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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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2023/7657430 |
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| author | E. Romero E. Ropero-Moriones |
| author_facet | E. Romero E. Ropero-Moriones |
| author_sort | E. Romero |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0dedfa239865477891e7aedab39a090b |
| institution | Kabale University |
| issn | 1607-887X |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-0dedfa239865477891e7aedab39a090b2025-02-03T01:29:34ZengWileyDiscrete Dynamics in Nature and Society1607-887X2023-01-01202310.1155/2023/7657430Testing Data Cloning as the Basis of an Estimator for the Stochastic Volatility in Mean ModelE. Romero0E. Ropero-Moriones1Department of Statistics and ORSchool of Applied Social Sciences and CommunicationDeveloped 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.http://dx.doi.org/10.1155/2023/7657430 |
| spellingShingle | E. Romero E. Ropero-Moriones Testing Data Cloning as the Basis of an Estimator for the Stochastic Volatility in Mean Model Discrete Dynamics in Nature and Society |
| title | Testing Data Cloning as the Basis of an Estimator for the Stochastic Volatility in Mean Model |
| title_full | Testing Data Cloning as the Basis of an Estimator for the Stochastic Volatility in Mean Model |
| title_fullStr | Testing Data Cloning as the Basis of an Estimator for the Stochastic Volatility in Mean Model |
| title_full_unstemmed | Testing Data Cloning as the Basis of an Estimator for the Stochastic Volatility in Mean Model |
| title_short | Testing Data Cloning as the Basis of an Estimator for the Stochastic Volatility in Mean Model |
| title_sort | testing data cloning as the basis of an estimator for the stochastic volatility in mean model |
| url | http://dx.doi.org/10.1155/2023/7657430 |
| work_keys_str_mv | AT eromero testingdatacloningasthebasisofanestimatorforthestochasticvolatilityinmeanmodel AT eroperomoriones testingdatacloningasthebasisofanestimatorforthestochasticvolatilityinmeanmodel |