Blockchain Papers

Zaghum Umar, Mariya Gubareva, Тамара Теплова, Dang Khoa Tran

No abstract is available for this record.

Yunus Karaömer

Purpose This study aims to analyze the time-varying correlation between the cryptocurrency policy uncertainty (UCRY Policy) and cryptocurrency returns. More specifically, it analyzes whether these correlations vary according to the uncertainty attributable to salient events such as China banning ICOs, cryptocurrency exchanges attacks, Coronavirus (Covid-19) pandemic crisis and the United States (U.S.) Security and Exchange Commission’s (SEC’s) announcement about Ripple. Design/methodology/approach To measure the dynamic relationship, it uses the dynamic conditional correlation (DCC) model of Engle (2002) to consider time variation in UCRY Policy and cryptocurrency returns. The data set encompasses the weekly frequency data of the UCRY Policy and Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Ripple (XRP), Stellar (XLM), Dash (DASH), Monero (XMR) from 4 September 2016, to 21 February 2021. Findings Empirical findings indicate that the correlations between the UCRY Policy and the BTC, ETH, LTC, XRP, XLM, DASH and XMR returns are consistently negative. Thus, an increase in the volatility of the UCRY Policy can lead to a decrease in volatility for BTC, ETH, LTC, XRP, XLM, DASH and XMR returns. Besides, these findings indicate that the estimated DCC is not only time-varying but also substantially responsive to salient events, such as China banning ICOs, cryptocurrency exchanges attacks, the Covid-19 pandemic crisis and SEC’s announcement about Ripple. Besides, empirical findings show that cryptocurrency returns are adversely impacted by UCRY Policy during the salient events (China bans ICOs, the hack of cryptocurrency exchanges, Covid-19 crisis), suggesting their failure to act as a hedge or safe-haven asset. Originality/value To the best of the author’s knowledge, this study investigates the time-varying correlation between UCRY Policy and cryptocurrency returns. Besides, this study may be useful for new studies and fill a gap in the finance literature, due to the limited number of studies on the UCRY Policy in the finance literature.

Khaled Mokni, Ahmed Bouteska, Mohamed Sahbi Nakhli

No abstract is available for this record.

Yunus Doğaç Arık, Melik Ertuğrul

Abstract Beginning from the onset of the Covid-19 pandemic, crypto assets have intensely been in the spotlight and have attracted significant investor attention. By being the first blockchain product, Bitcoin is the first crypto asset and still dominates the entire crypto market capitalization. In this study, we shed light on whether this energy-hungry crypto asset is an effective tool for portfolio volatility reduction from the perspective of the Modern Portfolio Theory. Based on a two-year period from April 2019 to April 2021, which includes the extreme impacts (crash and rally) of the pandemic on markets, we conclude that Bitcoin is not a beneficial instrument for volatility reduction if short-selling is not allowed. After removing this restriction, Bitcoin has very small negative investment weights in minimum variance portfolios. In other words, short-selling Bitcoin slightly reduces portfolio volatility.

Rui Ma, Ben R. Marshall, Nhut H. Nguyen, Nuttawat Visaltanachoti

Bitcoin is becoming a popular financial asset and means of transacting. However, little is known about an important aspect of the bitcoin market: its liquidity. We consider whether various dimensions of liquidity evident in other asset classes are present in bitcoin spot and futures liquidity. We find variations in spot liquidity across bitcoin exchanges and a strong commonality in bitcoin spot and futures market liquidity. The pricing of spot and futures bitcoin is relatively inefficient, and liquidity plays an important role. Deterioration in liquidity also contributes to bitcoin crash risk and large return declines. JEL Classification: G11, G23

Yavuz Gül

This paper investigates the causality and cointegration relationships between seven major cryptocurrencies, namely Bitcoin (BTC), Binance Coin (BNB), Cardano (ADA), Dogecoin (DOGE), Ethereum (ETH), Polkadot (DOT) and Ripple (XRP), using Johansen Cointegration and Granger Causality tests over the period from August 21, 2020 to April 19, 2021. Results indicate that there exists cointegration among cryptocurrencies in the long run. Findings also show that there is a bi-directional causal relationship between BNB and ETH. Additionally, BNB appears to be Granger cause of ADA, DOGE and DOT. On the other hand, analyses provide evidence of one-way causality running from XRP to both DOGE and DOT. These results might have some important implications for investors in terms of portfolio management.

