Blockchain Papers

Salim Lahmiri, Stelios Bekiros

No abstract is available for this record.

David Suda, Luke Spiteri

We implement hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs) on Bitcoin/US dollar (BTC/USD) with the aim of market phase detection. We make analogous comparisons to Standard and Poor’s 500 (S and P 500), a benchmark traditional stock index and a protagonist of several studies in finance. Popular labels given to market phases are “bull”, “bear”, “correction”, and “rally”. In the first part, we fit HMMs and HSMMs and look at the evolution of hidden state parameters and state persistence parameters over time to ensure that states are correctly classified in terms of market phase labels. We conclude that our modelling approaches yield positive results in both BTC/USD and the S and P 500, and both are best modelled via four-state HSMMs. However, the two assets show different regime volatility and persistence patterns—BTC/USD has volatile bull and bear states and generally weak state persistence, while the S and P 500 shows lower volatility on the bull states and stronger state persistence. In the second part, we put our models to the test of detecting different market phases by devising investment strategies that aim to be more profitable on unseen data in comparison to a buy-and-hold approach. In both cases, for select investment strategies, four-state HSMMs are also the most profitable and significantly outperform the buy-and-hold strategy.

Krishna Chakravarty, Manjusha Pandey, Siddharth Swarup Routaray

No abstract is available for this record.

Vasyl Derbentsev, Halyna Velykoivanenko, Natalia Datsenko

No abstract is available for this record.

Philip Nadler, Yike Guo

With the rise of cryptocurrency tokens as a new asset class, the question of the fair evaluation of a cryptocurrency token has become a question of increasing importance. We estimate the pricing kernel with which users price factors affecting their token holdings. We investigate how traditional risk factors such as market risk are evaluated, as well as how blockchain specific risk factors are priced in. In order to do so, we introduce an asset pricing model and modify its properties to make it applicable to cryptocurrency markets. We group the risk factors into market related and Bitcoin- and Ethereum blockchain specific risk factors. We find that blockchain specific risk factors are priced in. There is evidence that risk factors have moved from Bitcoin to Ethereum specific risk factors with an increasing importance of market factors, providing evidence for a decoupling of on-chain and off-chain trading activity.

Kirill Polovnikov, Vlad Kazakov, Sergey Syntulsky

No abstract is available for this record.

Leandro Maciel, Rosângela Ballini

Bitcoin has attracted the attention of investors lately due to its significant market capitalization and high volatility. This work considers the modeling and forecasting of daily high and low Bitcoin prices using a fractionally cointegrated vector autoregressive (FCVAR) model. As a flexible framework, FCVAR is able to account for two fundamental patterns of high and low financial prices: their cointegrating relationship and the long memory of their difference (i.e., the range), which is a measure of realized volatility. The analysis comprises the period from January 2012 to February 2018. Empirical findings indicate a significant cointegration relationship between daily high and low Bitcoin prices, which are integrated on an order close to the unity, and the evidence of long memory for the range. Results also indicate that high and low Bitcoin prices are predictable, and the fractionally cointegrated approach appears as a potential forecasting tool forcryptocurrencies market practitioners.

Jin-Bom Han, Sun-Hak Kim, Myong-Hun Jang, Kum-Sun Ri

No abstract is available for this record.

Muhammad Abubakr Naeem, Elie Bouri, Gideon Boako, David Roubaud

No abstract is available for this record.

Johnny K.H. Kwok

We investigate the long run relationship and the short run dynamic between the price and the NAV of Bitcoin Investment Trust (BIT), the world’s first publicly traded bitcoin fund. We examine whether the price and the NAV are cointegrated. We also investigate how the price and the NAV adjust to short-term deviations from long run relationship. Our results find that both the price and the NAV are cointegrated despite significant price premium over the NAV. Further, we find that the BIT prices adjust partially to the short-term deviations while the NAV does not react. The results may provide valuable insight to regulatory bodies when they consider the approval of bitcoin or cryptocurrency ETFs.

Hao Dong, Liming Chen, Xinyi Zhang, Pierre Failler · 5 authors

In this paper, we measure the asymmetric volatility spillover among six virtual financial asset (VFA) markets from January 1, 2014, to September 30, 2017, using the volatility spillover index based on a Markov regime-switching vector autoregressive (VAR) model and conduct a static and dynamic analysis under different regimes. The static results show that asymmetric effects of total, internal and net volatility spillover, on average, exist in all six VFA markets under different regimes. The dynamic results show that total, directional, and net spillover have significantly asymmetric effects. Thus, the government should monitor the specific VFA regimes and improve market regulation.

