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Estimating Stochastic Volatility Models: A Comparison of Two Importance Samplers
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Kai Ming Lee
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May 18, 2004
In this paper, we describe and compare two simulated Maximum Likelihood estimation methods for a basic stochastic volatility model. For both methods, the likelihood function is estimated using importance sampling techniques. Based on a Monte Carlo study, we assess which method is more effective. Further, we validate the two methods using diagnostic importance sampling test procedures. Stochastic volatility models with Gaussian and Student-t distributed disturbances are considered.
Published Online: 2004-5-18
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Articles in the same Issue
- Article
- Introduction
- Extensions of the Forward Search to Time Series
- Analyzing Financial Time Series through Robust Estimators
- Clusters of Extreme Observations and Extremal Index Estimate in GARCH Processes
- Estimating Stochastic Volatility Models: A Comparison of Two Importance Samplers
- MCMC Bayesian Estimation of a Skew-GED Stochastic Volatility Model
- GARCH-type Models with Generalized Secant Hyperbolic Innovations
- Mixture Processes for Financial Intradaily Durations
- Constructing Non-linear Gaussian Time Series by Means of a Simplified State Space Representation
- Statistical Tests for Lyapunov Exponents of Deterministic Systems
- Assessing Chaos in Time Series: Statistical Aspects and Perspectives
- On the Stationarity of First-order Nonlinear Time Series Models: Some Developments
- Experimental Design for Time-Dependent Models with Correlated Observations
- Inference and Forecasting for ARFIMA Models With an Application to US and UK Inflation
- Stability and Consistency of Seasonally Adjusted Aggregates and Their Component Patterns
- Seasonal Specific Structural Time Series
- Relationship between Local and Global Nonparametric Estimators Measures of Fitting and Smoothing