Chun Tang, Xiaoxing Liu

We use a time-varying vector autoregressive model to investigate the dynamic effect of investor attention on Bitcoin speculation and then examine the association of this effect with five types of events in the Bitcoin market. The results indicate that investor attention has a positive effect on Bitcoin speculation and this effect changes with time and decays as lag phases increase. Policy-related events are the key factors that make this effect time-varying, while safety events have no obvious impact. Besides, our results find the existence of contrarian strategy in the Bitcoin market.

Khumbulani L. Masuku, Thabo J. Gopane

Purpose The study considers time-varying risk premium in investigating the capability of technical analysis (TA) to predict and outperform a buy–hold strategy in Bitcoin exchange rate returns. Design/methodology/approach The study tests the technical trading rule of fixed moving average (FMA) on daily actual and equilibrium returns of Bitcoin exchange rates. The equilibrium returns are computed using dynamic CAPM in conjunction with a VAR-MGARCH (1, 1) system. The empirical evaluation of the study uses a case study of four Bitcoin exchange rates (BTC/AUD, BTC/EUR, BTC/JPY and BTC/ZAR) for the period 19 June 2010 to 30 October 2020. Findings The findings are consistent with related studies in conventional foreign exchange markets that find TA to be profitable, especially in emerging markets. Nevertheless, the consideration of risk premium has the effect of reducing the abnormal returns. Also, further robust tests reveal that Bitcoin returns possess a momentum effect which prompts further study in efficient market hypothesis research. Practical implications The empirical findings of this study should benefit portfolio managers and active investors on the strength of TA to predict returns in a speculative market like the Bitcoin exchange rate market. Originality/value The study takes cognisance that cryptocurrency trading is speculative in nature which renders it a good candidate for TA methods. While there are studies that have explored the value of TA in Bitcoin exchange rates, these studies fail to incorporate the effects of time-varying risk premiums, the strength and focus of the current paper.

Toan Huynh

In the decade following the 2008 financial crisis, the coronavirus viral disease 2019 (COVID-19) pandemic and United States (US) President Trump’s Twitter account became representations of market uncertainty, attracting the financial research of (Goodell, 2020; Benton and Philips, 2020). Due to the popularity of these events and their impact on financial markets, many unanswered questions still persist, particularly, how the financial structure has changed during this unique time. The popularity of Bitcoin, one of the main cryptocurrencies, has caused a controversial topic to arise in recent academic research, namely, whether its function compares to that of conventional precious metals such as gold and platinum. This doctoral thesis aims to fill this research gap in two ways: (i) by addressing market reactions to the COVID-19 pandemic and political news by answering the question of how US legislators traded at an industry level during the ongoing COVID-19 pandemic, and how Trump’s Twitter account could shake the equity market during a trade war, and (ii) by examining the power of the gold and platinum ratio, which was first studied by (Huang and Kilic, 2019) ), in predicting Bitcoin as well as how political sentiment could drive the returns, volatility, and volume of this cryptocurrency. This thesis contributes to the empirical evidence in the areas mentioned above due to the growing attention on the financial function of cryptocurrency, the debatable effects of political news regarding the use of social media, and the eventual and unprecedented scale of the COVID-19 pandemic.

