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This paper proposes a two-stage approach to parametric nonlinear time series modelling in discrete time with the objective of incorporating uncertainty or misspecification in the conditional mean and volatility. At the first stage, a reference or approximating time series model is specified and estimated. At the second stage, Bayesian nonlinear expectations are introduced to incorporate model uncertainty or misspecification in prediction via specifying a family of alternative models. The Bayesian nonlinear expectations for prediction are constructed from closed-form Bayesian credible intervals evaluated using conjugate priors and residuals of the estimated approximating model. Using real Bitcoin data including some periods of Covid 19, applications of the proposed method to forecasting and risk evaluation of Bitcoin are discussed via three major parametric nonlinear time series models, namely the self-exciting threshold autoregressive model, the generalized autoregressive conditional heteroscedasticity model and the stochastic volatility model. Supplementary Information: The online version contains supplementary material available at 10.1007/s00181-022-02255-z.
ABSTRACT
The sudden market crash around 20 February 2020 on the dawn of the COVID-19 pandemic has accelerated the digitalization of all human communication and revived the interest for risk mitigation during stress periods. Interestingly, FAANA (Facebook, Apple, Amazon, Netflix, and Alphabet) stocks exhibited positive returns with remarkable resilience throughout the pandemic period, suggesting a change in their investing risk. In this paper, we take a different step from the existing literature and examine the hedging, diversifying, and safe haven properties of FAANA stocks against four alternative assets, namely gold, U.S. Treasury bonds, Bitcoin, and U.S. Dollar/CHF. Our analysis covers an extended sample period comprising the heightened uncertainty during the recent pandemic period. It involves conditional correlations, optimal weights, hedge ratios, and hedging effectiveness for the pairs of FAANA stock and alternative asset during the full sample period and the COVID-19 pandemic period. The results show that the majority of FAANA stocks serve as weak/strong safe havens against gold, Treasury bonds, Bitcoin, and Dollar/CHF in the full sample period. Further, few FAANA stocks serve as strong safe havens against the U.S. Treasury and Dollar/CHF during the pandemic. Our findings suggest that FAANA, once thought as risky high growth tech stocks, have gained maturity and became a safe blanket during the latest turbulent period. © 2022 Elsevier Inc.
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PurposeThis paper investigates the impact of global sentiment and various coronavirus disease 2019 (COVID-19)-related media coverage news (Media-Hype index;Panic Index;Media Coverage Index, infodemic index and coronavirus statistics) on the dynamics of bitcoin returns during the COVID-19 pandemic using an asymmetric framework.Design/methodology/approachThe authors use an asymmetric framework based on quantile regression (QR) and quantile-on-quantile regression.FindingsQR results show that COVID-19 panic news negatively affects bitcoin market returns at times of extreme bearish. However, COVID-19 bullish sentiment negatively impacts bitcoin market returns during bullish market conditions. Quantile-on-quantile approach's (QQA) empirical results show that the effects of COVID-19-related news on bitcoin returns were heterogeneous, mainly negative and varied across quantiles.Research limitations/implicationsThe authors find some significant differences regarding the impact of news on bitcoin return dynamics compared to stock markets, suggesting the safe-haven role of bitcoin against stock during the ongoing epidemic.Practical implicationsThe authors find some significant differences regarding the impact of news on bitcoin return dynamics compared to stock markets, suggesting the safe-haven role of bitcoin against stock during the ongoing epidemic.Originality/valueThis study contributes to understanding the dynamics of bitcoin returns using various COVID-19 media news.
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This paper studies the time-varying market linkages between Bitcoin and green assets before and during the COVID-19 pandemic through a TVP-VAR model with stochastic volatility. Both the roles of uncertainty and environmental attention related to cryptocurrency are considered when modeling market linkages, which underlying asymmetry is detected from three perspectives, i.e., bidirectionality of the impact direction, time points where the unit shock of the IRF analysis is imposed, and before and after the pandemic. We find that the investment sheltering role of Bitcoin for green assets is enhanced and expanded after the onset of the pandemic, while green assets in turn consistently act as an effective hedge for Bitcoin irrespective of the pandemic. Additional analyses confirm the robustness of our findings, which possess implications for not only hedging against green portfolios but also seeking green shelters. © 2022 Elsevier B.V.
