Joy Dip Das, Sulalitha Bowala, Ruppa K. Thulasiram, A. Thavaneswaran
Constructing resilient portfolios is of crucial and utmost importance to investment management. This study compares traditional and data-driven models for building resilient portfolios and analyzes their performance for stocks (S&P 500) and highly volatile cryptocurrency markets. The study investigates the performance of traditional models, such as mean-variance and constrained optimization, and a recently proposed data-driven resilient portfolio optimization model for stocks. Moreover, the study analyzes these methods with evolving S&P CME bitcoin futures index and the Crypto20 index. These analyses highlight the need for further investigation into traditional and data-driven approaches for resilient portfolio optimization, including higher-order moments, particularly under varying market conditions. This study provides valuable insights for investors and portfolio managers aiming to build resilient portfolios that could be used in different market environments.
Weihao Han, David Newton, Emmanouil Platanakis, Charles Sutcliffe ¡ 5 authors
Abstract Cryptocurrency returns are highly nonnormal, casting doubt on the standard performance metrics. We apply almost stochastic dominance, which does not require any assumption about the return distribution or degree of risk aversion. From 29 longâshort cryptocurrency factor portfolios, we find eight that dominate our four benchmarks. Their returns cannot be fully explained by the threeâfactor coin model of Liu et al. So we develop a new threeâfactor model where momentum is replaced by a mispricing factor based on size and riskâadjusted momentum, which significantly improves pricing performance.
This paper investigates the dynamics and drivers of informational inefficiency in the Bitcoin futures market. To quantify the adaptive pattern of informational inefficiency, we leverage two groups of statistics which measure long memory and fractal dimension to construct a global-local market inefficiency index. Our findings validate the adaptive market hypothesis, and the global and local inefficiency exhibits different patterns and contributions. Regarding the driving factors of the time-varying inefficiency, our results suggest that trading activity of retailers (hedgers) increases (decreases) informational inefficiency. Compared to hedgers and retailers, the role played by speculators is more likely to be affected by the COVID-19 crisis. Extremely bullish and bearish investor sentiment has more significant impact on the local inefficiency. Arbitrage potential, funding liquidity, and the pandemic exert impacts on the global and local inefficiency differently. No significant evidence is found for market liquidity and policy uncertainty related to cryptocurrency.
Purpose The purpose of this study is to discover the motivating factors for cryptocurrency investment during an economic crisis in the MENA region, with reference to the economic crisis of 2019â2022, in Lebanon. Design/methodology/approach The authors used t-test, and logistic regressions on a sample of 254 Lebanese investors to differentiate between cryptocurrency investors, and non-investors. Linear regressions of a subsample of cryptocurrency investors determined the factors that explained increasing cash investment in cryptocurrencies. Data were collected from investors in Lebanon, which could limit the generalization of the research results across the MENA region. Findings Investors differed from non-investors in that they were male, owned investments in the stock, bond and commodity markets, had prior investment experience in cryptocurrencies, were risk-takers and had expectations of high returns. Investors increased the dollar investment in cryptocurrencies, if they were male, as they invested more funds in securities, had previously invested in cryptocurrencies and had stronger risk-taking propensity. Expectations of high returns drove investors to cryptocurrencies, but such expectations do not stimulate further cryptocurrency investment. Originality/value This study is an initial attempt to comprehend the reactions of investors in the MENA region to a currency crisis that triggered investment in cryptocurrencies following the collapse of fiat currencies, central bank default and restrictions on bank withdrawals.
Purpose The COVID-19 pandemic has led to global economic policy uncertainty, which has increased the need to investigate ways to mitigate the uncertainty. This study aims to examine the potential of cryptocurrencies as a hedge and safe haven avenue against economic policy uncertainty. Design/methodology/approach This study investigates the behavior of the five leading cryptocurrencies in relation to country-level and group-level economic policy uncertainty indices, as measured by the text-based method developed by Baker et al . ( The Quarterly Journal of Economics , 2016, 131, 1593â1636). The research covers a broad range of emerging and developed economies from July 2013 to September 2020. The study employs the approach of Narayan et al . ( Economic Modelling , 2016, 53, 388â397) to examine the hedging and safe-haven properties of cryptocurrencies. Findings This study finds that the top cryptocurrencies play a hedging role against economic policy uncertainty, with some exceptions. Additionally, there is evidence to support the idea that cryptocurrencies can serve as a safe haven during the COVID-19 pandemic. As a result, investors may benefit from using cryptocurrencies as a risk-management avenue during times of uncertainty. Originality/value This research contributes to the existing literature by testing the cryptocurrencies' hedging and safe haven properties in a new way, by analyzing their lead and lag behaviors using a recent and innovative approach. Additionally, it examines a wide range of emerging and advanced markets, providing insight into the potential of using cryptocurrencies as a risk mitigation avenue.
