Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/345335
Type: Artigo
Title: Riemann manifold Langevin methods on stochastic volatility estimation
Author: Zevallos, Mauricio
Gasco, Loretta
Ehlersc, Ricardo
Abstract: In this article, 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 datasets.
Subject: Teoria bayesiana de decisão estatística
Monte Carlo
Método de
Markov
Processos de
Avaliação de riscos
Sistemas estocásticos
Finanças - Métodos estatísticos
Bayesian statistical decision theory
Monte Carlo method
Markov processes
Risk assessment
Stochastic systems
Finance - Statistical methods
Country: Estados Unidos
Editor: Taylor & Francis
Rights: fechado
Identifier DOI: 10.1080/03610918.2016.1255972
Address: https://www.tandfonline.com/doi/abs/10.1080/03610918.2016.1255972
Date Issue: 2017
Appears in Collections:IMECC - Artigos e Outros Documentos

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