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This study attempts to examine the time-varying volatility spillovers between environmentally sustainable assets and quantify the value-at-risk of the portfolios across various frequencies.

To accomplish these objectives, this paper utilizes a connectedness index-based TVP-VAR model and applies the wavelet-based VaR ratio to daily data spanning from January 2018 to September 2023.

The empirical findings reveal a notable increase in the connectedness index between green stocks and green bonds during the COVID-19 crisis, signifying evidence of a contagion effect. The portfolio’s risk ratio also exhibited a sharp rise amid the pandemic, particularly over medium and long-term horizons, driven by increased spillover among green assets. Notably, our analysis indicates that green bonds influence the connectedness system between green stocks and the value-at-risk ratio, reducing volatility spillover and portfolio risk ratios across various investment horizons. These results highlight the role of green bonds as an effective diversification asset against the risks associated with green equities.

This research investigates the dynamic connectedness and value-at-risk ratio between eight green sectoral renewable energy and non-energy equities and green bonds. We put forward some portfolio implications for green investors with an environmental consciousness who desire to decarbonize their portfolios and mitigate environmental issues.

The aim of this paper is threefold: (1) to develop a new measure of investor sentiment rational (ISR) of developing countries by applying principal component analysis (PCA), (2) to investigate co-movements between the ten developing stock markets, the sentiment investor's, exchange rates and geopolitical risk (GPR) during Russian invasion of Ukraine in 2022, (3) to explore the key factors that might affect exchange market and capital market before and mainly during Russia–Ukraine war period.

The wavelet approach and the multivariate wavelet coherence (MWC) are applied to detect the co-movements on daily data from August 2019 to December 2022. Value-at-risk (VaR) and conditional value-at-risk (CVaR) are used to assess the systemic risks of exchange rate market and stock market return in the developing market.

Results of this study reveal (1) strong interdependence between GPR, investor sentiment rational (ISR), stock market index and exchange rate in short- and long-terms in most countries, as inferred from (WTC) analysis. (2) There is evidence of strong short-term co-movements between ISR and exchange rates, with ISR leading. (3) Multivariate coherency shows strong contributions of ISR and GPR index to stock market index and exchange rate returns. The findings signal the attractiveness of the Vietnamese dong, Malaysian ringgits and Tunisian dinar as a hedge for currency portfolios against GPR. The authors detect a positive connectedness in the short term between all pairs of the variables analyzed in most countries. (4) Both foreign exchange and equity markets are exposed to higher levels of systemic risk in the period of the Russian invasion of Ukraine.

This study provides information that supports investors, regulators and executive managers in developing countries. The impact of sentiment investor with GPR intensified the co-movements of stocks market and exchange market during 2021–2022, which overlaps with period of the Russian invasion of Ukraine.

This paper aims to analyze the downside risk for the stock indices of BRICS countries. The study also aimed to study the interrelationship, directional influence and interdependence among the stock exchanges of BRICS economies to provide insights for policymakers, fund managers, investors and other stakeholders.

The authors used Value at Risk (VaR) as an indicator of downside risk and time series econometrics for measuring the long run relationship, directional influence and interdependence.

The calculated VaR estimates, long-run linkages and strong interdependence among these indices especially with the returns of Brazil exerting a notable impact on the returns of other BRICS nations. These results emphasize the significance of taking into account cross-country spillover effects and domestic market dynamics in the context of portfolio management and risk assessment strategies. Further, from the extended results of variance decomposition analysis, the authors find that Brazil’s, China’s and South African stock market returns have a significantly lagged impact on their own stock market, while Russia’s and India stock market returns do not have a significantly lagged impact on their own stock markets.

To the best of the authors’ knowledge, this is the first study comprehensively analyzing the BRICS indices downside risk through the historical simulation method of VaR estimation, which is an unexplored area of risk management.

