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.
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.
Despite calls for regulation in the crypto utility token market, it is unclear how crypto token investors value current regulatory proposals. We find that on average, investors react negatively to news that increases the likelihood of securities and transparency-related regulation. We also find that this negative reaction is attenuated for tokens rated higher on quality and transparency by intermediaries, those that have higher levels of disclosure, and listed on more liquid exchanges. The observed variation in token transparency and this muted reaction suggest investors perceive disclosure costs to be lower for tokens in more transparent environments, suggesting that transparency matters to investors.
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.
In 2008 a group of programmers, alias Satoshi Nakamoto, introduced bitcoin. Bitcoin is a cryptocurrency \nor virtual money derived from mathematical cryptography and is conceived as an alternative to government authorised \ncurrency. The founder anticipated, through bitcoin’s construction and his digital mining processes, that bitcoin prices \nwould be relatively stable. However, the recent bitcoin price decline proves that bitcoin is extraordinarily volatile and is \nnot that stable as hoped. Although some scientists have already shown that the fundamental value of bitcoin is zero, the \nprice of bitcoin has reached over 19.000$ in December 2018. Since then, bitcoin prices dropped nearly 70% from their \npeak value and showed in addition to that the typical trends of a speculative bubble. \nHyman Minsky and Charles Kindleberger discussed three different patterns of speculative bubbles. One is when price \nrises in an accelerating way and then crashes very sharply after reaching its peak. Another is when the price rises and is \nfollowed by a more similar decline after reaching its peak. The third is when the price rises to a peak, which is then \nfollowed by a period of gradual decline known as the period of financial distress, to be followed by a much sharper crash \nat some later time. One of the key findings of this study is that all these three patterns occurred during 2017-18 for the \nbitcoin price. \nTherefore, the purpose of this paper is to analyse the historical bitcoin prices in context with the typical five-step \ncharacteristics of a speculative bubble. Furthermore, each phase of a speculative bubble is explained by a behavioural \nfinance approach and answer the price development of this cryptocurrency. The result is frightening, bitcoin can be seen \nas a perfect textbook example of a speculative bubble.
This project uses software development to investigate the link between software and finance. The focus of the work is developing and implementing a trading algorithm which seeks to make profit by making trades based on arbitrage opportunities between currencies. Specifically, the sets of currencies examined are two fiat currencies and one cryptocurrency. Trades are made by combining a blockchain system, which maintains the cryptocurrency, and the live foreign exchange market, which enables fiat currency exchange. The main methodologies for carrying out the research are test-driven development and the use of a simulation to facilitate trades. By passing all of the unit tests, the software is verified. In addition, data gathered during runs of the simulation show that the algorithm successfully identifies arbitrage opportunities and turns a profit on average over many runs. This project proposes an interesting topic for further research in the field of blockchain technology used for financial trading.
This paper studies 60 months of recent returns to examine relationships between bitcoin and 16 exchange- traded funds of currencies, bonds, stocks, commodities, and alternative assets. Bitcoin provides much higher returns, positive skewness, volatility and extreme returns, than all the other assets. Only stocks offer a better risk-return tradeoff than bitcoin. Bitcoin returns have very weak positive correlations with stocks, commodities, and alternatives. Only two funds of stocks and commodities have significant explanatory power of about 3% each for bitcoin returns. The full model of all the 16 funds explains only 15.09% of bitcoin returns. A partial model, with the six funds that are significant in the full model, explains 12.78% of bitcoin returns; 3 stock funds and 1 commodity fund have significant coefficients in this model. These findings indicate that bitcoin is a unique asset which is only weakly related to stocks and commodities. The results also show that small allocations to bitcoin improve the risk-return tradeoffs of stock and bond portfolios.
We conduct a detailed analysis of investors in successful initial coin offerings (ICOs). The average ICO has 4700 contributors. The median participant contributes small amounts and many investors sell their tokens before the underlying product is developed. Large presale investors obtain tokens at a discount and flip part of their allocation shortly after the ICO. ICO contributors lack the protections traditionally afforded to investors in early-stage financing. Nevertheless, returns 9 months after the ICO are positive on average, driven mostly by an increase in the value of the Ethereum cryptocurrency.
This paper explores whether asset market equilibria in cryptocurrency markets do exist. In doing so, it distinguishes between privacy and non-privacy coins. Most recently, privacy coins have attracted increasing attention in the public debate as non-privacy cryptocurrencies, such as Bitcoin, do not satisfy some users’ demands for anonymity. Analyzing ten cryptocurrencies with the highest market capitalization in each submarket in the 2016–2018 periods, we find that privacy coins exhibit a distinct market equilibrium. Contributing to the current debate on the market efficiency of cryptocurrency markets, our findings provide evidence of market inefficiency. Moreover, the asset market equilibrium of privacy coins appears to originate from non-privacy coins with highest market capitalizations. We argue that the reason for this finding could be that non-privacy coins may be the first choice for criminals who might prefer cryptocurrencies exhibiting both a high level of anonymity and liquidity.
We take this question to be isomorphic to, "What Keeps Fixed Exchange Rates Fixed?" and address it with analysis familiar in exchange-rate economics. Stablecoins solve the volatility problem by pegging to a national currency, typically the US dollar, and are used as vehicles for exchanging national currencies into non-stable cryptocurrencies, with some stablecoins having a ratio of trading volume to outstanding supply exceeding one daily. Using a rich dataset of signed trades and order books on multiple exchanges, we examine how peg-sustaining arbitrage stabilizes the price of the largest stablecoin, Tether. We find that stablecoin issuance, the closest analogue to central-bank intervention, plays only a limited role in stabilization, pointing instead to stabilizing forces on the demand side. Following Tether's introduction to the Ethereum blockchain in 2019, we find increased investor access to arbitrage trades, and a decline in arbitrage spreads from 70 to 30 basis points. We also pin down which fundamentals drive the two-sided distribution of peg-price deviations: Premiums are due to stablecoins' role as a safe haven, exhibiting, for example, premiums greater than 100 basis points during the COVID-19 crisis of March 2020; discounts derive from liquidity effects and collateral concerns.