of stock market volatility. Among the various macro variables in our dataset the term spread, housing starts, corporate … profits, and the unemployment rate have the highest predictive ability for long-term stock market volatility. While the term … spread and housing starts are leading variables with respect to stock market volatility, for industrial production and the …

In this paper, we investigate the dynamic response of stock market volatility to changes in monetary policy. Using a … vector autoregressive model, our findings reveal a significant and asymmetric response of stock returns and volatility to … monetary policy shocks. Although the increase in the volatility risk premium, futures-trading volume, and leverage appear to …

"low" and "high" volatility periods. These periods are determined by estimating asset dynamics using a SWARCH process. Our … results suggest that securities volatility is higher during periods of financial or economic instability. We use these results … to evaluate the impact of news during "low" and "high" volatility periods using a GARCH model. News effects, especially …

This study investigated how stock market volatility responded dynamically to unexpected changes during the COVID-19 … pandemic and the resulting uncertainty in Thailand. Using a multivariate GARCH-BEKK model, the conditional volatility dynamics …, the interlinkages, and the conditional correlations between stock market volatility and the increasing rate of COVID-19 …

interventions succeeded in reducing yields and volatility of government bond segments of the countries under the programme. Finally …

, both in terms of returns and volatility, is still a barren landscape. Using econometric methodology, the paper investigates …

terms of returns and volatility, received much less attention. With the use of an econometric methodology, the paper aims to …