We examine the quantile-time-frequency connectedness of stock returns among BRICS and G7 markets over the period January 2000 to January 2024, employing the Quantile Vector Autoregression (QVAR) model. Our findings reveal that spillover effects intensify during periods of extreme market conditions, compared to more tranquil phases. Furthermore, the stock markets of France, Germany, the United States, the United Kingdom, Italy, and Canada emerge as primary sources of contagion, whereas the BRICS markets and Japan primarily act as recipients across all quantile regimes. The frequency-quantile decomposition reveals that short-term dynamics primarily drive the net transmission of shocks at both the median and upper quantiles, whereas long-term dynamics are dominant at the lower quantile, indicating more persistent effects during market downturns. Finally, we construct investment portfolios based on the Minimum Connectedness Portfolio (MCP) approach and evaluate them through average portfolio weights and Hedging Effectiveness (HE) ratios. The results demonstrate that G7-based portfolios tend to have lower average weights and higher hedging efficiency, implying greater diversification benefits and enhanced risk mitigation performance compared to BRICS-based portfolios.

We develop an international capital asset pricing model in the presence of shadow costs of incomplete information and short sales. Our model shows the direct effect of exchange rate risk, information costs and short sales costs on asset prices. At equilibrium, this model gives an explicit expression of two systematic risk premium. The first one is linked to the exchange rate. The second one is related to international market risk. Our model explains in part the well-known home bias equity by market segmentation. This model, which is proposed for the first time in the literature, can be seen as an international version of Wu et al. (Rev Quant Finance Account 7:119–136, 1996) and Merton (J Finance 42:483–511, 1987) models of capital market equilibrium in international markets. Our analysis shows that the dispersion in beliefs increases the market inefficiency, and that short sale constraints can reduce the cost of ignorance and the magnitude of the home bias equity. Our model provides an operational approach in asset pricing to take account of previous important costs.

In this study, we aim to provide a comprehensive analysis of the risk management potential of sectoral Credit Default Swaps (CDSs) within financial portfolios. Our objectives are threefold: (i) to investigate the safe haven properties of sectoral CDSs; (ii) to assess their hedging effectiveness and evaluate the diversification benefits of incorporating sectoral CDSs into both conventional and Islamic stock market portfolios; and (iii) to compare these findings with those obtained from alternative assets such as the VSTOXX, gold, and Bitcoin indices. To achieve this, we estimate time-varying hedge ratios using a range of multivariate GARCH (MGARCH) models and subsequently compute hedging effectiveness metrics. Conditional correlations derived from the Asymmetric Dynamic Conditional Correlation (ADCC) model are employed in linear regression analyses to assess safe haven characteristics. This methodology is applied across different subperiods to capture the impact of the crypto currency bubble and the COVID-19 pandemic on hedging performance.