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This paper assesses the co-movement between Pax gold and six fiat currencies. It also investigates the optimal time-varying hedge ratios in order to examine the properties of Pax gold as a diversifier and hedge asset.

This paper examines the volatility spillover between Pax gold and fiat currencies using the framework of wavelet analysis, BEKK-GARCH models and Range DCC-GARCH. Moreover, this paper proposes to use the covariance and variance structure obtained from the new range DCC-GARCH framework to estimate the time-varying optimal hedge ratios, the optimal weighs and the hedging effectiveness.

Wavelet coherence method reveals that, at low frequency, large zone of co-movements appears for the pairs Pax gold/EUR, Pax gold/JPY and Pax gold/RUB. Further, the BEKK results show unidirectional (bidirectional) transmission effects between Pax gold and EUR, GBP, JPY and CNY (INR, RUB) fiat currencies. Moreover, the Range DCC results show that the Pax gold and the fiat currency returns are weakly correlated with low coefficients close to zero. Thus, Pax gold seems to serve as a safe haven asset against the systematic risk of fiat currency markets. In addition, the results of optimal weights show that rational investor should invest more in Pax gold and less in fiat currencies. Concerning the hedge ratios results, the findings reveal that the INR (JPY) fiat currency appears to be the most expensive (cheapest) hedge for the Pax-gold market. However, the JPY’s fiat currency appears to be the cheapest one. As for hedging effectiveness results, the authors found that hedging strategies including fiat currencies–Pax gold pairs are most likely to sharply decrease the portfolio’s risk.

A comprehensive understanding of the relationship between Pax Gold and fiat currencies is crucial for refining portfolio strategies involving cryptocurrencies. This research underscores the significance of grasping volatility transmissions between these currencies, providing valuable insights to guide investors in their decision-making processes. Moreover, it encourages further exploration into the interdependencies of digital currencies. Additionally, this study sheds light on effective contagion risk management, particularly during crises such as Covid-19 and the Russia–Ukraine conflict. It underscores the role of Pax Gold as a safe-haven asset and offers practical guidance for adjusting portfolios across various economic conditions. Ultimately, this research advances our comprehension of Pax Gold’s risk-return profile, positioning it as a potential hedge during periods of uncertainty, thereby contributing to the evolving literature on cryptocurrencies.

This study’s primary value lies in its pioneering empirical examination of the time-varying correlations and scale dependence between Pax Gold and fiat currencies. It goes beyond by determining optimal time-varying hedge ratios through the innovative Range-DCC-GARCH model, originally introduced by Molnár (2016) and distinguished by its incorporation of both low and high prices. Significantly, this analysis unfolds within the unique context of the Covid-19 pandemic and the Russian–Ukrainian conflict, marking a novel contribution to the field.

Bitcoin (BTC) is significantly correlated with global financial assets such as crude oil, gold and the US dollar. BTC and global financial assets have become more closely related, particularly since the outbreak of the COVID-19 pandemic. The purpose of this paper is to formulate BTC investment decisions with the aid of global financial assets.

This study suggests a more accurate prediction model for BTC trading by combining the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model with the artificial neural network (ANN). The DCC-GARCH model offers significant input information, including dynamic correlation and volatility, to the ANN. To analyze the data effectively, the study divides it into two periods: before and during the COVID-19 outbreak. Each period is then further divided into a training set and a prediction set.

The empirical results show that BTC and gold have the highest positive correlation compared with crude oil and the USD, while BTC and the USD have a dynamic and negative correlation. More importantly, the ANN-DCC-GARCH model had a cumulative return of 318% before the outbreak of the COVID-19 pandemic and can decrease loss by 50% during the COVID-19 pandemic. Moreover, the risk-averse can turn a loss into a profit of about 20% in 2022.

The empirical analysis provides technical support and decision-making reference for investors and financial institutions to make investment decisions on BTC.

This paper investigates the predictive impact of Financial Stress on hedging between the oil market and the GCC stock and bond markets from January 1, 2007, to December 31, 2020. The authors also compare the hedging performance of in-sample and out-of-sample analyses.

For the modeling purpose, the authors combine the GARCH-BEKK model with the machine learning approach to predict the transmission of shocks between the financial markets and the oil market. The authors also examine the hedging performance in order to obtain well-diversified portfolios under both Financial Stress cases, using a One-Dimensional Convolutional Neural Network (1D-CNN) model.