Çağrı Hamurcu

The main purpose of this study is to examine the effects of Elon Mask's Twitter posts about cryptocurrencies on cryptocurrency markets within the scope of herding behavior bias. For this purpose, the daily price values and transaction volumes of Bitcoin and Dogecoin are analyzed by applying the EGARCH models. The results show that Elon Musk's positive Twitter posts increase dogecoin's volatility more than bitcoin in terms of price and trading volume. In addition, the effect of positive tweets has been found to increase Bitcoin and Dogecoin prices and their market transactions. According to the results, while negative tweet sharing negatively affects bitcoin returns, it manifests itself with an increase in volatility after a certain period of time. Another result is that the Dogecoin return and negative tweet interaction vary according to time intervals, but the presence of the effect on volatility cannot be determined. It is also concluded that after the negative tweet, both bitcoin and dogecoin transaction volumes increased in the first days, but their volatility was not affected. The results are important in terms of showing the effects of an influential person's social media posts on the financial markets by creating a herd behavior effect. Revealing the "influential person effect" as a behavioral finance bias is seen as the originality of the study. It is thought that the findings can be evaluated in terms of pointing out a factor that may pose a potential risk to financial stability in the global sense.

Cristian Pinto‐Gutiérrez, Sandra Gaitán, Diego Jaramillo, Simón Velasquez

Non-fungible tokens (NFTs) can be used to represent ownership of digital art or any other unique digital item where ownership is recorded in smart contracts on a blockchain. NFTs have recently received enormous attention from both cryptocurrency investors and the media. We examine why NFTs have gotten so much attention. Using vector autoregressive models, we show that Bitcoin returns significantly predict next week’s NFT growth in popularity, measured by Google search queries. Moreover, wavelet coherence analysis suggests that Bitcoin and Ether returns are significant drivers of next week’s attention to NFTs. These results indicate that the remarkable increases in prices of major cryptocurrencies can explain the hype around NFTs.

Riccardo De Blasis, Alexander Webb

Abstract Perpetual futures, first proposed by Shiller (1993), have only seen wide use in cryptocurrency markets. We examine the contract design and market microstructure differences for the behavior of Bitcoin quarterly and perpetual futures prices and assess the implications for market participants and policymakers. We find perpetual futures exhibit multiple “u‐shaped” curves, seasonal effects, and opening effects despite lacking opening and closing hours. There is suggestive evidence of spillover effects between perpetual and quarterly futures contracts. We find quarterly futures offer cash‐and‐carry arbitrage opportunities, but similar to Hattori and Ishida (2021) these opportunities primarily exist during market dislocations.

Mohsen Asgari, Seyed Hossein Khasteh

Deep Reinforcement Learning solutions have been applied to different control problems with outperforming and promising results. In this research work we have applied Proximal Policy Optimization, Soft Actor-Critic and Generative Adversarial Imitation Learning to strategy design problem of three cryptocurrency markets. Our input data includes price data and technical indicators. We have implemented a Gym environment based on cryptocurrency markets to be used with the algorithms. Our test results on unseen data shows a great potential for this approach in helping investors with an expert system to exploit the market and gain profit. Our highest gain for an unseen 66 day span is 4850 US dollars per 10000 US dollars investment. We also discuss on how a specific hyperparameter in the environment design can be used to adjust risk in the generated strategies.

Jan-Oliver Strych

No abstract is available for this record.

