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The purpose of this study is to achieve a comprehensive understanding of how the intricate interconnections between oil price fluctuations, supply chain disruptions and shifting demand patterns collectively shape inflation dynamics within the Chinese economy, especially during critical periods such as the Covid-19 pandemic and geopolitical events like the Russia–Ukraine conflict. The importance of assessing the impact of oil price volatility on China’s inflation becomes particularly pronounced amidst these challenging circumstances.

This study uses the Markov Regime-Switching generalized autoregressive conditional heteroskedasticity (MRS-GARCH) family of models under student’s t-distributions to measure the uncertainty of oil prices and the inflation rate during the period spanning from 1994 to 2023 in China.

The results indicate that the MRS-GJR-GARCH-in-mean (MRS-GARCH-M) models, when used under student’s t-distributions, exhibit superior performance in modeling the volatility of both oil prices and the inflation rate. This finding underscores the effectiveness of these models in capturing the intricacies of volatility dynamics in the context of oil prices and inflation. The study has identified compelling evidence of regime-switching behavior within the oil price market. Subsequently, the author conducted an analysis by extracting the forecastable component, which represents the expected variation, from the best-fitted models. This allowed us to isolate the time series of oil price uncertainty, representing the unforecastable component. With this unforecastable component in hand, the author proceeded to estimate the impact of oil price fluctuations on the inflation rate. To accomplish this, the author used an autoregressive distributed lag model, which enables us to explore the dynamic relationships and lags between these crucial economic variables. The study further reveals that fluctuations in oil prices exert a noteworthy and discernible influence on the inflation rate, with distinct patterns observed across different economic regimes. The findings indicate a consistent positive impact of oil prices on inflation rate uncertainty, particularly within export-oriented and import-oriented industries, under both of these economic regimes.

This study offers original value by analyzing the impact of crude oil price volatility on inflation in China. It provides unique insights into the relationship between energy market fluctuations and macroeconomic stability in one of the world’s largest economies. By focusing on crude oil – a critical but often overlooked component – this research enhances understanding of how energy price dynamics influence inflationary trends. The findings can inform policymakers and stakeholders about the significance of energy market stability for maintaining economic stability and guiding inflation control measures in China.

This paper aims to introduce a heteroskedastic hidden truncation normal (HTN) model that allows for conditional volatilities, skewness and kurtosis, which evolve over time and are linked to economic dynamics and have economic interpretations.

The model consists of the HTN distribution introduced by Arnold et al. (1993) coupled with the NGARCH type (Engle and Ng, 1993). The HTN distribution nests two well-known distributions: the skew-normal family (Azzalini, 1985) and the normal distributions. The HTN family of distributions depends on a hidden truncation and has four parameters having economic interpretations in terms of conditional volatilities, kurtosis and correlations between the observed variable and the hidden truncated variable.

The model parameters are estimated using the maximum likelihood estimator. An empirical application to market data indicates the HTN-NGARCH model captures stylized facts manifested in financial market data, specifically volatility clustering, leverage effect, conditional skewness and kurtosis. The authors also compare the performance of the HTN-NGARCH model to the mixed normal (MN) heteroskedastic MN-NGARCH model.

The paper presents a structure dynamic, allowing us to explore the volatility spillover between the observed and the hidden truncated variable. The conditional volatilities and skewness have the ability at modeling persistence in volatilities and the leverage effects as well as conditional kurtosis of the S&P 500 index.

A stylized fact in finance literature is the belief in positive relationship between ex ante return and risk. Hence, a rational investor, by utility preference axiom can only consider committing fund in asset which promises commensurate higher return for higher risk. Questions have been asked as to whether this holds true across securities, sectors and markets. Empirical evidence appears less convincing, especially in developing markets. Accordingly, the author investigates the nature of reward for taking risk in the Nigerian Capital Market within the context of individual assets and markets.

The author employed ex post design to collect weekly stock prices of firms listed on the Premium Board of Nigerian Stock Exchange for period 2014–2022 to attempt to answer research questions. Data were analyzed using a unique M Vec TGarch-in-Mean model considered to be robust in handling many assets, and hence portfolio management.

The study found that idea of risk-expected return trade-off is perhaps more general than as depicted by traditional finance literature. The regression revealed that conditional variance and covariance risks reveal minimal or no differences in sign and sizes of coefficients. However, standard errors were also found to be large suggesting somewhat inconclusive evidence of existence of defined incentive structure for taking additional risk in the market.

