This research explores the effects of adding bitcoin to an optimal portfolio (naïve, long-only, unconstrained and semi-constrained) by relying on mean-CVaR in the Chinese market. Then backtesting to compare the performance of portfolios with and without bitcoin for each scenario is perfomed. Results show significant but weak correlations between various asset classes and bitcoin, implying a more mature financial profile of bitcoin in China compared to that in the west. Backtesting results show that the effect of adding bitcoin to optimal portfolios is not consistent over the entire out-of-sample period. The naïve and the long-only strategy improved the risk-reward ratio up until the late 2013 price-crash with no significant advantages thereafter. Shorting strategies on the other hand, with or without leverage, fail to produce more efficient portfolios when bitcoin is added, and this is consistent over the entire out-of-sample period. The results also show that semi-annual rebalancing amplifies the advantages of adding bitcoin to most portfolios except for the semi-constrained portfolio, although the weights analysis show significant shifts in weights which might not represent a feasible strategy in realistic scenarios.
In the era of diminishing power from US dollar and increasing competition among world currencies, Bitcoin, as a completely new concept as a medium of exchange, has received increasing attentions over the world. Nowadays, Bitcoin also becomes an investment vehicle, which carries attractive opportunities but also significant risks for the investment community. In this paper, we have compared the empirical performance of a newly-developed heavy-tailed distribution, the normal reciprocal inverse Gaussian (NRIG), with the most popular heavy-tailed distribution, the Student’s t distribution, under the GARCH framework in fitting the daily Bitcoin exchange rate returns. Our results indicate the heavy-tailed distribution has better performance in capture the daily Bitcoin exchange rate returns dynamics than the standard normal distribution. Our results also show the older fashioned Student’s t distribution still performs better than the new heavy-tailed distribution.
Created in 2009, bitcoin reaches record heights every week, having hit $17,000 on 11 December 2017 - the first day a bitcoin futures contract traded at the CBOE - versus $1,000 in early 2017 and $1 in 2001. Yet there is still no consensus among economists whether bitcoin comprises a new decentralised currency free of central bank influence, or is a purely speculative instrument.
The growth of peer-to-peer exchanges and the blockchain technology has led to a proliferation of cryptocurrencies and to a massive increase in the number of investors who actually negotiate digital money. Cryptocurrencies trade at prices mainly driven by investor sentiment, becoming a potential source of financial bubbles and instabilities. In this work, we apply quantitative models to the study of Bitcoin and Ether, two of the most famous cryptocurrencies. Our bubble detection methodology combines the Log Periodic Power Law (LPPL) model, originally created by Johansen, Ledoit and Sornette (JLS), and the statistical model developed by Phillips, Shi, and Yu (PSY). In particular, we employ three different versions of JLS model, i.e. Ordinary Least Square (OLS), Generalised Least Squares (GLS) and Maximum Likelihood Estimation (MLE), and two PSY statistical tests (BSADF and BSADF*). We find that, during the sample period 1st December 2016 - 16th January 2018, Bitcoin shows typical hallmarks of a bubble phase in mid December 2017 and in the first half of January 2018, anticipating the large crashes observed thereafter. Also the Ether price dynamics reveals bubble evidence in mid June 2017, anticipating the crash observed on 12th June, and a weaker signal around 12th January 2018, anticipating the crash observed in the same days. This paper confirms the high risk of speculative bubbles associated with cryptocurrencies, related to investor exuberance pumping market prices far away from their fundamental values, thus creating critical situations subject to possible crashes. Our methodology is general and can be applied to virtually any financial time series, and may support investing and risk management strategies.
Bitcoin is a widely-spread payment instrument, but it is doubtful whether the proof-of-work (PoW) nature of the system is financially sustainable on the long term. To assess sustainability, we focus on the bitcoin miners as they play an important role in the proof-of-work consensus mechanism of bitcoin to create trust in the currency. Miners offer their services against a reward while recurring expenses. Our results show that bitcoin mining has become less profitable over time to the extent that profits seem to converge to zero. This is what economic theory predicts for a competitive market that has a single homogenous good. We analyze the actors involved in the bitcoin system as well as the value flows between these actors using the e3value methodology. The value flows are quantified using publicly available data about the bitcoin network. However, two important value flows for the miners, namely hardware investments and expenses for electricity power, are not available from public sources. Therefore, we contribute an approach to estimate the installed base of bitcoin hardware equipment over time. Using this estimate, we can calculate the expenses miner should have. At the end of our analysis period, the marginal profit of mining a bitcoin becomes negative, i.e., to a loss for the miners. This loss is caused by the consensus mechanism of the bitcoin protocol, which requires a substantial investment in hardware and significant recurring daily expenses for energy. Therefore, a sustainable crypto currency needs higher payments for miners or more energy efficient algorithms to achieve consensus in a network about the truth of the distributed ledger.
