This study examines the risk spillovers between energy futures prices and Europe-based carbon futures contracts. We use a Markov regime-switching dynamic correlation, generalized autoregressive conditional heteroscedasticity (MSDCC- GARCH) model in order to capture the time variations and...

The main goal of this paper is to formally establish the volatility-herding link in the developing stock markets of the oil-rich GCC countries by examining how market volatility affects herd behavior after controlling for global factors. Using a regime-switching, smooth transition regression...

This paper evaluates the impact of industry herding on return momentum. While the findings support that winner industries outperform loser industries in subsequent months, we find that the profitability of industry momentum strategies depends on the level of herding in an industry. Loser...

This paper examines the predictive power of time-varying risk aversion over payoffs to the carry trade strategy via the cross-quantilogram methodology. Our analysis yields significant evidence of directional predictability from risk aversion to daily carry trade returns tracked by the Deutsche...

The aim of this study is to understand the effect of the recent novel coronavirus pandemic on investor herding behavior in global stock markets. Utilizing a daily newspaper-based index of financial uncertainty associated with infectious diseases, we examine the association between...

This paper examines the propagation of oil price uncertainty shocks to real equity prices using a large-scale Global Vector Autoregressive (GVAR) model of 26 advanced and emerging stock markets. The GVAR framework allows us to capture the transmission of local and global shocks, while...

This paper scrutinizes the effect of crude oil prices on herd behavior among investors in the Gulf Cooperation Council (GCC) stock markets. Using firm level data from Saudi Arabia, Qatar, Oman, Kuwait, Bahrain, Dubai and Abu Dhabi stock exchanges, we examine equity return dispersions within...