Abstract. This research employs Capital Asset Pricing Model and foreign exchange exposure theory to explain how the value of financial stocks is affected by the home country cryptocurrency. Previous literature proposed that financial stocks were related to the economic or individual financial ratio, but rarely discussed the impact of a cryptocurrency variable in the digital economy. This paper presents specific findings to prove that cryptocurrency development causes structural change in the financial industry, by examining 67,166 panel data observations from China and Taiwan markets. We offer the following important conclusions: 1. Financial stocks in the China market suffer significantly higher impacts from home country cryptocurrency exposure than the Taiwan market. 2. Financial stocks in the China market are more greatly shocked by the CAPM three factors variables than the Taiwan market. 3. There are significant differences between the two financial markets. 4. The dynamics of the adjustment process of cryptocurrency evolution and the monetary system are key solutions for both markets. Keywords: cryptocurrency, Fin-Tech, Exchange rate Exposure. JEL Classification A14, D82, F65, G12, F3 Formulas: 2; fig.: 0; tabl.: 4; bibl. 31.
Calendar anomalies as the seasonal tendencies in stock returns are the signal of irregular behaviour of stock markets. These anomalies have been comprehensively studied in many matured as well as emerging stock markets. But there is lack of exploration of calendar anomalies in the cryptocurrency market. So, the present treatise is an attempt to fill this lacuna by studying day of the week effect on cryptocurrencies' returns and volatility. This study is based on the prices of eight cryptocurrencies (viz. Bitcoin, EOS, Ethereum, Bitcoin Cash, Litecoin, Tether, XRP and Stellar) for a period starting from July 2017 and up to March 2020. The series of daily and day-wise returns were initially studied for stationarity using Ng-Perron tests and augmented DickeyâFuller test. The results from these tests confirmed that the cryptocurrencies' return series are stationary. The day of the week effect on cryptocurrencies returns was studied by introducing the dummies for each day of the week in the ordinary least square regression equation. The residuals from the ordinary least square regression equation were tested for ARCH effect using Engle's ARCH test. The results from the test confirmed the presence of ARCH effect in all series. The GARCH (1,1) model and PARCH model were further applied to account for ARCH effect and these models confirmed the presence of the day of the week effect in all the cryptocurrencies' returns and volatility except for day of week effect in Bitcoin and Tether returns. So, the significant day of the week effect was present in all cryptocurrencies' returns and volatility but the significant day of the week effect was absent in Bitcoin's returns and Tether's returns. These findings of significant day effect may help the existing and potential investors in taking investment decision in contemporary scenario of no ban in cryptocurrency market in India.
Risk in finance may come from (negative) asset returns whilst payment loss is a typical risk in insurance. It is often that we encounter several risks, in practice, instead of single risk. In this paper, we construct a dependence modeling for financial risks and form a portfolio risk of cryptocurrencies. The marginal risk model is assumed to follow a heteroscedastic process of GARCH(1,1) model. The dependence structure is presented through vine copula. We carry out numerical analysis of cryptocurrencies returns and compute Value-at-Risk (VaR) forecast along with its accuracy assessed through different backtesting methods. It is found that the VaR forecast of returns, by considering vine copula-based dependence among different returns, has higher forecast accuracy than that of returns under prefect dependence assumption as benchmark. In addition, through vine copula, the aggregate VaR forecast has not only lower value but also higher accuracy than the simple sum of individual VaR forecasts. This shows that vine copula-based forecasting procedure not only performs better but also provides a well-diversified portfolio.
Most studies in the Bitcoin literature are focused on daily data without considering other options. Therefore, it is necessary to analyse Bitcoin features at different frequencies. In this letter, we examine Bitcoin efficiency from 1 min to weekly data using the generalized Hurst exponent. Our results show that Bitcoin is more efficient over time regardless of the frequency. In particular, we observe that, since 2016, daily data are generally the most efficient frequency while 1 min and weekly data are the most inefficient. These results are relevant for investors and scholars since we detect the most profitable frequencies and underline the relevance of analysing different frequencies than daily data.
Using daily data of the 100 largest cryptocurrencies, we construct the efficient sorting portfolios and the quantile-based sorting portfolios based on ten factors. We find two price factors that can well predict cryptocurrency returns. The efficient sorting portfolios outperform the traditional quantile-based portfolios and the naive 1/N portfolios. The outperformance is largely due to the use of DCC-NL estimator, which captures the dynamic of covariance matrix and meanwhile addresses the curse of dimensionality. In addition, leverage constraints are important for cryptocurrency portfolios to control their risks.
