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Improving MCMC Using Efficient Importance Sampling

Liesenfeld, Roman; Richard, Jean-Francois - 2006

This paper develops a systematic Markov Chain Monte Carlo (MCMC) framework based upon Efficient Importance Sampling … solution. EIS is a simple, generic and yet accurate Monte-Carlo integration procedure based on sampling densities which are … MCMC components such as auxiliary sampling densities, normalizing constants and starting values. The potential of this …

Persistent link: https://www.econbiz.de/10014058202

The Gibbs Sampler with Particle Efficient Importance Sampling for State-Space Models

Grothe, Oliver - 2017

Monte Carlo (MC) approximation of the standard Gibbs procedure which uses sequential MC (SMC) importance sampling inside the … generic and easily implementable SMC approach known as Particle Efficient Importance Sampling (PEIS). By using SMC importance … sampling densities which are approximately fully globally adapted to the targeted density of the states, PEIS can substantially …

Persistent link: https://www.econbiz.de/10012970355

Markov Chain Monte Carlo Models, Gibbs Sampling, & Metropolis Algorithm for High-Dimensionality Complex Stochastic Problems

Malhotra, Yogesh - 2015

Algorithms, Gibbs Sampling and Metropolis-Hastings Algorithm. Network and security risk management application focus is on how …

Persistent link: https://www.econbiz.de/10013029835

A Markov Chain Quasi-Monte Carlo Method for Bayesian Estimation of Stochastic Volatility Model

Fung, Eric - 2012

In this paper, we propose a Markov Chain Quasi-Monte Carlo (MCQMC) approach for Bayesian estimation of a discrete-time version of the stochastic volatility (SV) model. The Bayesian approach represents a feasible way to estimate SV models. Under the conventional Bayesian estimation method for SV...

Persistent link: https://www.econbiz.de/10013116422

Stochastic billiards for sampling from the boundary of a convex set

Dieker, A. B.; Vempala, Santosh S. - In: Mathematics of operations research 40 (2015) 4, pp. 888-901

Persistent link: https://www.econbiz.de/10011408940

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

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

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/10009534187

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

Jensen, Mark J. - 2014

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

Modelling Stochastic Volatility with Leverage and Jumps : A Simulated Maximum Likelihood Approach via Particle Filtering

Malik, Sheheryar; Pitt, Michael K. - 2011

In this paper we provide a unified methodology for conducting likelihood-based inference on the unknown parameters of a general class of discrete-time stochastic volatility (SV) models, characterized by both a leverage effect and jumps in returns. Given the nonlinear/non-Gaussian state-space...

Persistent link: https://www.econbiz.de/10014185810

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

Barra, Istvan - 2016

these proposal densities are used in an independent Metropolis-Hastings algorithm or in importance sampling. Our method …

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

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

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