In terms of choice of methodology and outcomes, this research adds substantial value to body of knowledge. The adapted multivariate model used in this paper is a rare approach especially for management of portfolios in developing markets. Remarkably, the research found empirical evidence that positive risk-expected return trade-off, as known in mainstream literature, is not supported especially using a typical developing country data.

The current research aims to investigate the impact of coronavirus 2019 (COVID-19) evolution, government stringency measures and economic resilience on stock market volatility in the Middle East and North African (MENA) emerging markets. Other macroeconomic factors were also taken into account.

Based on financial data from 10 selected MENA countries, we tested an integrated framework that has not yet been explored in prior research. The exponential generalized autoregressive conditional heteroskedasticity (E-GARCH) was adopted to analyze data from March 2020 to February 2022.

Our research illustrates the direct and indirect effects of the virus outbreak on stock market stability and reports that economic resilience could alleviate the volatility shock. This finding is robust across the various proxies of economic resilience used in this study. We also argue that the negative impact of the pandemic on equity market variation gets more pronounced in countries with higher level of stringency scores.

Policymakers ought to strengthen their economic structures and reinforce the economic governance at the national level to gain existing and potential investors’ trust and ensure lower stock market volatilities in times of crisis. Our study also recommends some key economic factors to consider while establishing efficient policies to tackle unexpected shocks and prevent financial meltdowns.

Our findings add to the evolving literature on the reaction of economic and financial markets to the sanitary crisis, particularly in developing countries where research is still scarce. This study is the first of its kind to investigate the stock market reaction to stringency measures in the understudied MENA region.

From poor healthcare infrastructure to vaccine donors, India has traveled a long way. In this study, the author tried to find the investment certainty and persistence of volatility in the Indian healthcare system due to COVID-19.

Using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH 1,1) model, this study quantifies the change in the conditional variance after the first case report of COVID-19. The author has used the S&P BSE HEALTHCARE index time series to analyze India’s healthcare infrastructure and practices.

The author found evidence of a decrease in investment certainty in investments related to India’s healthcare infrastructure and practices after the first case report of COVID-19. Furthermore, the estimation of the econometric model suggests the presence of a large degree of volatility persistence in the S&P BSE HEALTHCARE index.

This research would be the first of its kind where the return volatility of the Indian healthcare sector has been discussed. Also, this research quantifies the return volatility of the healthcare sector during the pre- and post-COVID-19 period.

This study aims to test the contagion effect of the Tunisian revolution on the Egyptian stock market. Thus, the purpose of this research is to distinguish the contagion effect from the simple interdependence between these markets.

This paper examines the contagion hypothesis between Tunisia and Egypt during the Arab Spring, using a DCC-MGARCH model to capture time-varying contagion effects and dynamic linkages in stock markets. Therefore, to identify the contagion effect from the simple interdependence, the authors apply the pure contagion test developed by Forbes and Rigobon (2002).

The findings indicate a contagion effect, as the EGX 30 index exhibited similar changes, positive or negative, as the Tunindex index during the period of the Tunisian revolution. Moreover, the analysis demonstrates the presence of an interdependence between the Tunisian revolution and the Egyptian market, emphasizing the interconnections between these two economies.

The findings provide investors with a better understanding of financial market dynamics in times of major political unrest, notably on the Tunisian and Egyptian markets. By understanding the contagion effect of the Tunisian revolution on the Egyptian stock market, investors can further explore the complexities of these markets in times of financial crises, which can help mitigate losses and identify strategic investment opportunities.

This study makes two significant contributions to the field. First, it addresses the scarcity of research specifically focused on the contagion effect during the Arab Spring, aiming to fill this gap by testing the contagion effect of the Tunisian revolution on a nearby market. Second, it extends the contagion test of Forbes and Rigobon (2002), which associates “pure” contagion with a significantly higher correlation between markets during a crisis.

This paper aims to investigate the safe haven feature of Bitcoin, gold and two gold-backed cryptocurrencies (DGX and PAXG) against energy and agricultural commodities (crude oil, natural gas and wheat) during the COVID-19 pandemic, the Russia–Ukraine conflict and the Silicon Valley Bank (SVB) collapse.