Hayet Ben Haj Hamida, Francesco Scalera

In this paper we explores as to whether cryptocurrency returns exhibit asymmetric reverting patterns and we test the presence of regime changes in the GARCH volatility dynamics of Bitcoin log-returns. For these reason, we uses non-linear autoregressive and Markov-switching GARCH (SETAR-MSGARCH) models. We finds strong evidence of regime changes in the mean and GARCH process. In addition, we conclude that bad news and good news of the same size have same impacts for investors.

Hui-Pei Cheng, Kuang‐Chieh Yen

No abstract is available for this record.

Anwar Hasan Abdullah Othman, Adam Abdullah, Razali Haron

As crypto-currencies hold dual nature of a medium of exchange (currency) and an investment asset, some questions may arise about the potentiality of including crypto-currencies as liquid investment asset in financial institutions particularly in the banking sector to enhance their liquidity risk management and improve their portfolio diversification investment strategy. The objective of this study therefore is to examine the characteristics of Bitcoin currency based on the requirements of High-Quality Liquid Assets (HQLA) standards of Basel III and compare its volatility structure with other traditional asset classes that are already recommended by Basle III as HQLA. The study utilizes both descriptive and quantitative analysis using the GARCH family models to examine the volatility structures of these assets. The findings show that Bitcoin currency holds the same characteristics of HQLA, however; the risk of legality and recognition is still under consideration by legal authorities around the world and this risk will be eradicated in the future as crypto-currencies derive their legality from their real intrinsic value, multi-economic usefulness and not by law as in the case of fiat money currency. Furthermore, the symmetric volatility structure analysis shows the continuing persistence of volatility and predictability behavior in return series of Bitcoin currency and other- traditional asset classes in the U.S. market. However, Bitcoin’s stability has gradually improved over time. With regard to the asymmetric informative response, Bitcoin returns respond more to negative shock but it has no statistical significance, thus suggesting the lack of leveraging effect in Bitcoin market but this effect was found to be statistically persistent in other traditional asset class markets. In addition, Bitcoin returns show very low correlation with other traditional asset classes. All these imply that Bitcoin is a potential candidate as a hedge and asset diversifier, which is recommended to be included in the HQLA. This study provides some support to recent theoretical work on crypto asset return behaviour and liquidity risk management. The findings provide appropriate information about Bitcoin asset behaviour compared to other traditional asset classes which will enable them to make the right investment decision with regard to hedging, diversification and liquidity risk management. The findings of this study may assist in evaluating the suitability of including crypto assets into HQLA to improve the liquidity requirement standards and ensure that banks have an adequate amount of HQLA specifically during times of financial turmoil.

Xiao Fan Liu, Zeng-Xian Lin, Xiao-Pu Han

Thousands of cryptocurrencies have been issued and publicly exchanged since Bitcoin was invented in 2008. The total cryptocurrency market value exceeds 300 billion US dollars as of 2019. This paper analyzes the prices, volumes, blockchain transactions, coin difficulties and public opinion popularities of 3607 actively exchanged cryptocurrencies. We aim to reveal and explain the homogeneity, i.e., the strong correlation of market performance, and the heterogeneity, i.e., the imbalance of popularities and sophistications, of the cryptocurrencies.

Aviral Kumar Tiwari, Adeolu O. Adewuyi, Claudiu Tiberiu Albulescu, Mark E. Wohar

No abstract is available for this record.

Alexander Kuchansky, Аndrii Biloshchytskyi, Yurii Andrashko, Svitlana Biloshchytska · 6 authors

A fractal analysis of the Bitcoin time series for the period from 2012 to 2019 is carried out: Hurst exponents were calculated, the behavior of this indicator in dynamics was investigated, V-statistics were plotted. For the automatic determination of the average length of the nonperiodic cycle in the information system of time series analysis, the smoothing method of V-statistics based on the Kaufman's adaptive moving average and simple moving average with different periods is proposed. The results fractal analysis of the time series of the Bitcoin cryptocurrency price show that the Bitcoin market is characterized by an inescapable efficiency, i.e. periods of effectiveness are replaced periods of inefficiency. This is manifested by changing the type of time series of Bitcoin prices from persistence to random and antipersistence, especially during periods of intense price growth, due to the significant influence on the mechanism of generation of time series of random factors.

Zhaoying Qiao, Tianrui Chai, Jialu Gu, Xinyi Zhou · 6 authors

Bitcoin, a leading cryptocurrency in the financial market, is full of non-linearity, non-stationarity and high volatility. To make risk management strategies, emphasis on cryptocurrency price predicting is truly needy. However, studies about cryptocurrency prediction are lacking. In this paper, a novel hybrid model combining long short-term memory (LSTM), a state-of-the-art sequence learning method, with singular spectrum analysis (SSA) was proposed to predict Bitcoin price. SSA was employed to decompose the original time series into independent signals in term of trend, market fluctuation and noise. A smoothed series with valid information was reconstructed with reduction of noise. By introducing the smoothed series sequence into LSTM, prediction value is obtained. Empirical analysis shows that the proposed hybrid SSA-LSTM model outperforms baseline single LSTM model, according to root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The result suggests that the proposed hybrid model has satisfactory ability to grasp pattern of Bitcoin price series since SSA can extract valid information from the original series and avoid overfitting.

