Testing Data Cloning as the Basis of an Estimator for the Stochastic Volatility in Mean Model

Developed as a refinement of stochastic volatility (SV) models, the stochastic volatility in mean (SVM) model incorporates the latent volatility as an explanatory variable in both the mean and variance equations. It, therefore, provides a way of assessing the relationship between returns and volatil...

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Bibliographic Details
Published inDiscrete dynamics in nature and society Vol. 2023; pp. 1 - 12
Main Authors Romero, E., Ropero-Moriones, E.
Format Journal Article
LanguageEnglish
Published New York Hindawi 12.09.2023
John Wiley & Sons, Inc
Hindawi Limited
Subjects
Algorithms
Analysis
Bayesian analysis
Cloning
Digital currencies
Estimates
Kurtosis
Markov chains
Markov processes
Maximum likelihood estimates
Methods
Monte Carlo method
Stochastic models
Time series
Volatility
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Summary:Developed as a refinement of stochastic volatility (SV) models, the stochastic volatility in mean (SVM) model incorporates the latent volatility as an explanatory variable in both the mean and variance equations. It, therefore, provides a way of assessing the relationship between returns and volatility, albeit at the expense of complicating the estimation process. This study introduces a Bayesian methodology that leverages data-cloning algorithms to obtain maximum likelihood estimates for SV and SVM model parameters. Adopting this Bayesian framework allows approximate maximum likelihood estimates to be attained without the need to maximize pseudo likelihood functions. The key contribution this paper makes is that it proposes an estimator for the SVM model, one that uses Bayesian algorithms to approximate the maximum likelihood estimate with great effect. Notably, the estimates it provides yield superior outcomes than those derived from the Markov chain Monte Carlo (MCMC) method in terms of standard errors, all while being unaffected by the selection of prior distributions.
ISSN:1026-0226
1607-887X
DOI:10.1155/2023/7657430