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This paper aims to investigate the immediate effect of the outbreak of the COVID-19 pandemic by investigating volatility transmission and dynamic correlation between stock (conventional and Islamic) markets, bitcoin and major commodities such as gold, oil and silver at different investment horizons before and after 161 trading days of the outbreak of the COVID-19 pandemic.

The MGARCH-DCC and maximum overlap discrete wavelet transform -based cross-correlation were used in the estimation of the volatility spillover and continuous wavelet transform in the estimation of the time-varying volatility and correlation between the assets at different investment horizons.

The authors observed a sudden correlation breakdown following the COVID-19 shock. Oil (Bitcoin) was a major volatility transmitter before (during) COVID-19. Digital gold (Bitcoin), gold and silver became highly correlated during COVID-19. The highest co-movement between the assets was observed at medium and long-term investment horizons.

The study findings have a financial implication for day traders, investors and policymakers in the understanding of volatility transmission and intercorrelation in a bid to actively manage stylized and well-diversified asset portfolios.

This study is unique for its employment in estimating the time-varying conditional volatility of the investable assets and cross-correlations between them at different investment horizons, particularly before and after COVID-19 outbreak.

This article examines dynamic volatility spillovers between stock index returns of four main hospitality sub-sectors in US during the coronavirus disease 2019 (COVID-19) pandemic. These are tourism and travel, hotel and lodging, recreational services and food and beverages. Volatility spillovers are explicitly used as accurate and informative proxies for risk contagion between sectors during turbulent times.

The authors employ dynamic conditional correlation-generalized autoregression heteroskedasticity (DCC-GARCH) and wavelet coherence analysis (WCA) to analyze the phenomenon. The authors’ timeframe is divided into three main sub-periods, namely the pre-pandemic, the first wave and the second wave periods.

This study’s results reveal immense negative shocks in returns of all four sub-sectors on the Black Monday (8th March 2020). Moreover, high volatility persistence was observed during both waves with an exception of tourism and travel which exhibited lower volatility persistence during the second wave. The authors discovered magnified contagion effects between tourism and travel, hotel and lodgment and recreational services during the first wave of the pandemic with tourism and travel being the main volatility transmitter. Lower magnitudes of spillovers were observed between food and beverages and other sub-sectors with a decoupling effect being evident during the second wave.

This study’s findings contribute to the contagion theory by providing evidence of disproportional volatility spillover among hospitality sub-sectors despite being exposed to similar turbulent economic conditions.

Crucial implications can be drawn from this study’s findings to assist in risk management, asset valuation and portfolio management. The importance of close monitoring, safety measures, international diversification and adequacy of liquid assets during health crises cannot be stresses enough for hospitality firms. Retail investors, speculators and asset managers can take advantage of this study’s findings to design trading strategies and hedge against risk.

A body of knowledge pertaining to effects of crises such as COVID-19 on hospitality stocks has been proliferating. Nonetheless, there is still a relative dearth of empirical literature on volatility spillover between hospitality sub-sectors especially during periods of rising economic uncertainties.

This study empirically investigates the interdependency of select Asian emerging economies along with the financial stress index during the times of the global financial crisis, the Euro crisis and the COVID-19 period. Moreover, it inspects the long-memory effects of the different crises during the study period.

To address the objectives of the study, the authors apply different statistical tools, namely the adjusted correlation coefficient, fractionally integrated generalised autoregressive conditional heteroskedasticity (FIGARCH) model and wavelet coherence model, along with descriptive statistics.

Financial stress is having a prodigious effect on the economic growth of select economies. From the data analysis, it is found that the long-memory effect is noted in the gross domestic product (GDP) for India and Korea only, which implies that the volatility in the GDP series for these two nations demonstrates persistence and dependency on previous values over a lengthy period.

The study is unique of its kind to consider multi-segments within the period of the study to get a clear idea about the effects of the financial stress index on select Asian emerging economies by applying different econometric tools.

This study seeks to examine the relationship between macroeconomic shock indicators, namely geopolitical risk (GPR), global economic policy uncertainty (GEPU) and financial stress (FS), and returns as well as volatilities on seven carefully selected stock markets in Africa. Specifically, the study intends to unravel the co-movement and interdependence between the respective macroeconomic shock indicators and each of the stock markets under consideration across time and frequency.

This study employed wavelet coherence approach to examine the strength and stability of the relationships across different time scales and frequency components, thereby providing valuable insights into specific periods and frequency ranges where the relationships are particularly pronounced.

