Do cryptocurrencies integrate with the indices of equity, sustainability, clean energy, and crude oil? A wavelet coherency approach

The paper examines market co-movement between pairs of financial assets in the time-frequency 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 short-term scales and grow stronger in the medium-term scales, especially during the COVID-19 period, and further exhibit weaker heterogeneous associations in the long-term 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 COVID-19 periods (Medium-term), which facilitate the domestic and international investors expecting to hedge their price risk in equity markets using cryptocurrencies may have to look for short-term. The lead-lag heterogeneous effects of the asset-pairs may pave arbitrage opportunities for investors.

The impact of COVID-19 pandemic on the volatility of conventional and Islamic stock indexes: a comparative study on ASEAN and GCC countries

PurposeThe purpose of the study is to adopt Morlet's wavelet method to examine the differences in the level of volatility (i.e. riskiness) between the conventional and Shari'ah indexes during the COVID-19 pandemic (February 4 to June 19, 2020) on selected Association of South East Asian Nation (ASEAN) and Gulf Cooperation Council (GCC) countries. As a comparison, the equivalent time period of relative tranquillity is used;February 4 to June 19, 2019.Design/methodology/approachMorlet's wavelet method is used in analyzing the volatility levels for both the conventional and Shari'ah indexes before and during the COVID-19 pandemic for the selected ASEAN and GCC countries.FindingsThis study has several findings;first, the markets in the ASEAN region appear to be more volatile during the pandemic than in the GCC region. Second, most of the Shari'ah indexes were more volatile during the COVID-19 pandemic than their conventional counterparts. Nevertheless, the GCC index pairs appear to show more similarities between both the Shari'ah and conventional index.Practical implicationsThe findings from this study indicate that investors, government, regulators and all other stakeholders should stay vigilant during a pandemic or health threat period as it has become a pertinent source of volatility spillovers. As such, investors should devise optimal asset allocation strategies, portfolio diversification and portfolio rebalancing measures, taking into consideration not only financial adversity but also public health gravity as a potential source of turbulent markets.Originality/valueThis study uses the wavelet method to examine the volatility level of both the Shari'ah and conventional indexes during the COVID-19 pandemic and its equivalent time frame in 2019. It has further added to the Islamic literature by comparing the volatility between selected ASEAN and GCC countries. The wavelet method is most appropriate for short-duration studies as it captures both the time and frequency domains of the time-series behavior.

Evidence of COVID-19's financial epidemiology on the ASEAN-5 stock indices

Little research has been done to determine whether the pandemic's impact is strong enough to innovate all of the ASEAN-5 stock indices. The traditional studies of financial epidemiology in regional equities mainly focus on the major global stock markets. This paper utilizes the conventional t-test and the advanced computational power of the Wavelet Power Energy Spectrum (WPES) to investigate the magnitude of the significance of the COVID-19 impacts on the ASEAN-5 stock indices. Our t-test confirms that the pandemic has caused significant changes to the overall stock index activities. Further, the WPES analysis yielded notable results based on the spectrogram plots. First, based on the spectral analysis, during pandemic, the ASEAN-5 stock indices experienced episodes of innovation in terms of market activities. It was also observed that the regional stock indices experienced phases of volatility persistence, volatility clustering and long memory of up to four months. We conclude that, due to the impact of the pandemic, trend-following investors can't dominate the market as they react quickly and efficiently to new information. It is suggested that asset allocation strategists particularly should regularly review and conduct climate tests on their baskets to ensure their positions are durable and resistant to shocks. The results of this study offer significant insights for both institutional and retail investors, particularly in strategizing investment baskets during uncertainties. © 2023 AESS Publications. All Rights Reserved.

Did the Market Indices of G7 Countries Recover Evenly from the Start of COVID-19 (January 2020) Through December 2021?

Earlier work documented how COVID-19 affected the performance of the stock market indices around the world (Bieszk-Stolorz and Dmytrow, 2021;Lento and Gradojevic, 2021). Research has yet to investigate the longer-term recovery of these market indices. From a buy-and-hold perspective, this paper compares the recovery of indices in G7 countries and Hong Kong from the beginning of the pandemic in January 2020 to June 2021. The empirical results show that the null hypothesis of equal individual monthly returns in the indices of G7 countries and Hong Kong cannot be rejected. However, the null hypothesis of equal buy-and-hold returns in the indices of G7 countries and Hong Kong from January 2020 through June 2021 can be rejected, indicating that the market recovery status among the G7 countries and Hong Kong from the start of COVID-19 in January 2020 through June 2021 has been uneven and unequal. Copyright © 2022 GMP Press and Printing.

