provided between frequentist and Bayesian estimation. No significant difference is found between the qualities of the forecasts …

We present a self-consistent model for explosive financial bubbles, which combines a mean-reverting volatility process … started at least 4 years earlier. We confirm the validity and universality of the volatility-confined LPPL model on seven …

dynamic factor and a vector autoregressive model and includes stochastic volatility, denoted by FAVAR-SV. Next, a Bayesian … momentum strategy. The estimation of this modeling and strategy approach can be done using an extended and modified version of … risk features like volatility and largest loss, which indicates that complete densities provide useful information for risk. …

with time-varying loading coefficients and stochastic volatility, which allows for capturing changes in the pricing …

We are comparing two approaches for stochastic volatility and jumps estimation in the EUR/USD time series - the non …-parametric power-variation approach using high-frequency returns, and the parametric Bayesian approach (MCMC estimation of SVJD models …) using daily returns. We find that both of the methods do identify continuous stochastic volatility similarly, but they do …

This study explores the benefits of incorporating fat-tailed innovations, asymmetric volatility response, and an … extended information set into crude oil return modeling and forecasting. To this end, we utilize standard volatility models … Stochastic Volatility (SV), along with Mixed Data Sampling (MIDAS) regressions, which enable us to incorporate the impacts of …