The study found that GEPU, Financial Stress (FS) and GPR failed to induce significant influence on African stock market returns in the short term (0–4 months band), but tend to intensify in the long-term band (after 6th month). On the contrary, stock market volatilities exhibited strong coherence and interdependence with GEPU, FSI and GPR in the short-term band.

This study happens to be the first of its kind to comprehensively consider how the aforementioned macro-economic shock indicators impact stock markets returns and volatilities over time and frequency. Further, none of the earlier studies has attempted to examine the relationship between macro-economic shocks, stock returns and volatilities in different crisis periods. This study is the first of its kind in to employ data spanning from May 2007 to April 2023, thereby covering notable crisis periods such as global financial crisis (GFC) and the COVID-19 pandemic episodes.

In order to achieve sustainable development objectives, safeguard the ecosystem, combat global warming and preserve biodiversity for a more sustainable and secure future, the ecological footprint (EF) must be reduced. Therefore, embracing holistic methods, emphasizing renewable energy (RN) and environmental taxes (ET) is crucial. Therefore, the present study aims to capture the effect of ET and RN on EF in Germany.

To achieve this aim, the novel Fourier-based Autoregressive Distributive Lag (ADL) cointegration and the time and frequency-based connections among the variables are investigated in this work throughout the 1994–2021 time span using the wavelet analytic methods, including wavelet power spectrum (WPS) and wavelet coherence (WC) methods, respectively.

The study’s results express that (1) RN, ET and EF are cointegrated in the long run; (2) EF and RN have volatility; (3) RN use in Germany prevents environmental deterioration and (4) ET decreases EF.

The research findings imply that Germany needs rigorous environmental restrictions and enforcement of alternate energy sources for energy use plans and sustainable production objectives.

To the best of our knowledge, the effect of RN and ET on EF in Germany has not been comprehensively explored by using newly developed econometrics techniques and a single dataset. Therefore, the study provides important policy implementations for the German government and is also likely to open debate on the concept.

As the onset of the Russia–Ukraine military conflict on February 24, 2022, individuals from Ukraine have been relocating in search of safety and refuge. This study aims to investigate how the influx of Ukrainian refugees has impacted the stock markets and exchange rates of Ukraine's neighboring states.

The authors focused on the neighboring countries that share a western border with Ukraine and have received the highest number of refugees: Hungary, Poland, Romania and Slovakia. The analysis covered the period from April 24 to December 31, 2022. After this period, the influence of the refugees is small, insignificant. Wavelet coherence, wavelet power spectrum and the time-varying parameter vector autoregressions method were used for data processing.

The key finding are as follows: a link exists between the dynamics of refugees from Ukraine and volatility of the stock indices and exchange rate of the host countries; volatility was significant in the first weeks after the start of the conflict in all the analyzed states; and the highest volatility was recorded in Hungary and Poland; the effect of refugees was stronger on stock indices than that on exchange rates.

To the best of the authors’ knowledge, it is the first research that presents the impact of refugees from Ukraine on stock markets and exchange rates volatility in the countries analyzed.

This study is designed to examine the dynamic interrelationships between four cryptocurrencies (Bitcoin, Ethereum, Dogecoin and Cardano) and the Indian equity market. Additionally, the study seeks to investigate the potential safe haven, hedge and diversification uses of these digital currencies within the Indian equity market.

This study employs the wavelet approach to examine the time-varying volatility of the studied assets and the lead-lag relationship between stocks and cryptocurrencies. The authors execute the entire analysis using daily data from 1st October 2017 to 30th September 2023.

The result of the study shows that financial distress due to the pandemic and the Russian invasion of Ukraine have a negative effect on the Indian equities and cryptocurrency markets, escalating their price volatility. Also, the connectedness between the returns of stock and digital currency exhibits a strong positive relationship during periods of financial distress. Additionally, cryptocurrencies serve as a tool of diversification or hedging in the Indian equities markets during normal financial circumstances, but they do not serve as a diversifier or safe haven during periods of financial turmoil.

This study contributes to understanding the relationship between the Indian equity market and four cryptocurrencies using wavelet techniques in the time and frequency domains, considering both normal and crisis times. This can offer valuable insights into the potential of cryptocurrencies inside the Indian equities markets, mainly with respect to varying financial conditions and investment horizons.

This paper aims to accommodate the influence of both economic policy uncertainty and geopolitical risks in the relationship between oil price and exchange-rate returns in the Brazil, Russia, India, China and South Africa (BRICS) countries through an interaction term (EPGR).