Dynamic causality between oil prices and stock market indexes in Russia and China: does US financial instability matter?

Purpose: This study aims to examine the dynamic bidirectional causality between oil price (OIL) and stock market indexes in net oil-exporting (Russia) and net oil-importing (China) countries. Design/methodology/approach: The authors use monthly data for the period starting from October 1995 to October 2021. In this study, the bootstrap rolling-window Granger causality approach introduced by Balcilar et al. (2010) and the probit regression model are performed in order to identify the bidirectional causality. Findings: The results show that the causal periods mainly occur during economic, financial and health crises. For oil-exporting country, the results suggest that any increase (decrease) in the OIL leads to an appreciation (depreciation) in the stock market index. The effect of the stock market on OIL is more relevant for the oil-importing country than that for the oil-exporting one. The COVID-19 consequences are demonstrated in the impact of oil on the Russian stock market. The probit regression shows that the US financial instabilities increase the probability of causality between OIL and stock market indexes in Russia and China. Practical implications: The dynamic relationship between the variables must be taken into account in investment decisions. As financial instabilities in the USA drive the relationship between oil and stocks, investors should consider geopolitical, economic and financial elements when constructing their portfolios. Shareholders are required to include other assets in their portfolios since oil–stock relationship is highly risky. Originality/value: This study provides further evidence of the bidirectional oil–stock causal link. Additionally, it examines the impact of financial instabilities on the probability that the OIL and the stock market index cause each other through the Granger effect. © 2023, Emerald Publishing Limited.

Price guidance and discovery of the Chinese stock index Futures: Based on the rising, falling and fluctuating states.

Stock index futures have been around for more than 12 years in the Chinese market, but there are conflicting viewpoints on the role of Chinese stock index futures in the market. Many crises, including COVID-19, have heightened the financial system's vulnerability and instability. Do China's stock index futures play a role in stabilizing the market and discovering prices in turbulent times? This study employs a combination of VEC, PT, and IS methodologies to investigate the lead-lag relation and price discovery ability of stock index futures markets. By evaluating price interactions in three different scenarios, we reveal that stock index futures play a prominent role in price discovery, particularly in markets with excessive volatility. However, their impact on price discovery is weaker during stable market conditions. To the best of our knowledge, this study is the first to categorize the Chinese stock market into different states, providing valuable insights into the price discovery function of stock index futures. Our findings have important implications for policymakers and investors, as they highlight the need for increased attention to market manipulation and excessive speculation during volatile market conditions to protect the interests of investors.

Volatility and correlation of Islamic and conventional indices during crises

We examine the correlation and volatility of Islamic indices and their conventional counterparts during the 2008–2009 Global Financial Crisis as well as the Covid-19 pandemic. We provide evidence that the volatility of Islamic indices is relatively lower than that of conventional peers during turmoil periods. Consistent with the decoupling hypothesis, our results indicate that the volatility of Islamic and the volatility of conventional indices tend to move in tandem in tranquil times but diverge in times of crises. Our results also indicate that the correlation between Islamic and conventional indices is a priced risk factor for Islamic index returns. © 2022 Elsevier Inc.

Impact of oil prices on the Islamic and conventional stock indexes’ performance in Malaysia during the COVID-19 pandemic: Fresh evidence from the wavelet-based approach

The motivation behind conducting this research is to study the association between oil prices and Islamic and conventional stock indexes’ performance in the Malaysian market during COVID-19 using the wavelet analysis technique. The daily data on selected variables were collected from 1 January 2020, to 10 June 2021. Empirical investigation was made with wavelet analysis along with the Toda-Yamamoto test. The results revealed the significant response of both indexes to the oil price. Such response was negative for the short- and medium terms;however, it became positive in the long run. Our research has several important implications and recommendations for asset managers and policymakers. Policymakers and regulators should promote awareness and adopt effective action plans to minimize the risk of change in oil prices during the COVID-19 period. This research will enable investors, scholars, and policymakers to improve their current structure and prepare them for any potential future crisis. Copyright © 2022 Khan, Sharif, Islam, Ali, Fareed and Zulfaqar.