Much significant research has been done to investigate various facets of the link between Bitcoin price and its fundamental sources. This study goes beyond by looking into least to most influential factors-across the fundamental, macroeconomic, financial, speculative and technical determinants as well as the 2016 events-which drove the value of Bitcoin in times of economic and geopolitical chaos. We use a Bayesian quantile regression to inspect how the structure of dependence of Bitcoin price and its determinants varies across the entire conditional distribution of Bitcoin price movements. In doing so, three groups of determinants were derived. The use of Bitcoin in trade and the uncertainty surrounding China's deepening slowdown, Brexit and India's demonetization were found to be the most potential contributors of Bitcoin price when the market is improving. The intense anxiety over Donald Trump being the president of United States was shown to be a positive determinant pushing up the price of Bitcoin when the market is functioning around the normal mode. The velocity of bitcoins in circulation, the gold price, the Venezuelan currency demonetization and the hash rate were found to be the fundamentals influencing the Bitcoin price when the market is heading into decline.
Purpose The purpose of this article is to provide some insights on the true nature of bitcoin and to study empirically its performance by using robust models, widely used in the academic literature. Previous studies assess performance with simple measures such as the Sharpe ratio. Such measures are insufficient because they do not take into account the bitcoin’s specificities, such as the possibilities to diversify risk. Design/methodology/approach The authors use quantitative methodologies to assess the performance of financial assets. Performance is defined as a risk-adjusted return. The authors use regression analysis and measure bitcoin’s performance as the constant term ( α ) of the projection of its returns on the returns of relevant factors of risk. Findings Bitcoin has low correlation with the market index and with factor-mimicking portfolios, which indicates opportunities to diversify risk. The performance of bitcoin ( α ) is positive and significant; this result is robust across period and world region specifications. Research limitations/implications The true nature of bitcoin is subject of debate and needs further research. Furthermore, other factors should be considered in analysing the bitcoin’s performance, such as those related to investors’ behaviour or political risk. Practical implications The empirical results obtained in this paper may be used by professional portfolio managers to diversify risk and to enhance their portfolio’s performance. Originality/value This paper adds to the literature by arguing that bitcoin has the nature of common stock, and therefore, its performance has to be assessed with models that are relevant for this type of securities. This paper is the first using performance models that adjust returns for relevant sources of risk.
Alessandra Cretarola, Gianna Figà‐Talamanca, Marco Patacca
In recent literature it is claimed that BitCoin price behaves more likely to a volatile stock asset than a currency and that changes in its price are influenced by sentiment about the BitCoin system itself; in Kristoufek [10] the author analyses transaction based as well as popularity based potential drivers of the BitCoin price finding positive evidence. Here, we endorse this finding and consider a bivariate model in continuous time to describe the price dynamics of one BitCoin as well as a second factor, affecting the price itself, which represents a sentiment indicator. We prove that the suggested model is arbitrage-free under a mild condition and, based on risk-neutral evaluation, we obtain a closed formula to approximate the price of European style derivatives on the BitCoin. By applying the same approximation technique to the joint likelihood of a discrete sample of the bivariate process, we are also able to fit the model to market data. This is done by using both the Volume and the number of Google searches as possible proxies for the sentiment factor. Further, the performance of the pricing formula is assessed on a sample of market option prices obtained by the website deribit.com.
We discuss Russia's underlying motives for issuing its government-backed cryptocurrency, CryptoRuble, and the implications thereof and of other likely-soon-forthcoming government-issued cryptocurrencies to some stakeholders (populace, governments, economy, finance, etc.), existing decentralized cryptocurrencies (such as Bitcoin and Ethereum), as well as the future of the world monetary system (the role of the U.S. therein and a necessity for the U.S. to issue CryptoDollar), including a future algorithmic universal world currency that may also emerge. We further provide a comprehensive list of references on cryptocurrencies.
Digital currencies and cryptocurrencies have hesitantly started to penetrate the investors, and the next step will be the regulatory risk management framework. We examine the Value-at-Risk and Expected Shortfall properties for the major digital currencies, Bitcoin, Ethereum, Litecoin, and Ripple. The methodology used is GARCH modelling followed by Filtered Historical Simulation. We find that digital currencies are subject to a higher risk, therefore, to higher sufficient buffer and risk capital to cover potential losses.
Attempts to accurately measure the monetary velocity or related properties of Bitcoin have often attempted to either directly apply definitions from traditional macroeconomic theory or to use specialized metrics relative to the properties of the Blockchain such as bitcoin-days destroyed. In this paper, it is demonstrated that beyond being a useful metric, bitcoin-days destroyed has mathematical properties that allow one to calculate the average dormancy (time since last use in a transaction) of the bitcoins used in transactions over a given time period. In addition, transaction volume and average dormancy are shown to have unexpected significance in helping estimate the average size of the pool of traded bitcoins by virtue of the expression Little's Law, though only under limited conditions.