Sitara Karim, Muhammad Abubakr Naeem, Nawazish Mirza, Jéssica Paule-Vianez

Purpose This study quantified the hedge and safe haven features of bond markets for multiple cryptocurrency indices from June 2014 to April 2021 to highlight whether bond markets offer hedging facilities to uncertainty indices of cryptocurrencies. Design/methodology/approach The authors employed the methodology of Baur and McDermott (2010) and AGDCC-GARCH model to measure the hedge and safe-haven characteristics of three bond markets (BBGT, SPGB and SKUK) for three uncertainty indexes of cryptocurrencies (UCRPR, UCRPO and ICEA). Findings The authors find that bond markets are neither hedge nor safe havens except for SKUK which is a safe haven investment for cryptocurrency indices and offers substantial diversification during the periods of economic fragility. In addition, the hedge effectiveness of SPGB outperforms other bonds during crisis periods and provides sufficient diversification potential for cryptocurrency indices. Practical implications The findings are important for policymakers, regulatory bodies, financial firms and investors in assessing hedge and safe haven characteristics of bond markets against cryptocurrency indices. Originality/value Employing the novel methodology of AGDCC-GARCH with three different bond markets and three uncertainty indices of cryptocurrencies, the current study adds to the existing strand of literature in terms of quantifying hedge and safe-haven attributes of bond markets for cryptocurrency uncertainty indexes.

Tao Tang, Yanchen Wang

No abstract is available for this record.

Mieszko Mazur

Bitcoin market capitalization recently surpassed $1 trillion. While popular belief holds that a key characteristic of bitcoin is its excessive volatility, this article provides evidence that this is largely a misperception. We show that bitcoin return fluctuations are lower than those of roughly 900 stocks in the S&P1500 and 190 stocks in the S&P500. Moreover, we find that bitcoin is less volatile than commodities such as oil and silver, US Treasuries, AAA-rated corporate bonds, EU carbon credits, and some of the most popular technology and media stocks, including Apple, Twitter, and Netflix. Equally important, we find that during the March 2020 stock market crash triggered by COVID-19, the volatility of bitcoin was lower than that of most of these asset classes. The significant decline in bitcoin volatility over the past decade renders it more investable for conservative investors.

Feng Han, Shuai Ma, Jiheng Zhang

Financial data are expensive and highly sensitive with limited access. We aim to generate abundant datasets given the original prices while preserving the original statistical features. We introduce the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) into the field of the stock market, futures market and cryptocurrency market. We train our model on various datasets, including the Hong Kong stock market, Hang Seng Index Composite stocks, precious metal futures contracts listed on the Chicago Mercantile Exchange and Japan Exchange Group, and cryptocurrency spots and perpetual contracts on Binance at various minute-level intervals. We quantify the difference of generated results (836,280 data points) and original data by MAE, MSE, RMSE and K-S distances. Results show that WGAN-GP can simulate assets prices and show the potential of a market simulator for trading analysis. We might be the first to look into multi-asset classes in a systematic approach with minute intervals across stocks, futures and cryptocurrency markets. We also contribute to quantitative analysis methodology for generated and original price data quality.

Lehlohonolo Letho, Grieve Chelwa, Abdul Latif Alhassan

Purpose This paper examines the effect of cryptocurrencies on the portfolio risk-adjusted returns of traditional and alternative investments within an emerging market economy. Design/methodology/approach The paper employs daily arithmetic returns from August 2015 to October 2018 of traditional assets (stocks, bonds, currencies), alternative assets (commodities, real estate) and cryptocurrencies. Using the mean-variance analysis, the Sharpe ratio, the conditional value-at-risk and the mean-variance spanning tests. Findings The paper documents evidence to support the diversification benefits of cryptocurrencies by utilising the mean-variance tests, improving the efficient frontier and the risk-adjusted returns of the emerging market economy portfolio of investments. Practical implications This paper firmly broadens the Modern Portfolio Theory by authenticating cryptocurrencies as assets with diversification benefits in an emerging market economy investment portfolio. Originality/value As far as the authors are concerned, this paper presents the first evidence of the effect of diversification benefits of cryptocurrencies on emerging market asset portfolios constructed using traditional and alternative assets.

