On stylized facts of cryptocurrencies returns and their relationship with other assets, with a focus on the impact of COVID-19

This study provides an empirical analysis on the main univariate and multivariate stylized facts iin return series of the two of the largest cryptocurrencies, namely Ethereum and Bitcoin. A Markov-Switching Vector AutoRegression model is considered to further explore the dynamic relationships between cryptocurrencies and other financial assets. We estimate the presence of volatility clustering, a rapid decay of the autocorrelation function, an excess of kurtosis and multivariate little cross-correlation across the series, except for contemporaneous returns. The analysis covers the pandemic period and sheds lights on the behaviour of cryptocurrencies under unexpected extreme events.

How the real estate indexes have performed during the COVID-19 crisis? Multifractal analysis revisited with wavelet

Purpose: The purpose of this paper is to test the existence of stylized facts, such as the volatility clustering, heavy tails seen on financial series, long-term dependence and multifractality on the returns of four real estate indexes using different types of indexes: conventional and Islamic by comparing pre and during COVID-19 pandemic. Design/methodology/approach: Firstly, the authors examined the characteristics of the indexes. Secondly, the authors estimated the parameters of the stable distribution. Then, the long memory is detected via the estimation of the Hurst exponents. Afterwards, the authors determine the graphs of the multifractal detrended fluctuation analysis (MF-DFA). Finally, the authors apply the WTMM method. Findings: The results suggest that the real estate indexes are far from being efficient and that the lowest level of multifractality was observed for Islamic indexes. Research limitations/implications: The inefficiency behavior of real estate indexes gives us an idea about the prediction of the behavior of future returns in these markets on the basis of past informations. Similarly, market participants would do well to reassess their investment and risk management framework to mitigate new and somewhat higher levels of risk of their exposures during the turbulent period. Originality/value: To the authors' knowledge, this is the first real estate market study employing STL decomposition before applying the MF-DFA in the context of the COVID-19 crisis. Likewise, the study is the first investigation that focuses on these four indexes. © 2023, Emerald Publishing Limited.

On stylized facts of cryptocurrencies returns and their relationship with other assets, with a focus on the impact of COVID-19

This study provides an empirical analysis on the main univariate and multivariate stylized facts iin return series of the two of the largest cryptocurrencies, namely Ethereum and Bitcoin. A Markov-Switching Vector AutoRegression model is considered to further explore the dynamic relationships between cryptocurrencies and other financial assets. We estimate the presence of volatility clustering, a rapid decay of the autocorrelation function, an excess of kurtosis and multivariate little cross-correlation across the series, except for contemporaneous returns. The analysis covers the pandemic period and sheds lights on the behaviour of cryptocurrencies under unexpected extreme events. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

On the stylized facts of precious metals’ volatility: A comparative analysis of pre- and during COVID-19 crisis

The main aim of this paper is to investigate the stylized facts associated with the volatility of precious metals before and during the COVID-19 pandemic using GARCH-type models. In particular, we employ an ARMA-GARCH, ARMA-EGARCH and ARMA-FIGARCH framework to account for volatility clustering, asymmetry and long memory in the volatility of gold, silver, platinum and palladium. Based on structural breaks, we divide the whole sample into sub-samples and find that the breakpoints occurred after the declaration of COVID-19 pandemic. Our results show a very distinct behaviour in the memory of the four metals before and during the crisis. While there is a moderate persistence in the full sample and in the pre-COVID-19 sub-period for the four metals, this effect vanishes after the crisis outburst. Positive asymmetric effects are also found in gold and silver volatilities, which intensify during COVID-19 phase. We ascribe this phenomenon to the hedge/safe-haven properties of these metals. By contrast, a diverse pattern is observed in the palladium and platinum volatilities, which display negative asymmetries before the pandemic, in tandem of financial markets. After the crisis, these metals show mixed evidence. Moreover, we argue that COVID-19 significantly affects the volatility of precious metals. © 2022 Elsevier B.V.

Testing volatility spillovers using GARCH models in the Japanese stock market during COVID-19

This paper investigates volatility spillovers in the stock market in Japan during the COVID-19 pandemic by using GARCH family models. The empirical analysis is focused on the dynamics of the NIKKEI 225 stock market index during the sample period from July 30, 1998, to January 24, 2022. In other words, the sample period covers both the period of the global financial crisis (GFC) and the COVID-19 pandemic. The econometrics includes GARCH (1,1), GJR (1,1), and EGARCH (1,1) models. By applying GARCH family models, this empirical study also examines the long-term behavior of the Japanese stock market. The Japanese stock market is much more stable and efficient than emerging or frontier markets characterized by higher volatility and lower liquidity. The paper establishes that NIKKEI 225 index dynamics is different in intensity in the case of the two most recent extreme events analyzed, namely the global financial crisis (GFC)of 2007-2008 and the COVID-19 pandemic. The findings confirmed the presence of the leverage effect during the sample period. Moreover, the empirical results identified the presence of high volatility in the sample returns of the selected stock market. Nevertheless, the econometric framework showed that the negative implications of the GFC were much more severe and caused more significant contractions compared to the COVID-19 pandemic for the Japanese stock market. This study contributes to the existing literature by providing additional empirical evidence on the long-term behavior of the stock market in Japan, especially in the context of extreme events. © 2022 LLC CPC Business Perspectives. All rights reserved.

Risk-Return Trade-off and Volatility Characteristics in the Indian Stock Market

The study examines the volatility characteristics of Indian stock markets and their tradeoff between the risk and return. It finds a positive but insignificant association between the risk and returns during the subsample (the pre-COVID and COVID pandemic outbreak) and whole sample periods. The study also shows that the weak form of Indian stock markets is not sustainable. Consistent with the GARCH literature, persistent and asymmetric effects are evidenced, and the magnitude of the negative shocks has a larger immediate impact than the positive shocks. These results would help measure the volatility in the Indian stock markets and provide investors and regulators with necessary information about the market efficiency, persistency (long-memory process) and asymmetric effects. © 2022 Manickam Tamilselvan et al;published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License