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Heterogeneity in informational inefficiency in a cross-market virtual currency, such as Bitcoin, allows for the extraction of differential gains from a portfolio of investments over time. In this paper, we measure inefficiency in five country/region segmented Bitcoin markets based on dynamic estimation of the fractional integration order of their price series. Results reveal a time-varying and country-specific pattern of inefficiency in the five Bitcoin markets, although the degree of inefficiency in each market has declined over time. Further, we introduce a new decomposition method to disentangle components of the inefficiency degree. Results suggest that the total variation around the convergence benchmark has fallen, whilst the proportion due to the difference between convergence and efficiency has risen from approximately 77% in 2013 to almost 100% in 2020. Besides, evidence of convergence emerges until the outbreak of COVID-19, beyond which the inefficiency degree diverges measurably. We show that Bitcoin markets have become more efficient after the first-wave COVID era and then the nature of market segmentation has played a less important role, levelling the cross-market difference and thus reducing the potential for arbitrage. © 2023 Elsevier B.V.
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This study evaluates the portfolio diversification potential of different classes of assets-equity, cryptocurrency and precious metals-using total, asymmetric and frequency-based spillover transmission framework. The VARbased generalized variance decomposition method is used to analyse the daily prices of S&P 500, bitcoin, gold, silver and platinum between April 2011 through January 2021. The results of aggregate spillover support bitcoin as a potential diversifier due to its isolation from other sets of assets. The decomposition of overall spillover into downside and upside spillover reveals a higher downside connectedness than the upside, suggesting an asymmetric interdependence amongst these markets. Moreover, the frequency based aggregate spillovers suggest the connectedness is driven mostly by the shorter time-horizons. The study provides important policy implications for market participants with distinct investment objectives.
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Across the globe, COVID-19 has disrupted the financial markets, making them more volatile. Thus, this paper examines the market volatility and asymmetric behavior of Bitcoin, EUR, S&P 500 index, Gold, Crude Oil, and Sugar during the COVID-19 pandemic. We applied the GARCH (1, 1), GJR-GARCH (1, 1), and EGARCH (1, 1) econometric models on the daily time series returns data ranging from 27 November 2018 to 15 June 2021. The empirical findings show a high level of volatility persistence in all the financial markets during the COVID-19 pandemic. Moreover, the Crude Oil and S&P 500 index shows significant positive asymmetric behavior during the pandemic. Apart from this, the results also reveal that EGARCH is the most appropriate model to capture the volatilities of the financial markets before the COVID-19 pandemic, whereas during the COVID-19 period and for the whole period, each GARCH family evenly models the volatile behavior of the six financial markets. This study provides financial investors and policymakers with useful insight into adopting effective strategies for constructing portfolios during crises in the future.
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This study examines the relationship and risk spillover between Bitcoin, crude oil, and six traditional markets (the US stock, Chinese stock, gold, bond, currency, and real estate markets) from 2019 to 2020, during which the coronavirus disease 2019 (COVID-19) outbreak occurred as well. We first discuss the static relationship between Bitcoin and these markets using a quantile-on-quantile model and examine the dynamic relationship using a time-varying copula model. A conditional value-at-risk model is subsequently used to estimate the risk spillover between the markets studied. The empirical results reveal that the relationship between these markets is always time-varying, and the COVID-19 outbreak has revealed such changes in the relationship between Bitcoin and other traditional financial markets. The risk of all single markets has enhanced because of the pandemic. Further, the risk spillover of these markets has also changed dramatically since the COVID-19 outbreak during which the Bitcoin market has played an important role and exerted a significant impact on the crude oil market, and the four other markets (US stock, gold, Chinese stock, and real estate markets). Overall, our findings indicate that investors and policymakers need to be made aware of the risk spillover between Bitcoin, crude oil, and other traditional markets and that flexible hedge strategies and policies should be implemented in response to the challenges and economic recession observed following the COVID-19 outbreak.
