The purpose of this study is to investigate the relationship between investor sentiment and leading equity market indices from the U.S., Europe, Asia, and globally between January 2020 and June 2022. The methodological approaches utilized are quantile regression and wavelet analysis. The results...

This paper develops a method to improve the estimation of jump variation using high frequency data with the existence of market microstructure noises. Accurate estimation of jump variation is in high demand, as it is an important component of volatility in finance for portfolio allocation,...

We extend the event study methodology into a richer and more dynamic environment by including time-varying parameters. Under the Bayesian framework, useful to update relevant information in a sequential learning mechanism, we use the Kalman filter to consider time dependent parameters, and we...

Understanding the relationship between macroeconomic variables and the stock market is important because macroeconomic variables have a systematic effect on stock market returns. This study uses monthly data from India for the period from April 1994 to July 2018 to examine the long-run...