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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

Estimating Stochastic Volatility and Jumps Using High-Frequency Data and Bayesian Methods

Fičura, Milan - 2015

-parametric power-variation approach using high-frequency returns, and the parametric Bayesian approach (MCMC estimation of SVJD models …

Persistent link: https://www.econbiz.de/10013030080

Bayesian Estimation of an Extended Local Scale Stochastic Volatility Model

Deschamps, Philippe J. - 2011

A new version of the local scale model of Shephard (1994) is presented. Its features are identically distributed evolution equation disturbances, the incorporation of in-the-mean effects, and the incorporation of variance regressors. A Bayesian posterior simulator and a new simulation smoother...

Persistent link: https://www.econbiz.de/10013120871

Deep Bayesian Estimation of the Rough Path-Dependent Volatility Model for Forecasting Purposes

Parent, Léo - 2023

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 presents challenges when it comes to estimating the...

Persistent link: https://www.econbiz.de/10014354222

Joint independent metropolis-hastings methods for nonlinear non-Gaussian state space models

Barra, Istvan; Hoogerheide, Lennart; Koopman, Siem Jan; … - 2013

Persistent link: https://www.econbiz.de/10010191411

Nonparametric Bayesian Volatility Estimation

Gugushvili, Shota - 2020

Given discrete time observations over a fixed time interval, we study a nonparametric Bayesian approach to estimation of the volatility coefficient of a stochastic differential equation. We postulate a histogram-type prior on the volatility with piecewise constant realisations on bins forming a...

Persistent link: https://www.econbiz.de/10012852986

Joint Bayesian Analysis of Parameters and States in Nonlinear, Non-Gaussian State Space Models

Barra, Istvan - 2016

We propose a new methodology for designing flexible proposal densities for the joint posterior density of parameters and states in a nonlinear, non-Gaussian state space model. We show that a highly efficient Bayesian procedure emerges when these proposal densities are used in an independent...

Persistent link: https://www.econbiz.de/10013005987

Bayesian Estimation of the Multi-Factor Heston Stochastic Volatility Model

Fruhwirth-Schnatter, Sylvia; Sögner, Leopold - 2014

The goal of this article is an exact Bayesian analysis of the Heston (1993) stochastic volatility model. We carefully study the effect different parameterizations of the latent volatility process and the parameters of the volatility process have on the convergence and the mixing behavior of the...

Persistent link: https://www.econbiz.de/10014221761

Bayesian Student-T Stochastic Volatility Models Via Scale Mixtures

Choy, Boris - 2009

The normal error distribution for the observations and log-volatilities in a stochastic volatility (SV) model is replaced by the Student-t distribution for robustness consideration. The model is then called the t-t SV model throughout this paper. The objectives of the paper are two-fold....

Persistent link: https://www.econbiz.de/10013156986

Issues in Comparing Stochastic Volatility Models Using the Deviance Information Criterion

Chan, Joshua - 2014

The deviance information criterion (DIC) has been widely used for Bayesian model comparison. In particular, a popular metric for comparing stochastic volatility models is the DIC based on the conditional likelihood — obtained by conditioning on the latent variables. However, some recent...

Persistent link: https://www.econbiz.de/10013051070