The authors use the threshold GARCH (T-GARCH)-asymmetric dynamic conditional correlation (ADCC) model to evaluate the asymmetric dynamic conditional correlation between the return series and compare the diversifying, hedging and safe-haven ability of Bitcoin, gold and the two gold-backed cryptocurrencies (DGX and PAXG) against financial swings in the commodity market during the COVID-19 outbreak, the Russian–Ukrainian military conflict and SVB collapse. The authors also calculate the hedging ratios (HR) and hedging effectiveness index (HE). The authors finally use the wavelet coherence (WC) approach to check our results’ robustness and further investigate the impact of the three crises on the relationship between Bitcoin, gold gold-backed cryptocurrencies and commodities.

The results show that PAXG serves as a strong hedging instrument while gold, Bitcoin and DGX act as strong diversifiers during normal times. During crises, gold outperforms Bitcoin as a diversifier and a safe haven against commodities. Gold-backed cryptocurrencies also exhibit strong performance as diversifiers and safe havens. HR results indicate that Bitcoin and DGX are more cost-effective for commodities risk mitigation than gold and PAXG. In terms of hedging effectiveness, gold and PAXG emerge as the best hedging instruments for commodities, while DGX is considered the worst one. Bitcoin shows superior hedging against oil compared to wheat and gas risks. Moreover, the results of the WC approach confirm those of the T-GARCH-ADCC results in both the short and long run.

This paper provides a comprehensive analysis of the diversification ability of gold, Bitcoin and gold-backed cryptocurrencies during different crises (the COVID-19 pandemic, the Russia–Ukraine conflict and the SVB collapse). By taking into consideration gold-backed cryptocurrencies, the authors expand the understanding of safe havens beyond conventional assets.

This paper aims to examine the dynamic conditional correlation (DCC) and hedging ratios between Dow Jones markets and oil, gold and bitcoin. Using daily data, including the COVID-19 pandemic and the Russia–Ukraine war. We employ the DCC-generalized autoregressive conditional heteroskedasticity (GARCH) and asymmetric DCC (ADCC)-GARCH models.

DCC-GARCH and ADCC-GARCH models.

The most of DCCs among market pairs are positive during COVID-19 period, implying the existence of volatility spillovers (Contagion-effects). This implies the lack of additional economic gains of diversification. So, COVID-19 represents a systematic risk that resists diversification. However, during the Russia–Ukraine war the DCCs are negative for most pairs that include Oil and Gold, implying investors may benefit from portfolio-diversification. Our hedging analysis carries significant implications for investors seeking higher returns while hedging their Dow Jones portfolios: keeping their portfolios unhedged is better than hedging them. This is because Islamic stocks have the ability to mitigate risks.

Our paper may make a valuable contribution to the existing literature by examining the hedging of financial assets, including both conventional and Islamic assets, during periods of stability and crisis, such as the COVID-19 pandemic and the Russia–Ukraine war.

This study aims to analyze the volatility spillover effects of crude oil, gold price, interest rate (yield) and the exchange rate (USD (United States Dollar)/INR (Indian National Rupee)) on inflation volatility in India.

This study uses the multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models (Baba, Engle, Kraft and Kroner [BEKK]-GARCH and dynamic conditional correlation [DCC]-GARCH) to examine the volatility spillover effect of macroeconomic indicators and strategic commodities on inflation in India. The monthly data are collected from January 2000 till December 2020 for the crude oil price, gold price, interest rate (5-year Indian bond yield), exchange rate (USD/INR) and inflation (wholesale price index [WPI] and consumer price index [CPI]).

In BEKK-GARCH, the results reveal that crude oil price volatility has a long time spillover effect on inflation (WPI). Furthermore, no significant short-term volatility effect exists from crude oil market to inflation (WPI). However, the short-term volatility effect exists from crude oil to inflation while considering CPI as inflation. Gold price volatility has a bidirectional and negative spillover effect on inflation in the case of WPI. However, there is no price volatility spillover effect from gold to inflation in the case of CPI. The price volatility in the exchange rate also has a negative spillover effect on inflation (but only on CPI). Furthermore, volatility of interest rates has no spillover effect on inflation in WPI or CPI. In DCC-GARCH, a short-term volatility impact from all four macroeconomic indicators to inflation is found. Only crude oil and exchange rate have long-term volatility effect on inflation (CPI).

In an economy, inflation management is an essential task. The findings of the current study can be beneficial in this endeavor. The knowledge of the volatility spillover effect of all the four markets undertaken in the study can be significantly helpful in inflation management, especially for inflation-targeting policy.

It is observed that no other study has addressed this issue. We do not find any other research which studies the volatility spillover effect of gold, crude oil, interest rate and exchange rate on the inflation volatility. The current study is novel with a significant contribution to the vast knowledge in this context.