According to the results, the in-sample analysis shows that investors can use oil to hedge stock markets under positive Financial Stress. In addition, the authors prove that oil hedging is ineffective in reducing market risks for bond markets. The out-of-sample results demonstrate the ability of hedging effectiveness to minimize portfolio risk during the recent pandemic in both Financial Stress cases. Interestingly, hedgers will have a more efficient hedging performance in the stock and oil market in the case of positive (negative) Financial Stress. The findings seem to be confirmed by the Diebold-Mariano test, suggesting that including the negative (positive) Financial Stress in the hedging strategy displays better out-of-sample performance than the in-sample model.

This study improves the understanding of the whole sample and positive (negative) Financial Stress estimates and forecasts of hedge effectiveness for both the out-of-sample and in-sample estimates. A portfolio strategy based on transmission shock prediction provides diversification benefits.

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 paper aims to investigate the effects of volatility transmission among Bitcoin and other leading cryptocurrencies, namely, Binance USD, BNB, Cardano, Dogecoin, Ethereum, Polkadot, Polygon, Solana, Tether, USD Coin and XRP.

The multivariate BEKK-GARCH model is used with the daily data set from 1 January 2017 to 31 March 2023. The data set is analysed in its entirety and is also the COVID-19 epidemic period.

The study reveals that while the volatility of cryptocurrency prices is influenced by their own historical shocks and volatility, there is proof of the effects shock transmission among Bitcoin and other notable cryptocurrencies. Furthermore, the authors identify the spillover effects of volatility among all 11 pairs and provide evidence that conditional correlations with varying time constants are present, and predominantly positive for both the entire and COVID-19 outbreak periods.

The findings will be helpful to market experts who want to avoid losses in traditional assets. To develop the best risk management and hedging strategies, businesses might use the information to build asset portfolios or personalise payment methods. The use of such data by investors and portfolio managers could aid in the development of investment opportunities, risk insurance plans or hedging strategies for the management of financial portfolios.

To the best of the authors’ knowledge, the use of the BEKK-GARCH model for examining the effects of volatility spillover among Bitcoin and the other eleven top cryptocurrencies has not been previously documented.

Paper aims to investigate the interdependencies and spillover effects that the Visegrad (V4 hereafter) Equity Markets hold on each other. The V4 group stands for the political alliance of four Central European countries: Poland, the Czech Republic, Hungary and Slovakia.

The study uses Wavelet coherence, dynamic conditional correlation GARCH (1, 1) and unrestricted vector autoregression (VAR) methodologies. Daily data series (covering the period from January 2, 2006, to February 2, 2023) are analyzed to assess coherence, time-varying conditional correlation and shock transmission among the V4 Equity Markets.

Wavelet analysis reveals that the Slovak equity market does not maintain coherence with three other equity markets. The time-varying conditional correlation documents for the high interdependence during the COVID-19 outbreak of the four indexes. The VAR estimates reveal that shocks in the Warsaw equity market are easily transmitted in Prague and Budapest exchanges but not in Bratislava. The results show that the Slovak equity market tends to be isolated from the influence of other three V4 exchanges. This isolation is attributed to its size, limited volume and adoption of the euro in 2009. The study emphasizes the Slovak financial system’s gravitation toward the Eurozone after euro adoption.

Notably, the findings provide important signals for local and international investors as the results cover four significant international shocks. The global meltdown of 2008/09, the Greek debt crisis of 2010/11, the COVID-19 pandemic and the Russia-Ukraine war.

This paper aims to investigate the conditional equicorrelation and cross-quantile dependence between the DeFi, European and GCC currency markets (Oman, Qatar, Bahrain, Kuwait, Saudi Arabia and the United Arab Emirates).

This study applies the GARCH-DECO model and cross-quantilogram framework.

The findings reveal evidence of weak and negative average equicorrelations between the examined markets through time, excluding the COVID-19 outbreak and Russia–Ukraine conflict, which is consistent with the literature examining relationships in different markets. From the cross-quantilogram model, the authors note that the dependence between DeFi, EURO and GCC foreign exchange rate markets is greatest in the short run and diminishes over the medium- and long-term horizons, indicating rapid information processing between the markets under consideration, as most innovations are transmitted in the short term.

For the pairs of DeFi and currency markets, the static and dynamic optimal weights and hedging ratios are also estimated, providing new empirical data for portfolio managers and investors.