Nader Naifar, Sohale Altamimi, Fatimah Alshahrani, Mohammed Alhashim
This paper aims to investigate the impact of global financial, economic, and gold price uncertainty indices (VIX, EPU, and GVZ) and investor sentiment based on media coverage news on the returns of Bitcoin and Ethereum during the COVID-19 pandemic. We adopt an asymmetric framework based on the Quantile-on-Quantile approach, which examines the quantiles of the cryptocurrency returns, investor sentiment, and the various uncertainties indicators. The empirical findings suggest that the COVID-19 pandemic has significantly impacted cryptocurrency returns. Specifically, (i) the results demonstrate the predictive power of Economic Policy Uncertainty (EPU) during this period, as evidenced by a strong negative association between EPU and cryptocurrency returns across all quantiles; ( ii ) the correlation between cryptocurrency returns and the VIX index was negative but weak, across various quantile combinations of Ethereum and Bitcoin returns; ( iii ) an increase in COVID-19 news negatively affected Bitcoin returns across all quantiles; ( iv ) Bitcoin and Ethereum cannot be relied upon as effective hedging tools against global financial and economic uncertainty during the COVID-19 pandemic. Studying the behavior of cryptocurrency during uncertainty like pandemics is extremely important because it provides investors with insights on diversifying their portfolios and hedging their risks.
Abstract The paper examines market coâmovement between pairs of financial assets in the timeâfrequency domain. Recent finance literature confirms the integration of cryptocurrencies and financial assets, which may bring more investments with the possibility of surplus liquidity in the cryptocurrency segment, leading to financial instability. The novelty of this paper is examining the integration of cryptocurrencies and the indices of equity, sustainability, renewable energy, and crude oil for the daily observations from 2015 to 2021 by using the wavelet coherency method. The empirical results signify no integration in the shortâterm scales and grow stronger in the mediumâterm scales, especially during the COVIDâ19 period, and further exhibit weaker heterogeneous associations in the longâterm scales. However, the sustainability, clean energy indices follow similar dynamics of the equity market and crypto pairs. In contrast, the global crude oil index showcases the minor integration with cryptocurrencies compared with other traditional asset classes. Hence, the cryptocurrency market fails to confirm the safe haven features, especially during the COVIDâ19 periods (Mediumâterm), which facilitate the domestic and international investors expecting to hedge their price risk in equity markets using cryptocurrencies may have to look for shortâterm. The leadâlag heterogeneous effects of the assetâpairs may pave arbitrage opportunities for investors.
Cryptocurrencies have gained popularity and are increasingly used in the global financial system, despite their volatile nature. They have become an attractive financial instrument for individuals and corporations due to their potentials for high returns, decentralized nature, and exemption from strict government regulations. This study aims to investigate how cryptocurrency volatility affects the performance of companies listed on the Nigerian Exchange Limited (NGX). The study uses an ex post facto research design and the GARCH (1,1) model. Weekly data on Bitcoin and Ethereum were obtained from www.ng.investing.com and used to construct a cryptocurrency composite index with principal component analysis (PCA). The All-Share Index data were extracted from the Security and Exchange Commission (SEC) statistical bulletin between January 2017 and December 2021. The result of the mean equation shows that cryptocurrency trading in Nigeria responds more to positive sentiment and good news than bad news, while the variance equation reveals that current conditional volatility of cryptocurrencies and companies' performance is influenced by their previous shocks and past volatility conditions. The study also found evidence of volatility clustering in companiesâ performance on the NGX. Therefore, investors are advised to exercise caution in an expanding cryptocurrency market, while regulators and policymakers should use relevant indicators to avoid contagion risk that could spread to the stock market. This paper is significant and relevant to achieving the Nigerian government's plan to introduce an official virtual currency.
The Non-Fungible-Token (NFT) market has experienced explosive growth in recent years. According to DappRadar, the total transaction volume on OpenSea, the largest NFT marketplace, reached 34.7 billion dollars in February 2023. However, the NFT market is mostly unregulated and there are significant concerns about money laundering, fraud and wash trading. The lack of industry-wide regulations, and the fact that amateur traders and retail investors comprise a significant fraction of the NFT market, make this market particularly vulnerable to fraudulent activities. Therefore it is essential to investigate and highlight the relevant risks involved in NFT trading. In this paper, we attempted to uncover common fraudulent behaviors such as wash trading that could mislead other traders. Using market data, we designed quantitative features from the network, monetary, and temporal perspectives that were fed into K-means clustering unsupervised learning algorithm to sort traders into groups. Lastly, we discussed the clustering results' significance and how regulations can reduce undesired behaviors. Our work can potentially help regulators narrow down their search space for bad actors in the market as well as provide insights for amateur traders to protect themselves from unforeseen frauds.