The impact of COVID-19 pandemic on the volatility of conventional and Islamic stock indexes: a comparative study on ASEAN and GCC countries

Purpose The purpose of the study is to adopt Morlet's wavelet method to examine the differences in the level of volatility (i.e. riskiness) between the conventional and Shari'ah indexes during the COVID-19 pandemic (February 4 to June 19, 2020) on selected Association of South East Asian Nation (ASEAN) and Gulf Cooperation Council (GCC) countries. As a comparison, the equivalent time period of relative tranquillity is used;February 4 to June 19, 2019. Design/methodology/approach Morlet's wavelet method is used in analyzing the volatility levels for both the conventional and Shari'ah indexes before and during the COVID-19 pandemic for the selected ASEAN and GCC countries. Findings This study has several findings;first, the markets in the ASEAN region appear to be more volatile during the pandemic than in the GCC region. Second, most of the Shari'ah indexes were more volatile during the COVID-19 pandemic than their conventional counterparts. Nevertheless, the GCC index pairs appear to show more similarities between both the Shari'ah and conventional index. Practical implications The findings from this study indicate that investors, government, regulators and all other stakeholders should stay vigilant during a pandemic or health threat period as it has become a pertinent source of volatility spillovers. As such, investors should devise optimal asset allocation strategies, portfolio diversification and portfolio rebalancing measures, taking into consideration not only financial adversity but also public health gravity as a potential source of turbulent markets. Originality/value This study uses the wavelet method to examine the volatility level of both the Shari'ah and conventional indexes during the COVID-19 pandemic and its equivalent time frame in 2019. It has further added to the Islamic literature by comparing the volatility between selected ASEAN and GCC countries. The wavelet method is most appropriate for short-duration studies as it captures both the time and frequency domains of the time-series behavior.

The relationship between global stock and precious metals under Covid-19 and happiness perspectives.

In this paper, we examine the relationship between global stock markets, as respectively represented by the FTSE All-World Series and the MSCI Emerging Markets indexes, and the S&P GSCI Precious Metals index from 01 September 1999 to 03 May 2021. We employ the conditional correlation multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) to investigate this stock-precious metals nexus in terms of return and volatility spillovers. The study assesses impacts of the Covid-19 pandemic on the stock-precious metals nexus and further examine this relationship by supplementing the Twitter's Daily Happiness Sentiment index to the methodological framework for the period from 01 January 2020 to 03 May 2021. We find that precious metals positively influence stock markets before the Covid-19 outbreak and firmly play a valuable role due to their hedge and safe haven characteristics. In contrast, the bivariate GARCH framework does not provide statistically significant evidence on the stock-precious metals nexus during the Covid-19 pandemic. Meanwhile, the tri-variate GARCH approach with stock markets, precious metals, and happiness sentiment indexes reveals sufficiently complicated interactions between these return series. Prominently, past change in the happiness index negatively affects the stock returns but positively drives the performance of precious metals. These findings indirectly demonstrate the stock-precious metals nexus under impacts of the Covid-19 pandemic and reflect the demand of precious metals during crisis periods. Accordingly, we suggest a reasonable method of adjusting the proxies when no interaction effect is significantly found during unprecedented outbreaks.

Risk analysis of China's stock markets based on topological data structures

We choose 100 stocks from China's markets and use their daily returns from January 3, 2013 to August 31, 2020 to investigate the risk situation in China's stock markets by exploring their correlations in the sample period. We build complexes and carry out topological data analysis on them. The persistence landscapes and their LP-norms show that there are three clear turbulent periods since 2013. The dates are then detected when the stocks are strongly correlated. As is well known, the financial risks easily break out and spread in such situations, so we call the dates critical dates for risks. We can also take them as the early warning signals for potential risks. We then construct planar maximal filtered graphs on the critical dates to help discover the systematically important companies. We find that they changed obviously in three different turbulent periods. It reminds us to analyze the risks' characteristics of the risks and implement risk prevention. The method combing topological data analysis and complex networks is shown to be effective in detecting critical information from markets, and hence is worth popularizing. © 2022 The Authors. Published by Elsevier B.V.