To the best of the authors’ knowledge, this is one of the most important research looking into the conditional correlation and predictability between the DeFi, EURO and GCC foreign exchange markets. More importantly, this study provides the first empirical proof of the safe-haven, hedging and diversification qualities of DeFi, EURO and GCC currencies, and this work also covers the COVID-19 pandemic and the Russia–Ukraine war with the use of a single dynamic measure produced by the GARCH-DECO model. In addition, the directional predictability between variables under consideration using the cross-quantilogram model is examined, which can be capable of capturing the asymmetry in the quantile dependent structure. The findings are helpful for both policymakers and investors in improving their trading selections and strategies for risk management in different market conditions.

This study aims to empirically investigate the linkages between digital trails of social signals (content and profile features of bitcoin-related tweets) and bitcoin price return using a VAR-BEKK-GARCH model.

Bitcoin-related tweets were collected every hour for six months from September 1, 2020, to February 29, 2021. The analysis involved two steps: first, examining tweet content, profiles, sentiment and emotions; and second, investigating the relationship between social signal volatility and hourly bitcoin price return.

Results indicate that bitcoin price changes can impact the sentiment expressed in tweets about bitcoin, and vice versa. While sadness exhibits a bidirectional volatility spillover with bitcoin, fear and anger display a one-period lag. Quartile analyses reveal that only fear in the second quartile shows a bidirectional spillover effect with bitcoin, while all other emotions except sadness demonstrate a unidirectional spillover effect in all remaining quartiles.

The study uses a novel two-step approach to analyze volatility spillovers between social signals and bitcoin price returns. Findings can guide investors and portfolio managers in making better allocation decisions and assist policymakers and regulators in reducing the adverse effects of bitcoin’s volatility on financial system stability.

Investors aim for returns when investing in stocks, making return volatility a crucial concern. This study compares symmetric and asymmetric GARCH models to forecast volatility in emerging nations like the G4 countries. Accurate volatility forecasting is vital for investors to make well-informed investment decisions, forming the core purpose of this study.

From January 1993 to May 2021, the study spans four periods, focusing on the global economic crisis of 2008, the Russian crisis of 2015 and the COVID-19 pandemic. Standard generalized autoregressive conditional heteroscedasticity (GARCH), exponential GARCH (E-GARCH) and Glosten-Jagannathan-Runkle GARCH models were employed to analyse the data. Robustness was assessed using the Akaike information criterion, Schwarz information criterion and maximum log-likelihood criteria.

The study's findings show that the E-GARCH model is the best model for forecasting volatility in emerging nations. This is because the E-GARCH model is able to capture the asymmetric effects of positive and negative shocks on volatility.

This unique study compares symmetric and asymmetric GARCH models for forecasting volatility in emerging nations, a novel approach not explored in prior research. The insights gained can aid investors in constructing more effective risk-adjusted international portfolios, offering a better understanding of stock market volatility to inform strategic investment decisions.

The aim of this study is to examine the nature and determinants of stock market integration between India and other Asia–Pacific countries (Malaysia, Hong Kong, Singapore, South Korea, Japan, China, Indonesia, the Philippines, Thailand and Taiwan) over the period 1991–2021.

Unit root tests, the dynamic conditional correlation-Glosten Jagannathan and Runkle-generalized autoregressive conditional heteroscedasticity (DCC-GJR-GARCH), pooled ordinary least squares (OLS) regression and random effects models are employed for the analysis.

The empirical results show that the DCC between each pair of sample countries is less than 0.5, indicating weak ties between the pairs of sample countries. Also, the DCC between India and other Asia–Pacific stock markets is positive and low, implying low level of integration. The correlation between India and China stock markets is found to be the highest, implying significant level of integration. The main reason for it would be strong economic linkages and bilateral trade relationship between India and China. Moreover, gross domestic product (GDP), interest rate (IR), consumer price index (CPI)-inflation and money supply (MS) differentials are the major driver of stock market integration between India and other Asia–Pacific countries.

The findings of the study have important implications for investors, portfolio managers and policymakers. It is found that the DCC between India and other Asia–Pacific countries (considered in the study) except China is low, which indicates weak ties between the pairs of sample countries. This implies that the Indian stock market provides good investment opportunities for foreign investors. Also, investors and portfolio managers can attain more diversified benefits and can minimize country risk by investing across Asia–Pacific countries. Further, knowledge about the factors that integrate the Indian stock market with the other Asia–Pacific stock markets will help policymakers frame suitable economic and financial stabilization policies.

This study contributes to the extant literature: first, by examining the linkages of Indian stock market with other Asia–Pacific countries; second, although previous studies confirmed the existence of linkages among the various stock markets, few researchers pay attention to the factors driving the process of stock market integration. This study provides additional evidence by examining the significant macroeconomic factors driving the process of such integration in the Asia–Pacific region considered under the study.