This study explores whether Islamic equities offer portfolio diversification benefits to cryptocurrency investors. It employs the Continuous Wavelet Transform model to examine the nature of coherence between major cryptocurrency asset classes and major Asian Islamic equity markets on different investment horizons. We consider a range of Islamic equity indices for multiple countries and a basket of three prominent cryptocurrencies: Bitcoin, Ethereum and Ripple. Findings suggest that Asian Islamic equities offer portfolio diversification opportunities. Our findings also imply that Asian Islamic equities are not efficient and are prone to short-term speculative activities.
People favor nonfungible token (NFT) because of the attribute to prove digital assetsâ ownership and promote interactions. Investors are keen to buy and use NFT pictures as social media avatars and participate in online communities around NFT collections. However, information manipulation in the NFT market has led to investors significant losses. Our work explored a way to correspond social media accounts with Ethereum addresses and studied the microstructure of NFT market. Taking Goblintown.wtf as an example, we analyzed the participants, mechanism, and impact of Twitter information manipulation in the market. We found five categories of investors in the NFT market under information manipulation: primary investors, amateur investors, fanatic investors, short-term rational investors, and long-term rational investors. We argue that investors will consume their limited attention more likely when joining NFT online communities. This will lead to more complicated for them to make investment decisions rationally.
Price feeds of securities is a critical component for many financial services, allowing for collateral liquidation, margin trading, derivative pricing and more. With the advent of blockchain technology, value in reporting accurate prices without a third party has become apparent. There have been many attempts at trying to calculate prices without a third party, in which each of these attempts have resulted in being exploited by an exploiter artificially inflating the price. The industry has then shifted to a more centralized design, fetching price data from multiple centralized sources and then applying statistical methods to reach a consensus price. Even though this strategy is secure compared to reading from a single source, enough number of sources need to report to be able to apply statistical methods. As more sources participate in reporting the price, the feed gets more secure with the slowest feed becoming the bottleneck for query response time, introducing a tradeoff between security and speed. This paper provides the design and implementation details of a novel method to algorithmically compute security prices in a way that artificially inflating targeted pools has no effect on the reported price of the queried asset. We hypothesize that the proposed algorithm can report accurate prices given a set of possibly dishonest sources.
Yeguang Chi, Wenyan Hao, Jiangdong Hu, Zhenkai Ran
Abstract We investigate the crossâsection assetâpricing patterns of major cryptocurrencies from 2017 to 2021. We show that the basis, momentum, and basisâmomentum factors earn statistically significant excess returns, a result consistent with the findings reported in the commodity futures literature. The basis is the strongest signal predicting crossâsectional differences in cryptocurrency futures returns; the momentumâinduced risk premium is not statistically powerful, whereas the basis momentumâinduced risk premium disappears when accounting for the basisâinduced risk premium. Daily factor returns are statistically much stronger than weekly factor returns. Monthly factor returns are nonsignificant.
Recently the disposition of the number of financial companies to encompass cryptocurrencies in their portfolios has speeded up. Cryptocurrencies are the first pure digital assets to be included by asset directors. Although they share many things in common with more traditional assets, they have their peculiar characters and their behavior as an asset is still in the continuous phase of being understood. It is therefore significant to sum up the available research publication and finding on cryptocurrency marketing, consisting trading avenues, trading impulses, trading techniques, research and management of risk. This publication highlights an intensive survey of cryptocurrency trading research, by consulting 146 research publications on numerous aspects of the cryptocurrency business (e.g., cryptocurrency trading systems, bubble, and extreme condition, prediction of volatility, and return, crypto-assets portfolio construction, and crypto-assets, technical trading and others). This publication also investigates datasets, research inclination and dissemination among the objects of research (contents/properties) and technologies, finalizing with some promising opportunities that are open and transparent in the cryptocurrency market.
Skander Slim, Ibrahim Tabche, Yosra Koubaa, Mohamed Osman ¡ 5 authors
Abstract Motivated by the relationship between trading intensity and volatility and the attractiveness of durationâbased volatility estimators, this paper investigates the ability of price duration to forecast realized volatility of Bitcoin. Using highâfrequency transaction data, trading intensity is measured by price duration and incorporated in the class of heterogeneous autoregressive (HAR) models. Results provide compelling evidence that trading intensity improves the forecasting performance of a highly competitive set of HAR models, commonly used in the literature. HAR extensions that incorporate price duration systematically deliver the lowest forecast errors and generate economically significant gains in volatility targeting exercise over multiple horizons. However, results show no evidence in favor of a unique durationâaugmented model. The predictive ability of price duration is supported by a number of robustness checks, including alternative estimation windows, bull and bear market states, and alternative thresholds that define price events.