Chamil W. Senarathne

This article examines whether the investment strategies of cryptocurrency market involve high-risk gambling. Results show that the cryptocurrency risk premiums co-move closely with the return on CBOE Volatility Index (VIX). As such, the strategies of cryptocurrency trading closely resemble that of high-risk gambling. In other words, traders' expectations co-move closely (significantly) with the expected future payoffs from gambling. The co-movement is more pronounced when the gambling offers gains rather than losses and the payoffs are above average. VIX index returns significantly Granger-cause CSAD of returns (with and without Bitcoin) indicates that the cryptocurrency trading constitutes a form of gambling where the motivation for gambling comes from the amount of variation (i.e. riskiness) in the gambling payoffs. These findings warrant policymakers of countries to revisit the existing regulatory framework governing the conduct of electronic finance in the financial services industry.

Juan Guillermo Lazo Lazo, Gonzalo Herrera, Alanpierre Vargas Guevara, Alvaro Talavera · 6 authors

Cryptocurrencies, since its creation, have evolved by attracting investors (companies, financial institutions and individuals) that seek significant results, based on the great value of these. However, the finance market is characterized at the level of volatility and uncertainty, which leads to the ups and downs in the price, facing these difficulties, the investment manager must make decisions. This paper proposes a methodology to support decision-making in the investment management in the cryptocurrencies market, adopting a conservative investment position that should reduce risk and maximize the return on investment. The methodology seeks from the historical price of the cryptocurrencies the estimation of the transitions probabilities of the returns and establish levels, this is done based on the analysis of the Markov chains, which is integrated into the multiple decision trees to identify the cryptocurrency that projects a greater return when is sold one and two periods after having been acquired. The results are compared with the real data and the efficiency of the methodology for the support to the decisions in the management of the investment in the cryptocurrencies is checked.

Ur Koumba, Calvin Mudzingiri, Jules Mba

This study is confined in analysing how the economic policy uncertainty (EPU) effects affect exchange rates on cryptocurrency assets in times of financial turbulence characterized by low confidence in the financial stock markets, and tranquil periods where the financial stock markets behave smoothly. Our research employs the D-Vine pair-copula method on daily selected cryptocurrency (Bitcoin, Ethereum and Ripple) prices within the period of the 10 August 2016 to the 23 February 2018. Our findings document the presence of the dependence between the US EPU and cryptocurrencies and indicate a significant correlation with Ethereum which exhibits a much better return.

Mária Bohdalová, Michal Greguš

Nowadays Bitcoin as cryptocurrency takes a significant place on the global financial markets. This paper analyzes the Bitcoin closing prices and traded volume during the period from December 28, 2013 to January 22, 2019. This period is known as a period with rapid increasing of the Bitcoin closing prices, mainly in the second half of the year 2017. The aim of this paper is twofold. First, we compute the Hurst coefficient to discover the close price dynamics and traded volume using a fractal point of view. We have discovered an anti-persistent behavior in the traded volume and random character of bitcoin closing prices. Second, we propose an analysis of the relationship between the close prices and traded volume. Our findings show how changes in the high-price period differ from changes in the low-price period. We also found that high prices caused investors to be afraid to trade due to possible rapid decrease in bitcoin closing prices.

Katarzyna Włosik

Bitcoin can be exchanged for other cryptocurrencies as well as for fiat currencies on many different platforms. Nevertheless, its real convertibility may be limited by market liquidity. The main aim of this article is to characterize and compare big and small bitcoin markets in terms of liquidity. I examine four platforms with high trade volume: Kraken, Bitstamp, BitFlyer and BTCBOX, as well as small entities which enable bitcoin to be traded in Polish zloty: BitBay and BitMarket. I compare the number of trades and the time between trades on selected bitcoin markets, determine the volume distribution throughout the day and analyse the dynamics of Amihud’s illiquidity measure – ILLIQ. I find that an exchange which is among the global leaders in terms of trading bitcoin in a particular traditional currency can be considered a smaller market in terms of trade volume in another traditional currency. Moreover, the results imply that BitBay and BitMarket can be perceived as local markets. They are mainly used for trading in Polish zloty, and are illiquid in terms of trading in the remaining traditional currencies. Home bias, the fact that they offer a possibility of trading in a less popular currency (in comparison to the world reserve currencies), and that have their interface in Polish, may give these platforms a competitive advantage.

Paraskevi Katsiampa, Κωνσταντίνος Μουτσιάνας, Andrew Urquhart

No abstract is available for this record.