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This article investigates the dynamical complexity and fractal characteristics changes of the Bitcoin/US dollar (BTC/USD) and Euro/US dollar (EUR/USD) returns in the period before and after the outbreak of the COVID-19 pandemic. More specifically, we applied the asymmetric multifractal detrended fluctuation analysis (A-MF-DFA) method to investigate the temporal evolution of the asymmetric multifractal spectrum parameters. In addition, we examined the temporal evolution of Fuzzy entropy, non-extensive Tsallis entropy, Shannon entropy, and Fisher information. Our research was motivated to contribute to the comprehension of the pandemic's impact and the possible changes it caused in two currencies that play a key role in the modern financial system. Our results revealed that for the overall trend both before and after the outbreak of the pandemic, the BTC/USD returns exhibited persistent behavior while the EUR/USD returns exhibited anti-persistent behavior. Additionally, after the outbreak of COVID-19, there was an increase in the degree of multifractality, a dominance of large fluctuations, as well as a sharp decrease of the complexity (i.e., increase of the order and information content and decrease of randomness) of both BTC/USD and EUR/USD returns. The World Health Organization (WHO) announcement, in which COVID-19 was declared a global pandemic, appears to have had a significant impact on the sudden change in complexity. Our findings can help both investors and risk managers, as well as policymakers, to formulate a comprehensive response to the occurrence of such external events.
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This study investigates the connectedness between Bitcoin and fiat currencies in two groups of countries: the developed G7 and the emerging BRICS. The methodology adopts the regular (R)-vine copula and compares it with two benchmark models: the multivariate t copula and the dynamic conditional correlation (DCC) GARCH model. Moreover, this study examines whether the Bitcoin meltdown of 2013, selloff of 2018, COVID-19 pandemic, 2021 crash, and the Russia-Ukraine conflict impact the linkage with conventional currencies. The results indicate that for both currency baskets, R-vine beats the benchmark models. Hence, the dependence is better modeled by providing sufficient information on the shock transmission path. Furthermore, the cross-market linkage slightly increases during the Bitcoin crashes, and reaches significant levels during the 2021 and 2022 crises, which may indicate the end of market isolation of the virtual currency.
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This paper explores the asymmetric effect of COVID-19 pandemic news, as measured by the coronavirus indices (Panic, Hype, Fake News, Sentiment, Infodemic, and Media Coverage), on the cryptocurrency market. Using daily data from January 2020 to September 2021 and the exponential generalized autoregressive conditional heteroskedasticity model, the results revealed that both adverse and optimistic news had the same effect on Bitcoin returns, indicating fear of missing out behavior does not prevail. Furthermore, when the nonlinear autoregressive distributed lag model is estimated, both positive and negative shocks in pandemic indices promote Bitcoin's daily changes; thus, Bitcoin is resistant to the SARS-CoV-2 pandemic crisis and may serve as a hedge during market turmoil. The analysis of frequency domain causality supports a unidirectional causality running from the Coronavirus Fake News Index and Sentiment Index to Bitcoin returns, whereas daily fluctuations in the Bitcoin price Granger affect the Coronavirus Panic Index and the Hype Index. These findings may have significant policy implications for investors and governments because they highlight the importance of news during turbulent times. The empirical results indicate that pandemic news could significantly influence Bitcoin's price.
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This paper investigates the potential hedging and safe-haven properties of several alternative investment assets, including gold, Bitcoin, oil, and the oil price volatility index (OVX), against the risks of the Saudi stock market and its constituent sectors in different phases of the COVID-19 pandemic. Using daily data, we employ the bivariate dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (DCC-GARCH) technique to model volatilities and conditional correlations. Our findings show that all investigated alternative investment assets had a time-varying hedging role in the Saudi stock market, which became expensive during the early stages of the COVID-19 pandemic. Our results also show that the optimal weights for gold were substantially higher than those of other assets, reaching a peak during the pandemic, implying that investors consider gold a flight-to-safety asset. Additionally, we find that gold and OVX were strong hedges and could have served as weak safe havens for investors during the early stages of the COVID-19 pandemic, while the remaining assets generally lacked these properties and could be merely used as diversifiers. Our empirical findings offer several key implications for policymakers and portfolio managers in Saudi Arabia that may be applicable to similar markets. In particular, we show that OVX-based products can serve as a promising hedging asset for stock markets in oil-exporting countries.