heteroskedasticity, which are commonly observed in financial markets. The focus is on parameter estimation, inference and volatility …An effective approach for forecasting return volatility via threshold nonlinear heteroskedastic models of the daily … forecasting in a Bayesian framework. An MCMC sampling scheme is employed for estimation and shown to work well in simulation …

This paper investigates the performance of various conditional volatility models to forecast the second moment of … that accounting for volatility regimes and asymmetry does not enhance the performance of one-day-ahead forecasts of either …

more than a single regime, have performed substantially better than standard methods in terms of volatility and Value …

with a second DNN. After formalizing the estimation problem within the framework of Bayesian decision theory, the article …The rough path-dependent volatility (RPDV) model (Parent 2022) effectively captures key empirical features that are … characteristic of volatility dynamics, making it a suitable choice for volatility forecasting. However, its complex structure …

weather conditions. To account for the uncertainty in predicting wind power production, this article examines the volatility …-switching GARCH (MRS-GARCH) model on forecasting volatility of wind power. The realized volatility, which is derived from lower …-scale data, serves as a benchmark for the latent volatility. We find that the MRS-GARCH model significantly outperforms …

. Applying our model to high-frequency transaction data, we detect two distinct regimes in the intraday volatility process: a … dominant volatility regime that is observable throughout the trading day representing the risk-transferring trading activity of … investors, and a minor volatility regime that concentrates around market liquidity shocks which mainly capture impacts of firm …

A new model - the factorial hidden Markov volatility (FHMV) model - is proposed for financial returns and their latent … variances. It is also applicable to model directly realized variances. Volatility is modeled as a product of three components: a … Markov chain driving volatility persistence, an independent discrete process capable of generating jumps in the volatility …

The transmission mechanisms of volatility between markets can be characterized within a new Markov Switching bivariate …

The asymmetric stochastic volatility (ASV) models extend the stochastic volatility model (SV) by modeling the … correlation between the asset return and its volatility. We prove by simulation studies that fitting the ASV models may infer … erroneous estimations of the correlation coefficients. Even if the true return-volatility correlation structure is different …