation for velocity, shows considerable discrepancy from the other approaches.
Anwar Hasan Abdullah Othman, Syed Musa Alhabshi, Razali Haron
This study investigates whether symmetric and asymmetric volatility effects are persisted in the daily return series of Bitcoin currency compared to the gold and fiat money system using GARCH family models. The symmetric analysis shows that the three monetary systems exhibit time-varying volatility with high persistence and predictability behaviour whereas asymmetric analysis indicates that Bitcoin currency and gold are not significantly respond to asymmetric information effects in the financial markets however, the US dollar index is affected by the positive shocks. This suggesting Bitcoin and gold have the capability for hedging or safe-haven assets against market risk specifically during times of economic turmoil. Evidence suggests that cryptocurrency is a potential alternative to current fiat money system, offering benefit for policy makers and a good investment option for positional investors in terms of hedging, portfolio diversification strategy and risk management.