A Simulation Study comparing MCMC, QML and GMM Estimation of the Stochastic Volatility Model
(2016) NEKN01 20141Department of Economics
- Abstract
- The stochastic volatility (SV) model is an alternative to GARCH models to model time varying volatility. In this thesis the basic stochastic volatility model and three different estimation methods are described---namely, Bayesian Markov chain Monte Carlo (MCMC) methods, quasi maximum-likelihood (QML) and generalized method of moments (GMM).
To compare these estimation methods a large scale simulation study is conducted where many different parameter values and sample sizes are compared. Since both the latter two methods are non-likelihood based, our hypothesis is that the likelihood based MCMC would perform better. The conclusion of the study is that this is the case, MCMC turns out to be more efficient than QML and GMM by quite a... (More) - The stochastic volatility (SV) model is an alternative to GARCH models to model time varying volatility. In this thesis the basic stochastic volatility model and three different estimation methods are described---namely, Bayesian Markov chain Monte Carlo (MCMC) methods, quasi maximum-likelihood (QML) and generalized method of moments (GMM).
To compare these estimation methods a large scale simulation study is conducted where many different parameter values and sample sizes are compared. Since both the latter two methods are non-likelihood based, our hypothesis is that the likelihood based MCMC would perform better. The conclusion of the study is that this is the case, MCMC turns out to be more efficient than QML and GMM by quite a large margin, especially for estimating the latent volatilities. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8893766
- author
- Nilsson, Carl LU
- supervisor
- organization
- course
- NEKN01 20141
- year
- 2016
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- Monte Carlo simulation, stochastic volatility, Markov chain Monte Carlo, quasi-maximum likelihood, generalized method of moments
- language
- English
- id
- 8893766
- date added to LUP
- 2016-10-20 11:04:39
- date last changed
- 2016-10-20 11:04:39
@misc{8893766, abstract = {{The stochastic volatility (SV) model is an alternative to GARCH models to model time varying volatility. In this thesis the basic stochastic volatility model and three different estimation methods are described---namely, Bayesian Markov chain Monte Carlo (MCMC) methods, quasi maximum-likelihood (QML) and generalized method of moments (GMM). To compare these estimation methods a large scale simulation study is conducted where many different parameter values and sample sizes are compared. Since both the latter two methods are non-likelihood based, our hypothesis is that the likelihood based MCMC would perform better. The conclusion of the study is that this is the case, MCMC turns out to be more efficient than QML and GMM by quite a large margin, especially for estimating the latent volatilities.}}, author = {{Nilsson, Carl}}, language = {{eng}}, note = {{Student Paper}}, title = {{A Simulation Study comparing MCMC, QML and GMM Estimation of the Stochastic Volatility Model}}, year = {{2016}}, }