Fitting general stochastic volatility models using Laplace accelerated sequential importance sampling

Authors:
Tore Selland Kleppe;Hans Julius Skaug
Affiliations:
-;-
Venue:
Computational Statistics & Data Analysis
Year:
2012

Citing 5
Cited 0

On the limited memory BFGS method for large scale optimization

Mathematical Programming: Series A and B
Choice of Basis for Laplace Approximation

Machine Learning
Evaluating derivatives: principles and techniques of algorithmic differentiation

Evaluating derivatives: principles and techniques of algorithmic differentiation
Automatic approximation of the marginal likelihood in non-Gaussian hierarchical models

Computational Statistics & Data Analysis
Indirect inference methods for stochastic volatility models based on non-Gaussian Ornstein-Uhlenbeck processes

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.03

Visualization

Abstract

A methodology for fitting general stochastic volatility (SV) models that are naturally cast in terms of a positive volatility process is developed. Two well known methods for evaluating the likelihood function, sequential importance sampling and Laplace importance sampling, are combined. The statistical properties of the resulting estimator are investigated by simulation for an ensemble of SV models. It is found that the performance is good compared to the efficient importance sampling (EIS) algorithm. Finally, the computational framework, building on automatic differentiation (AD), is outlined. The use of AD makes it easy to implement other SV models with non-Gaussian latent volatility processes.