Riemann Manifold Langevin Methods on Stochastic Volatility Estimation
Computation
2015-07-20 v1
Abstract
In this paper we perform Bayesian estimation of stochastic volatility models with heavy tail distributions using Metropolis adjusted Langevin (MALA) and Riemman manifold Langevin (MMALA) methods. We provide analytical expressions for the application of these methods, assess the performance of these methodologies in simulated data and illustrate their use on two financial time series data sets.
Keywords
Cite
@article{arxiv.1507.05079,
title = {Riemann Manifold Langevin Methods on Stochastic Volatility Estimation},
author = {Mauricio Zevallos and Loretta Gasco and Ricardo Ehlers},
journal= {arXiv preprint arXiv:1507.05079},
year = {2015}
}