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Estimating a semiparametric asymmetric stochastic volatility model with a dirichlet process mixture

Jensen, Mark J.; Maheu, John M. - 2012

factor ; Dirichlet process mixture ; infinite mixture model ; leverage effect ; marginal likelihood ; MCMC ; non …

Persistent link: https://www.econbiz.de/10009534187

Estimating a Semiparametric Asymmetric Stochastic Volatility Model with a Dirichlet Process Mixture

Jensen, Mark J. - 2014

In this paper, we extend the parametric, asymmetric, stochastic volatility model (ASV), where returns are correlated with volatility, by flexibly modeling the bivariate distribution of the return and volatility innovations nonparametrically. Its novelty is in modeling the joint, conditional,...

Persistent link: https://www.econbiz.de/10013066096

Alternative Formulations of the Leverage Effect in a Stochastic Volatility Model with Asymmetric Heavy-Tailed Errors

Deschamps, Philippe J. - 2016

compared with each other and with a GARCH formulation, using Bayes factors. MCMC estimation relies on a parametric proposal …, suggest that the last two leverage formulations strongly dominate the conventional one. The performance of the MCMC method is …

Persistent link: https://www.econbiz.de/10012998056

Bayesian SAR model with stochastic volatility and multiple time-varying weights

Costola, Michele; Iacopini, Matteo; Wichers, Casper - 2023

A novel spatial autoregressive model for panel data is introduced, which incorporates multilayer networks and accounts for time-varying relationships. Moreover, the proposed approach allows the structural variance to evolve smoothly over time and enables the analysis of shock propagation in...

Persistent link: https://www.econbiz.de/10014416011

Bayesian Estimation of Generalized Hyperbolic Skewed Student GARCH Models

Deschamps, Philippe J. - 2012

Efficient posterior simulators for two GARCH models with generalized hyperbolic disturbances are presented. The first model, GHt-GARCH, is a threshold GARCH with a skewed and heavy-tailed error distribution; in this model, the latent variables that account for skewness and heavy tails are...

Persistent link: https://www.econbiz.de/10013105412

The Stochastic Volatility in Mean Model with Time-Varying Parameters : An Application to Inflation Modeling

Chan, Joshua C. C. - 2015

This paper generalizes the popular stochastic volatility in mean model of Koopman and Hol Uspensky (2002) to allow for time-varying parameters in the conditional mean. The estimation of this extension is nontrival since the volatility appears in both the conditional mean and the conditional...

Persistent link: https://www.econbiz.de/10013026159

Modeling Stock Returns Using Asymmetric Garch-Icapm with Mixture and Heavy-Tailed Distributions : An Application to COVID-19 Pandemic Forecasts

Khanthaporn, Rewat; Wichitaksorn, Nuttanan - 2021

COVID-19 pandemic is an extreme event that created a turmoil in stock markets around the world. This unexpected circumstance poses a critical question whether the prevailing models can help predict the plummets of indices, hence the returns. In this study, we model the stock returns using...

Persistent link: https://www.econbiz.de/10013236407

A Gaussian Mixture Hidden Markov Model for the VIX

Aigner, Andreas A. - 2023

A Hidden Markov Model (HMM) is used to model the VIX (the Cboe Volatility Index). A 4- state Gaussian mixture is fitted to the VIX price history from 1990 to 2022. Using a growing window of training data, the price of the S&P500 is predicted and two trading algorithms are presented, based on the...

Persistent link: https://www.econbiz.de/10014356167

Identifying Price Jumps from Daily Data with Bayesian vs. Non-Parametric Methods

Fičura, Milan - 2017

parametric approach utilizing a Stochastic-Volatility-Jump-Diffusion (SVJD) model, estimated with MCMC and extended with Particle …-sample estimation does the MCMC based parametric approach significantly outperform the L-Estimator. In the case of the out …-sample estimates, based on a combination of MCMC an Particle Filters, used to sequentially estimate the jump occurrences immediately at …

Persistent link: https://www.econbiz.de/10012964932

An Infinite Hidden Markov Model with Stochastic Volatility

Chenxing; Maheu, John M.; Yang, Qiao - 2022

This paper extends the Bayesian semiparametric stochastic volatility (SV-DPM) model of Jensen and Maheu (2010). Instead of using a Dirichlet process mixture (DPM) to model return innovations, we use an infinite hidden Markov model (IHMM). This allows for time variation in the return density...

Persistent link: https://www.econbiz.de/10013295177

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