Due to its characteristics of decentralization, no counterfeit currency, and anonymity, Bitcoin has developed incredibly rapidly, gradually realizing free exchange with real currency, and stepping into the purchase of real goods and services. This paper applied historical daily frequency data of Bitcoin and constructed traditional technical indicator factors such as CCI, AROON, MA, PSY, etc. Then logistic regression and XGBoost were leveraged to predict the rise and fall of the price of Bitcoin. The results showed that XGBoost classifier obtained a higher score than two logistic regressions, therefore the XGBoost method performed better until this stage: 163% of the return, 32% of the maximum retracement. This paper helps to buy and sell Bitcoin better, get a higher positive return, and may provide ideas for the stock market research.
Andrew Meegan, Shaen Corbet, Charles Larkin, Brian M. Lucey
Blockchain technology appears to be ready to revolutionise a broad number of industries. However, the blockchain itself contains a number of inefficiencies and areas for improvement, namely: transaction fees and transaction speeds. Directed acyclic graphs (DAGs) address, and improve on these inefficiencies and a number of digital currencies utilising this technology have already begun to appear. This paper provides an explanation of the technology behind DAG-based assets, while identifying and highlighting strategic advantages that DAGs possess over traditional blockchains. We conduct an EGARCH volatility analysis of a range of blockchain-based and DAG-based cryptocurrencies in the aftermath of a range of market shocks, taking the form of regulatory announcements such as bans and broad restrictions for cryptocurrencies. We find that DAG-based assets become increasingly responsive to market shocks as they mature. Such behaviour mirrors that of established cryptocurrencies such as Bitcoin, Ethereum and Litecoin, providing evidence that DAG-based cryptocurrencies now share similar characteristics to traditional blockchain-chain based products.
In this paper, we analyze various Decentralized Finance (DeFi) protocols in terms of their token distributions. We propose an iterative mapping process that allows us to split aggregate token holdings from custodial and escrow contracts and assign them to their economic beneficiaries. This method accounts for liquidity-, lending-, and staking-pools, as well as token wrappers, and can be used to break down token holdings, even for high nesting levels. We compute individual address balances for several snapshots and analyze intertemporal distribution changes. In addition, we study reallocation and protocol usage data, and propose wrapping complexity as a proxy for measuring token dependencies and ecosystem integration. The paper offers new insights on DeFi interoperability as well as token ownership distribution and may serve as a foundation for further research.
Cryptocurrencies provide an important dimension of innovation to the evolution of the exchange medium we call money. There are now close to 2,000 such currencies, and their potential and volume is growing. The impact of such currencies for money laundering, law enforcement, and banking supervision have been extensively discussed on the transaction level. But this is the âmicroâ level of analysis. What has been rare is a âmacroâ level discussion of the impact on the monetary system of a country. Central banks, which are institutions tasked with providing monetary stability, will see their problems rise while the power of their traditional tools to control money supply and interest rates â such as reserve requirements and the discount rates â is declining. But the new digital technologies â such as distributed ledgers â and new approaches provide regulatory bodies also with new and potentially powerful tools. The task for central banks and policy makers is to create new approaches to use, regulate, and incent them in shaping the macro-economic path of their economy. The paper will propose several of these approaches. This is of particular importance in an economic recovery post coronavirus. In the process, central banks will also, predictably, issue their own digital currencies, and a tiny number of those will become global super-currencies. This will create a new type of issues.
Muhammad Abubakr Naeem, Imen Mbarki, Muhammad Tahir Suleman, Xuan Vinh Vo · 5 authors
Abstract We examine the predictive ability of Twitter Happiness Sentiment for six major cryptocurrencies using daily data from August 7, 2015 to December 31, 2019. At first instance, our results conclude a significant nonlinear relationship between Twitter Happiness Sentiment and cryptocurrencies. The nonlinear dependence structure is further enhanced when using the quantileâonâquantile (QQ) analysis, which indicates that high and low sentiment predicts returns of five cryptocurrencies. These findings are statistically and economically significant.
Abstract Accurate measurement of relationship between assets is sensitive to different market conditions in different horizons and has implications for portfolio optimization. Cryptocurrencies are new category of assets that can reduce the risk of wellâdiversified portfolio including gold. The paper explores the connections between seven cryptocurrencies and gold at bear (bull) markets across time to uncover the hedging properties of cryptocurrencies for gold investors. Wavelet technique was used to decompose the daily return series of the assets into shortâ, mediumâ and longâterm frequencies. Quantile regression (QR) and quantileâinâquantile regression (QQR) were applied on the decomposed series to establish the association between the assets over 19 quantiles ( Ï = 0.05 to 0.95). QR results show all cryptocurrencies as hedges for gold regardless of market regime in the medium to longâterms. QQR results depict inverse association at bear market but positive association at bull market across time suggesting hedging possibilities at bear markets. Our study provides precise information to investors, regulators and policy makers on risk mitigating strategies for extreme gold market fluctuations across time and market states.