Purpose As cryptocurrencies continue to gain viability as an asset class, institutional investors and publicly traded firms have started taking investment positions in digital currencies. What firms may not be considering, however, is the effect these assets may have on their risk profiles. This study aims to (1) measure the effect of cryptocurrencies on the risk and return characteristics of publicly traded companies; (2) decipher the motives behind holding cryptocurrencies as an asset class; and (3) determine whether one reason for holding is more effective than another. To conduct this research, the four largest publicly traded holders of cryptocurrency as well as four of the most prominent cryptocurrencies are explored. Design/methodology/approach The cross-sectional analysis approach has been used to analyze the daily returns, volatility, betas and Sharpe Ratios of firms during periods without cryptocurrency strategies and during periods with cryptocurrency strategies. Findings The impact of the cryptocurrency asset class on common stock performance and corporate disclosures are documented. The importance of risk disclosures on cryptocurrency holdings is emphasized: Firms must better inform their stakeholders through comprehensive disclosures in financial statements. Firms utilize cryptocurrencies for various reasons such as treasury management tools or as direct sources of income. Consequently, the impact on returns and risks varies substantially. Originality/value To the best of the authorsâ knowledge, this is one of the first studies on cryptocurrency investments in the treasury departments of publicly traded companies. The study contributes to the literature by extracting relevant information regarding company risk reporting and cryptocurrency risk at firms. The conclusions also promote firm transparency with detailed reporting of cryptocurrency holding risks.
Mohamed Shaker Ahmed, Adel Mahmoud Al Samman, Kaouther Chebbi
Purpose This paper aims to investigate feedback trading and autocorrelation behavior in the cryptocurrency market. Design/methodology/approach It uses the GJR-GARCH model to investigate feedback trading in the cryptocurrency market. Findings The findings show a negative relationship between trading volume and autocorrelation in the cryptocurrency market. The GJR-GARCH model shows that only the USD Coin and Binance USD show an asymmetric effect or leverage effect. Interestingly, other cryptocurrencies such as Ethereum, Binance Coin, Ripple, Solana, Cardano and Bitcoin Cash show the opposite behavior of the leverage effect. The findings of the GJR-GARCH model also show positive feedback trading for USD Coin, Binance USD, Ripple, Solana and Bitcoin Cash and negative feedback trading for Ethereum and Cardano only. Originality/value This paper contributes to the literature by extending Sentana and Wadhwani (1992) to explore the presence of feedback trading in the cryptocurrency market using a sample of the most active cryptocurrencies other than Bitcoin, namely, Ethereum, USD coin, Binance Coin, Binance USD, Ripple, Cardano, Solana and Bitcoin Cash.
Design/Methodology/Approach: This chapter applies tests associated with the adaptive market hypothesis (AMH) and Johansen cointegration test. AMH acknowledges the views of the efficient market hypothesis and behavioural finance approach.Purpose: Cryptocurrencies are considered a new asset class by multiasset portfolio managers. Hence, we examine the AMH and cointegration in the cryptocurrency market to know whether select cryptocurrencies can be diversified.Findings: We find that cryptocurrencies are efficient and there is a long-run relationship among constituent series, and there is no short-run causality derived from bitcoin, Ethereum and litecoin to bitcoin, while stellar and Dogecoin have short-run causality to bitcoin.Originality/Value: This chapter is different from the existing one as this is the first study in which the AMH and Johansen cointegration test are applied to check the efficiency and relationship of Bitcoin, Ethereum, and Monero, Stellar, litecoin and Dogecoin.
We examine the effects of US, German, and Japanese macroeconomic news surprises and monetary policy decisions on the intraday cyclical volatility of Bitcoin and Ethereum markets. We first document intraday seasonality specific to each day of the week and show that these patterns exhibit a slightly different volatility compared to all-day seasonality. Second, the US monetary policy news and macroeconomic surprises generate the largest effect on the seasonal volatility. Third, Ethereum seasonality is more sensitive to macroeconomic fundamentals compared to Bitcoin. These results suggest that to improve cryptocurrency pricing, portfolio management, and risk management practices associated with cryptocurrency transactions, investors should consider the interactions between day of the week effects, intraday seasonality patterns and the macroeconomics news releases.
Abstract This paper examines the impact of market related events and investor base on the spread of Bitcoin prices between two exchange platforms, Coinbase and Binance. Based on highâfrequency data samples collected from 2019 to 2021, we show how investors from different bases react differently to market related events, which create the price spreads between exchange platforms. We also identify the arbitrage opportunities these spreads create and establish arbitrage strategies for all identified events to exploit the variations in Bitcoin prices traded on both platforms. Findings indicate arbitrage offers profits that are higher overall than holding Bitcoin on either platform.