The authors use continuous wavelet transform (CWT), wavelet coherence (WC) and partial wavelet coherence (PWC). First, the authors apply the CWT to examine the evolution of oil prices, EPGR and exchange rate returns. Second, the authors use WC to investigate the relationship between oil price and exchange rate returns (excluding EPGR). Third, the authors use PWC to account for EPGR’s impact on the oil exchange rate returns dynamics and explore partial correlations in the oil and exchange rate returns dynamics.

The empirical results generally show that EPGR is a key driver in the oil and exchange rate returns nexus.

The relevance of EPGR in influencing exchange rate volatility is confirmed by the findings. As a result, it is critical for government officials and foreign exchange investors to use EPGR as a leading indicator when establishing foreign exchange trading strategies and economic forecasts.

This study is the first, to the best of the authors’ knowledge, to apply a wavelet-based technique to account for EPGR in the relationship between oil and exchange rate returns in the BRICS countries.

This study investigates the nexus between the returns on oil prices (OP) and unemployment (UR) while taking into account the influences of two of the most representative measures of uncertainty, the Baker et al. (2016) and Caldara and Iacovello (2021) indexes of economic policy uncertainty (EP) and geopolitical risks (GP), in the relationship.

The authors use data on the US, Canada, France, Italy, Germany and Japan from January 2000 to February 2022 and the UK from January 2000 to December 2021. The authors then apply the continuous wavelet transform (CWT), wavelet coherence (WC), partial wavelet coherence (PWC) and multiple wavelet coherence (MWC) to examine the returns within a time and frequency framework.

The CWT tracks the movement and evolution of individual return series with evidence of high variances and heterogenous tendencies across frequencies that also align with critical events such as the GFC and COVID-19 pandemic. The WC reveals the presence of a bidirectional relationship between OP and UR across economies, showing that the two variables affect each other. The authors’ findings establish the predictive influence of oil price on unemployment in line with theory and also show that the variation in UR can impact the economy and alter the dynamics of OP. The authors employ the PWC and MWC to capture the impact of uncertainty indexes in the co-movement of oil price and unemployment in line with the theory of “investment under uncertainty”. Taking into account the common effects of EP and GP, PWC finds that uncertainty measures significantly drive the co-movement of oil prices and unemployment. This result is robust when the authors control for the influence of economic activity (proxied by the GDP) in the co-movement. Furthermore, the MWC reveals the combined intensity, strength and significance of both oil prices and the uncertainty measures in predicting unemployment across countries.

This study investigates the relationship between oil prices, uncertainty measures and unemployment under a time and frequency approach.

1. Wavelet approaches are used to examine the relationship between oil prices and unemployment in the G7.

2. We account for uncertainty measures in the dynamics of oil prices and unemployment.

3. We observe a bidirectional relationship between oil prices and unemployment.

4. Uncertainty measures significantly drive oil prices and unemployment co-movement.

5. Both oil prices and uncertainty measures significantly drive unemployment.

Wavelet approaches are used to examine the relationship between oil prices and unemployment in the G7.

We account for uncertainty measures in the dynamics of oil prices and unemployment.

We observe a bidirectional relationship between oil prices and unemployment.

Uncertainty measures significantly drive oil prices and unemployment co-movement.

Both oil prices and uncertainty measures significantly drive unemployment.

Due to its high leverage nature, a bank suffers vitally from the credit risk it inherently bears. As a result, managing credit is the ultimate responsibility of a bank. In this chapter, we examine how efficiently banks manage their credit risk via a powerful tool used widely in the decision/management science area called data envelopment analysis (DEA). Among various existing versions, our DEA is a two-stage, dynamic model that captures how each bank performs relative to its peer banks in terms of value creation and credit risk control. Using data from the largest 22 banks in the United States over the period of 1996 till 2013, we have identified leading banks such as First Bank systems and Bank of New York Mellon before and after mergers and acquisitions, respectively. With the goal of preventing financial crises such as the one that occurred in 2008, a conceptual model of credit risk reduction and management (CRR&M) is proposed in the final section of this study. Discussions on strategy formulations at both the individual bank level and the national level are provided. With the help of our two-stage DEA-based decision support systems and CRR&M-driven strategies, policy/decision-makers in a banking sector can identify improvement opportunities regarding value creation and risk mitigation. The effective tool and procedures presented in this work will help banks worldwide manage the unknown and become more resilient to potential credit crises in the 21st century.