This chapter tries to hedge extreme financial risk of entrepreneurs who work with wheat by combining wheat with four stock indices of developed and emerging European markets in a portfolio. Extreme risk of the portfolios is measured by the parametric and historical value-at-risk (VaR) metrics. Portfolios that target maximum return-to-VaR ratio are also constructed because different market participants prefer different goals. Preliminary equicorrelation results indicate that integration between wheat and emerging markets is lower (0.218) vis-á-vis the combination of wheat and developed markets (0.307), which gives preliminary advantage to emerging markets in diversification efforts. The results show that portfolios with emerging stock indices have significantly lower parametric (–0.816) and historical (–0.831) VaR than portfolios with developed indices, –1.080 and –1.295, respectively. As for optimal portfolios, the portfolios with developed indices have a slight upper hand. This chapter shows that parametric VaR is not a good measure of extreme risk, because it neglects the third and fourth moments.

This paper examines the impact of macroeconomic volatility on stock volatility, both under normal conditions and during the COVID-19 pandemic in Vietnam.

We extend the existing Exponential Generalized Autoregressive Conditional Heteroskedasticity model by adding a new component: the thresholds – the levels of macroeconomic volatility at which the market may respond differently. These thresholds are estimated for both positive and negative volatility.

The impact of macroeconomic volatility on stock volatility is asymmetric: there are thresholds of macroeconomic volatility at which its pattern changes. These thresholds are higher in the case of positive volatility compared with negative volatility. The thresholds were also higher during the COVID-19 pandemic. Macroeconomic variables influence stock volatility differently depending on market conditions. While GDP is more significant in normal periods, interest rates affect it in both normal and unstable phases.

Our models consider only two variables representing macroeconomic variables: interest rate and GDP. Furthermore, only one lag period of the variables is included in the analysis. In the future, more macrovariables and longer lags could be included when computational techniques advance.

Policymakers should consider the impact of macroeconomic volatility on the stock market when designing policies, especially at thresholds. Similarly, investors should pay more attention to macroeconomic volatility when constructing and managing their portfolios, particularly when such volatility is close to thresholds.

The inclusion of thresholds as parameters to be estimated into the model provides more insights into the impact of macroeconomic variables on stock volatility.

In this research, arbitrage opportunity is tested between the yield rates computed by the NSS model, and the computed forward rates between conventional and Islamic finance to see any arbitrage opportunity. The research questions are the conventional and Islamic finance yields at the same level and equal to each other to avoid arbitrage? Whether conventional and Islamic forward rates differ significantly and thus create any arbitrage opportunity. This study aims to find the presence or absence of arbitrage between conventional and Islamic finance yield rates.

The NSS model is the latest model in calculating yield and forward rates. In the method the error level is minimized so expected yield rate and given yield rate both converged (Vahidin and Anastasios, 2020). When they converged it gives the researchers all six months’ yield rates. For the Nelson Siegal method, all the six months’ yield rates are available and these yield rates can be used to compute the forward rates.

The authors concluded there is a significant difference between the conventional yield rate and the Islamic yield rate. It suggests that because there are significant differences, its suggest arbitrage is possible. So anyone interested in making a guaranteed profit. The conventional yield rates are lower; hence, anyone can borrow from the conventional finance system and invest the money in the Islamic financial system because investments are getting higher rates of income in the form of yield rate in Islamic Finance. So, one can make money because of this difference. Statistically, it is possible to make money, but practically, the authors observed the difference, however it is very meager. The arbitrage opportunity between Islamic finance and conventional finance will not affect the economy because the significant difference is too small. The disturbance in the arbitrage opportunity due to the values is very meager and insignificant.

This research does not address the derivative contracts’ role in risk management; future researchers could take up this as another research.

This research will be beneficial for financial institutions, especially institutional investors. Besides, this research will help the regulators and investment bankers in assisting where and future losses especially bond portfolios in conventional finance and Islamic finance. This study will also contribute and help the asset manager of mutual funds in the mutual fund industries.

In effect, this research will strengthen the financial system, capital market and bond market, derivative contracts such as options contracts, futures contracts, swap contracts and forward contracts will use computed forward rates for assessing future losses (value at risk [VaR]) and to hedge them (Balakrishnan, 2020).

As this topic is rarely studied it will increase the literature present in this domain.