Segun Kamoru Fakunmoju, Olawale Banmore, Abiodun Gbadamosi, Olajide Idowu Okunbanjo

Digital financial trading has brought a new dimension of financial technology transactions to the globe. Cryptocurrency trading is one of the new dimensions. However, cryptocurrency trading is plagued with unlawful and monetary corrupt practices, unregulated foreign currency markets, and unknown party participants. Thus, it creates the unpredicted challenge of instigating fear in the investors’ minds and scaring away economic agents, and in turn, it adversely affects economic activities. The research investigated the effects of cryptocurrency on the performance of the Nigerian economy. The specific objective was to examine the effect of cryptocurrency trading and monetary and monetary corrupt practices on Nigerian economic performance. The research used primary data through 98 copies of the questionnaire. Tobit regression method of analysis was applied to analyze the data. The finding reveals that cryptocurrency and monetary and monetary corrupt practices have a negative but significant effect on Nigerian economic performance with marginal effects of -0,172 and -0,734 with P < 0,05 as the significance level. The research concludes that cryptocurrency and monetary corrupt practices affect Nigerian economic performance. The research recommends that the government, through the Central Bank of Nigeria (CBN), should regulate and control cryptocurrency trading by using global digital financing system software. The software will monitor and control cryptocurrency trading in Nigeria to enhance cryptocurrency trading to contribute to and increase Nigerian economic activities.

Zehra Eksi, Daniel Schreitl

The Bitcoin market exhibits characteristics of a market with pricing bubbles. The price is very volatile, and it inherits the risk of quickly increasing to a peak and decreasing from the peak even faster. In this context, it is vital for investors to close their long positions optimally. In this study, we investigate the performance of the partially observable digital-drift model of Ekström and Lindberg and the corresponding optimal exit strategy on a Bitcoin trade. In order to estimate the unknown intensity of the random drift change time, we refer to Bitcoin halving events, which are considered as pivotal events that push the price up. The out-of-sample performance analysis of the model yields returns values ranging between 9% and 1153%. We conclude that the return of the initiated Bitcoin momentum trades heavily depends on the entry date: the earlier we entered, the higher the expected return at the optimal exit time suggested by the model. Overall, to the extent of our analysis, the model provides a supporting framework for exit decisions, but is by far not the ultimate tool to succeed in every trade.

Mudassar Hasan, Muhammad Abubakr Naeem, Muhammad Arif, Syed Jawad Hussain Shahzad · 5 authors

We examine the dynamics of liquidity connectedness in the cryptocurrency market. We use the connectedness models of Diebold and Yilmaz (Int J Forecast 28(1):57-66, 2012) and Baruník and Křehlík (J Financ Econom 16(2):271-296, 2018) on a sample of six major cryptocurrencies, namely, Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Ripple (XRP), Monero (XMR), and Dash. Our static analysis reveals a moderate liquidity connectedness among our sample cryptocurrencies, whereas BTC and LTC play a significant role in connectedness magnitude. A distinct liquidity cluster is observed for BTC, LTC, and XRP, and ETH, XMR, and Dash also form another distinct liquidity cluster. The frequency domain analysis reveals that liquidity connectedness is more pronounced in the short-run time horizon than the medium- and long-run time horizons. In the short run, BTC, LTC, and XRP are the leading contributor to liquidity shocks, whereas, in the long run, ETH assumes this role. Compared with the medium term, a tight liquidity clustering is found in the short and long terms. The time-varying analysis indicates that liquidity connectedness in the cryptocurrency market increases over time, pointing to the possible effect of rising demand and higher acceptability for this unique asset. Furthermore, more pronounced liquidity connectedness patterns are observed over the short and long run, reinforcing that liquidity connectedness in the cryptocurrency market is a phenomenon dependent on the time-frequency connectedness.

Michael Dempsey, Huy Pham, Vikash Ramiah

We consider the performance of cryptocurrencies in the light of fundamental asset pricing and portfolio theory. We observe how a traditional focus on reducing asset return volatility with Markowitz diversification misses the significance of such volatility for growth. The recognition that asset growth is more likely subject to exponential or continuously compounding growth characteristics reveals that asset volatility can be exploited both across assets and across investment periods to deliver superior returns.

Paul Marmora

No abstract is available for this record.