This paper analyzes high-frequency estimates of good and bad realized volatility of Bitcoin. We show that volatility asymmetry depends on the volatility regime and the forecast horizon. For one-day ahead forecasts, good volatility commands a stronger impact on future volatility than bad volatility on average and in extreme volatility regimes but not across all quantiles and volatility regimes. For 7-day ahead forecasting horizons the asymmetry is similar to that observed in stock markets and becomes stronger with increasing volatility. Compared with stock markets, the persistence and predictability of volatility is low indicating high variations of volatility.
Abstract While gaining more popularity both as a financial asset and a commodity, a number of cryptocurrencies are emerging with a loosely regulated market microstructure which is a challenge to their efficiency. We have ranked 6 out of the top 10 cryptocurrencies based on their inefficiency ratios, using a novel timeâvarying generalised Hurst exponent methodology. All the six crypto markets exhibit a timeâvarying efficiency throughout the studied period, thus indicating a varying degree of exploitable profitable trading opportunities. The inefficiency ratio indicates that Bitcoin is the third most inefficient market, while the first and second most inefficient markets are DASH and NEM, respectively, thus they provide the most abnormal profit opportunities. However, the most efficient crypto markets are Ethereum and Ripple according to the order of their rankings. Further research could be performed on the factors affecting the inefficiency index to understand the efficiency determination of these cryptocurrency markets.
The development of cryptocurrency as a means of exchange without legal backing and invisibility of the identity of operators has posed peculiar challenges such as illicit financial flow and terrorism amongst others, to the country. This study, therefore, sought to examine the effect of cryptocurrency on the Nigerian economy. The study was hinged on social exchange theory. Secondary data were obtained from the CBN statistical bulletin and Global Financial Integrity Report for a period of six years from 2013 to 2018. The data were analyzed using a simple regression model. The result shows that R is 5.8% which means that there is a low positive relationship between cryptocurrency and the level of economic development in Nigeria. It further shows an adjusted R square of -24.6 which depicts that cryptocurrency has a low inverse effect on the level of economic development in Nigeria. In conclusion, the computed p-value of 0.913 which is higher than the set p-value of 0.05 shows that cryptocurrency does not have a significant effect on the level of economic development in Nigeria. Hence, it is recommended that, in order to sustain economic development from the activities of cryptocurrency in Nigeria, the Central Bank of Nigeria needs to ensure that laws and mechanisms are put in place to adequately capture the activities of cryptocurrency in the country.
This paper presents an overview of the main developments of cryptocurrencies and discusses their future perspectives. First, it briefly reviews the history of cryptocurrencies since the creation of Bitcoin, presents the main market trends, and discusses the key features of cryptocurrencies in the context of blockchain. Second, it analyses current cryptocurrency projects, like the Libra project, and other applications of the blockchain technology. Third, it presents a systematic economics and financial literature review on cryptocurrencies. Fourth, it examines the challenges, benefits, and future perspectives of cryptocurrencies and blockchain technology, with a focus on the environmental issues and central bank digital currencies.
A major concern of the adoption and scalability of Blockchain technologies refers to their efficient use for payments. In this work, we analyze how Lightning Network (LN), which represents a relevant infrastructural novelty, is influenced by the market dynamics of its referring cryptocurrency, namely Bitcoin. In so doing, we focus on how the LN is efficient in performing transactions and we relate this feature to the market conditions of Bitcoin. By applying the TodaâYamamoto variant of Granger-causality, we note that market conditions of Bitcoin do not significantly influence the topological configuration of the LN. Hence, although the LN represents a second layer on the Bitcoin blockchain, our findings suggest that its efficient functioning does not appear to be related to the simple market performance of its underlying cryptocurrency and, in particular, of its volatile market fluctuations. This result may therefore contribute to shed light on the practical usage of the LN as a blockchain technology to favor transactions.
This paper empirically examines the convergence of cryptocurrency markets with particular attention to top 30 cryptocurrencies. The study applies the novel Phillips and Sul panel convergence technique to daily closing price data of 30 cryptocurrencies for the period October 4, 2017 to May 31, 2020. The empirical findings suggest the evidence of divergence and the existence of club convergence across the cryptocurrency markets. The study finds the existence of five clubs in the top 30 cryptocurrency markets. The outcome of the study helps the investors and crypto lovers to diversify their portfolio by seeing the common transition path of the group of currencies.