Comparative Analysis of Major Stock Markets in Relation to Global Financial Crises: Evidence from 2000 to the COVID-19 Pandemic

We analyze the impact of financial crises on major stock markets from 2000 to the COVID-19 pandemic using Fourier series. Analyzing the behaviors of the spectra obtained from monthly returns of their indices, we identify three global financial crises from 2000 to 2015, with different characteristics. In addition, applying Z-test and the color-contour plotting method to monthly propagations of the spectra of major frequencies from the monthly returns of each index, we analyze the developments in each market around the crises by comparing patterns in the color-contour plots. Using recent status analysis, we identify an instability around 2016 close to a real crisis;starting in 2020, the markets, which had already recovered from this instability have generated abnormal signals of an approaching crisis. Applying Z-test and color-contour plotting to monthly propagations from the recent status, we show that recent developments in major markets might be more serious than those occurring around previous financial crises. © 2022, Kauno Technologijos Universitetas. All rights reserved.

Fuzzy Time-Series Models Based on Intuitionistic Fuzzy, Rough Set Fuzzy, and Differential Evolution

Time-series forecasting is a vital concern for any data having temporal variations. Comparing with the other conventional time-series methodologies, the fuzzy time-series (FTS) proved its superiority. Substantial research using time-series forecasting to predict the stock index data has been found in the earlier works. The fuzzy sets approach alone cannot explain the data thoroughly. In this article, we have proposed three different methods of time-series forecasting. The first method is based on a rough set of FTS, a rule induction-based method;the second method is based on intuitionistic FTS. The last method is the extension of the second method using differential evolution. In the first model, a fuzzy algorithm based on rules is used to derive prediction rules from the time-series data and adopt an adaptive expectation model that replaces the fuzzy logical relationships or groups. In the second method, to split the universe of discourse into a non-uniform interval, a clustering algorithm-based intuitionistic fuzzy approach is used, taking care of the membership and non-membership function. Finally, the last method has been tuned for a better outcome using differential evolution. To examine the results, contrast analyses on the Taiwan stock exchange data and daily cases of COVID-19 pandemic prediction have been carried out. The outcome of the proposed approaches validates that the first and second techniques, showing promising results. However, the third method outperforms the other methods and the present techniques concerning the root-mean-square error metric. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Dynamics lead-lag relationship of jumps among Chinese stock index and futures market during the Covid-19 epidemic.

This paper introduces thermal optimal path method to investigate the dynamics lead-lag relationship of jumps among Chinese stock index and futures market under the background of the Covid-19 epidemic. Based on three representative stock indexes and their index futures in China, we find the lead-lag structure changes significantly before and after the outbreak of COVID-19. Before the epidemic, there is mutual effect between different markets jumps. However, CSI 300 futures and SSE 50 futures significantly lead other markets for the after-epidemic period. For the volatility forecasting based on cross-market jumps, the lagged jumps of CSI 300 and SSE 50 index futures have significantly impacts on the volatility forecast of other markets.

Impact of COVID-19 pandemic on stock market via sparse principal component analysis

The COVID-19 pandemic has caused severe public health and economic consequences around the world. It is of great importance to evaluate the impact of the COVID-19 pandemic on the economy, especially the stock market. To this end, we proposed to use several state-of-art sparse principal component analysis (PCA) methods for the stock data of the CSI 300 index from February 1, 2019 to February 1, 2021. To show the influence of the outbreak of the COVID-19 pandemic, we divide this period into two periods, i. e., before and after January 1, 2020. Based on this division, we attempted to extract the principal components and construct portfolio accordingly. The results show that the proportion of principal components representing the market declined after the outbreak. For the constitution in the first two principal components, the important stock sets are substantially different after the outbreak. The stocks from the health care sector start to play an important role in the portfolio of the CSI 300 index after the outbreak. Compared with the CSI 300 index, the first two principal components from the sparse PCA methods can obtain higher returns with a much smaller set of stocks in the portfolio. In conclusion, the outbreak of the COVID-19 pandemic led to changes in both proportion and constitution of the principal component of the stocks in the CSI 300 index. © 2021, Editorial Department of Journal of University of Science and Technology of China. All rights reserved.