The cryptocurrency is a decentralized digital money. Bitcoin is a digital asset designed to work as a medium of exchange using cryptography to secure the transactions, to control the creation of additional units, and to verify the transfer of assets. The objective of this study is to forecast Bitcoin exchange rate in high volatility environment. Methodology implemented in this study is forecasting using autoregressive integrated moving average (ARIMA). This study performed autocorrelation function (ACF) and partial autocorrelation function (PACF) analysis in determining the parameter of ARIMA model. Result shows the first difference of Bitcoin exchange rate is a stationary data series. The forecast model implemented in this study is ARIMA (2, This model shows the value of Rsquared is 0.444432. This value indicates the model explains 44.44% from all the variability of the response data around its mean. The Akaike information criterion is 13.7805. This model is considered a model with good fitness. The error analysis between forecasting value and actual data was performed and mean absolute percentage error for ex-post forecasting is 5.36%. The findings of this study are important to predict the Bitcoin exchange rate in high volatility environment. This information will help investors to predict the future exchange rate of Bitcoin and in the same time volatility need to be monitor closely. This action will help investors to gain better profit and reduce loss in investment decision.
From the Washington University Senior Honors Thesis Abstracts (WUSHTA), 2017. Published by the Office of Undergraduate Research. Joy Zalis Kiefer, Director of Undergraduate Research and Associate Dean in the College of Arts & Sciences; Lindsey Paunovich, Editor; Helen Human, Programs Manager and Assistant Dean in the College of Arts and Sciences Mentors: Mina Lee and Li Yang
Abstract Cryptocurrencies have left the dark side of the finance universe and become an object of study for asset and portfolio management. Since they have low liquidity compared to traditional assets, one needs to take into account liquidity issues when adding them to a portfolio. We propose a Liquidity Bounded Risk-return Optimization (LIBRO) approach, which is a combination of risk-return portfolio optimization under liquidity constraints. Cryptocurrencies are included in portfolios formed with stocks of the S&P 100, US Bonds, and commodities. We illustrate the importance of the liquidity constraints in an in-sample and out-of-sample study. LIBRO improves the weight optimization in the sense that it only adds cryptocurrencies in tradable amounts depending on the intended investment amount. The returns greatly increase compared to portfolios consisting only of traditional assets. We show that including cryptocurrencies in a portfolio can indeed improve its risk–return trade-off.
Joerg Osterrieder, Stephen Chan, Jeffrey Chu, Saralees Nadarajah
We analyze statistical properties of the largest cryptocurrencies (determined by market capitalization), of which Bitcoin is the most prominent example. We characterize their exchange rates versus the U.S. Dollar by fitting parametric distributions to them. It is shown that returns are clearly non-normal, however, no single distribution fits well jointly to all the cryptocurrencies analysed. We find that for the most popular currencies, such as Bitcoin and Litecoin, the generalized hyperbolic distribution gives the best fit, while for the smaller cryptocurrencies the normal inverse Gaussian distribution, generalized t distribution, and Laplace distribution give good fits. The results are important for investment and risk management purposes.
Bitcoin was the first cryptocurrency to use blockchain and has been the market leader since the first bitcoin was mined in 2009. After the birth of Bitcoin with the genesis block, more than 1,000 altcoins and crypto-tokens have been created, with at least 919 trading actively on unregulated or registered exchanges. This entire class of cryptocurrencies and tokens has been classified by some tax authorities as having the same status as commodities. If cryptocurrency is viewed in the same class as commodities, how different is it in terms of its risk and return structure? This article sets out to help readers understand cryptocurrencies and to explore their risk and return characteristics using a portfolio of cryptocurrency represented by the Cryptocurrency Index (CRIX). Substantial discussions are centered on Bitcoin and its close variants. Some questions are raised about the potential of cryptocurrencies as an investment class. Results show that the return correlations between cryptocurrencies and traditional assets are low and that adding CRIX returns to a traditional asset portfolio improves risk–return performance. Sentiment analysis also indicates the CRIX has a relatively high Sharpe ratio. Although we should view the results with care, a new form of financing for cryptocurrency and blockchain start-ups is born. The disruption brought about by Bitcoin may be felt beyond payments through what is known as initial crypto-token offerings or initial token sales. <b>TOPICS:</b>Currency, risk management, performance measurement, mutual funds/passive investing/indexing
Guglielmo Maria Caporale, Luis A. Gil‐Alana, Alex Plastun
This paper examines persistence in the cryptocurrency market. Two different long-memory methods (R/S analysis and fractional integration) are used to analyse it in the case of the four main cryptocurrencies (BitCoin, LiteCoin, Ripple, Dash) over the sample period 2013–2017. The findings indicate that this market exhibits persistence (there is a positive correlation between its past and future values), and that its degree changes over time. Such predictability represents evidence of market inefficiency: trend trading strategies can be used to generate abnormal profits in the cryptocurrency market.