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,...

Particle Filter algorithms for filtering latent states (volatility and jumps) of Stochastic-Volatility Jump-Diffusion (SVJD) models are being explained. Three versions of the SIR particle filter with adapted proposal distributions to the jump occurrences, jump sizes, and both are derived and...

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...

This paper develops a method to select the threshold in threshold-based jump detection methods. The method is motivated by an analysis of threshold-based jump detection methods in the context of jump-diffusion models. We show that over the range of sampling frequencies a researcher is most...