The daily urban dynamic indicator: Gauging the urban dynamic in Porto during the COVID-19 pandemic.

The SARS-CoV-2 outbreak motivated the development of a myriad of weekly and daily indicators that track economic activity to estimate and predict the consequences of the pandemic. With some exceptions, these indicators are calculated at the country level and are mainly focused on tracking economic factors, disregarding local urban phenomena. To address this, we present the Urban Dynamic Indicator (UDI), a novel composite indicator designed to measure a city's daily urban dynamic. The UDI is applied to Porto municipality, in Portugal, and it corresponds to a latent factor obtained through a factor analysis over seasonal adjusted daily data regarding traffic intensity, public transportation usage, internet usage in public buses, NO2 emissions and noise level. The UDI's values show that, by the end of 2020, despite the approach of economic activity to its pre-pandemic values, as suggested by the Portuguese Daily Economic Indicator (DEI), Porto urban dynamic did not recover completely. The UDI enriches the information available for Porto city planners and policymakers to respond to crisis situations and to gauge the application of local policies that contribute to urban sustainable planning. Furthermore, the methodology defined in this work can be followed for the development of daily urban dynamic indicators elsewhere.

The COVID-19 pandemic, short-sale ban, and market efficiency: empirical evidence from the European equity markets

This study looks at the inefficiency of stock indices of France, Italy, and Spain around their financial regulatory authorities' short-sale ban during the COVID-19 pandemic crisis. The empirical analysis of this study provides evidence of price predictability of the basis of futures contract prior to the short-sale restriction. Moreover, the results show a significant underpricing in futures contracts of FTSE MIB and IBEX35 indices while the two months of short-sale banned period. These findings suggest that prohibiting short selling during the market downturn might undermine the stock markets' efficiency and generate arbitrage opportunities for speculative investors.

Multifractal Behaviors of Stock Indices and Their Ability to Improve Forecasting in a Volatility Clustering Period.

The financial market is a complex system, which has become more complicated due to the sudden impact of the COVID-19 pandemic in 2020. As a result there may be much higher degree of uncertainty and volatility clustering in stock markets. How does this "black swan" event affect the fractal behaviors of the stock market? How to improve the forecasting accuracy after that? Here we study the multifractal behaviors of 5-min time series of CSI300 and S&P500, which represents the two stock markets of China and United States. Using the Overlapped Sliding Window-based Multifractal Detrended Fluctuation Analysis (OSW-MF-DFA) method, we found that the two markets always have multifractal characteristics, and the degree of fractal intensified during the first panic period of pandemic. Based on the long and short-term memory which are described by fractal test results, we use the Gated Recurrent Unit (GRU) neural network model to forecast these indices. We found that during the large volatility clustering period, the prediction accuracy of the time series can be significantly improved by adding the time-varying Hurst index to the GRU neural network.

Assessing the impact of COVID-19 on major industries in Japan: A dynamic conditional correlation approach.

This study assesses the impact of the novel coronavirus disease (COVID-19) cases on the Japanese stock market. As of October 30, 2020, the cumulative number of cases in Japan has reached over one hundred thousand. COVID-19 has significantly affected both the lifestyle and the economy in Japan. First, this study develops composite stock indices by industry sector and prefecture, taking into consideration the effects of the increase in infections on industries and firms in the core prefectures. Second, this study investigates the dynamic conditional correlations between the composite stock index returns and the increment in COVID-19 cases using dynamic conditional correlation multivariate GARCH models. Finally, it can contribute to financial research in terms of coexistence of regional business economies with COVID-19.

Analysis of global stock markets' connections with emphasis on the impact of COVID-19.

We explore global stock markets' connections during the financial crises or risks since 1995 with emphasis on the situation under COVID-19. We choose 40 countries/regions and take one index from each of them, and then compute the correlation coefficients and distances between each pair of the indices with a sliding window. We construct the complexes and carry out topological data analysis mainly through persistence landscapes and their L p -norms, which exhibit the complexes' daily changes. We establish a critical dates' detection system based on the persistence landscapes. Topological features of the complex networks are shown on the critical dates and dates before them. All the results show clearly that the connections became even closer among the markets when COVID-19 spread worldwide than those of any other risk. The robustness and effectiveness of these methods provide guidance for the analysis of financial crises in the future.