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.
An analyse of bolivar/bitcoin trading activity indicates that Bitcoin trading in Venezuela is more a reflection of a survival technique rather than an investment strategy with the median transactions price significantly smaller than the medians in the Argentine peso, the Brazilian real and the UK pound. A large number of very small trades point to the practice of exchanging only as many bitcoins as is necessary for immediate use, with inflation as high as 3-4%/day. An analysis of the size of transactions in bitcoins also points to much smaller bitcoin transactions in the bolivar compared to the peso, real and pound. While Bitcoin transactions in the real and the pound indicate a range of trading opportunities from small to large transactions, peso/bitcoin transactions indicate a currency that may be at the start of a journey not dissimilar to the bolivar as transactions start to get smaller in 2018. This analysis supports the anecdotal evidence that bolivar/bitcoin trading is used as a survival technique in a country with a failing economy and worthless fiat currency
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.
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.
Aleš Zamuda, Vincenzo Crescimanna, Juan C. Burguillo, Joana Dias · 12 authors
This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking Initial Coin Offering (ICO) values for incoming cryptocurrencies, trading strategies employing the new Sentiment Analysis metrics, and risk aversion in cryptocurrencies trading through a multi-objective portfolio selection. Our perspective is rationalized on the perspective on elastic demand of computational resources for cloud infrastructures.
This study assessed the volatility and the Value at Risk (VaR) of daily returns of Bitcoins by conducting a comparative study in the forecast performance of symmetric and asymmetric GARCH models based on three different error distributions. The models employed are the SGARCH and TGARCH which were validated based on AIC, MAE and MSE measures. The results indicated that the SGARCHGED (1,1) with generalised error distribution term was identified as the best fitted GARCH model. Though, this best fitted model based on information loss (AIC) did not provide the best out-of-sample forecast, the differences was insignificant. Thus, the study clearly demonstrates that it is reliable to use the best fitted model for volatility forecasting. Also, to further validate the performance of the best fitted model, it was subjected to a historical back-test using Value at Risk (VaR). Though, it was evident from the study that no model was superior, it was indicated that an average loss of 1.2% is expected to be exceeded only 1% of the time. Moreover, volatility forecast from the back testing was relatively high during the first quarter of 2018 but begun decreasing steadily with time.
John Abonongo, Anuwoje Ida Logubayom, Raymond Nero
This paper explores the half-life volatility measure of three cryptocurrencies (Bitcoin, Litecoin and Ripple). Two GARCH family models were used (PGARCH (1, 1) and GARCH (1, 1)) with the student-t distribution. It was realised that, the PGARCH (1, 1) was the most appropriate model. Therefore, it was used in determining the half-life of the three returns series. The results revealed that, the half-life was 3 days, 6 days and 4 days for Bitcoin, Litecoin and Ripple respectively. This shows that, the three coins have strong mean reversion and short half-life and that it takes the respective days for volatility in each of coin to return half way back without further volatility.
Cryptocurrency, the most controversial and simultaneously the most interesting asset, has attracted many investors and speculators in recent years. The visibly significant market capitalization of cryptos also motivates modern financial instruments such as futures and options. Those will depend on the dynamics, volatility, or even the jumps of cryptos. We provide a comprehensive investigation of the risk dynamics of the Bitcoin Market from a realized volatility perspective. The Bitcoin market is extremely risky in the sense of volatility, entangled jumps, and extensive consecutive jumps, which reflect the major incidents worldwide. Empirical study shows that the lagged realized variance increases the future realized variance, while the jumps, especially positive ones, significantly reduce future realized variance. The out-of-sample forecasting model reveals that, in terms of forecasting accuracy and utility gain, investors interested in the long-term realized variance benefit from explicitly modelling the jumps and signed estimators, which is unnecessary for the short-term realized variance forecast.
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 investigate similarities and differences between stock and cryptocurrency networks obtained from log-return and volatility time series. We constructed correlation and Fast Fourier Transform based graphs and minimum spanning trees from a set of 100 highly capitalized cryptocurrencies and 100 highly capitalized NASDAQ stocks over a time window of fixed length. Our analysis is based on comparison between both economies in terms of network properties. We also examined distributions of node degrees and edge weights. Our results show that cryptocurrencies and companies with high capitalization tend to correspond to central and densely connected nodes. Network topologies for both economies and node degree distributions are rather similar. Nevertheless, the crypto-economy is more correlated and more strongly linked to important nodes, unlike the graphs of NASDAQ stocks, where we observed clusters of nodes having small dissimilarities.
The evolution of the energy production and distribution towards innovative decentralized models, dictates the introduction of emerging technologies to transform the conventional energy sector into smart