Simulation-based Estimation Methods for Financial Time Series Models
AbstractThis chapter overviews some recent advances on simulation-based methods of estimating financial time series models that are widely used in financial economics. The simulation-based methods have proven to be particularly useful when the likelihood function and moments do not have tractable forms, and hence, the maximum likelihood (ML) method and the generalized method of moments (GMM) are diffcult to use. They are also capable of improving the finite sample performance of the traditional methods. Both frequentist's and Bayesian simulation-based methods are reviewed. Frequentist's simulation-based methods cover various forms of simulated maximum likelihood (SML) methods, the simulated generalized method of moments (SGMM), the efficient method of moments (EMM), and the indirect inference (II) method. Bayesian simulation-based methods cover various MCMC algorithms. Each simulation-based method is discussed in the context of a specific financial time series model as a motivating example. Empirical applications, based on real exchange rates, interest rates and equity data, illustrate how the simulation-based methods are implemented. In particular, SML is applied to a discrete time stochastic volatility model, EMM to estimate a continuous time stochastic volatility model, MCMC to a credit risk model, the II method to a term structure model.
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Bibliographic InfoPaper provided by Singapore Management University, School of Economics in its series Working Papers with number 19-2010.
Length: 37 pages
Date of creation: Oct 2010
Date of revision:
Publication status: Published in SMU Economics and Statistics Working Paper Series
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-11-27 (All new papers)
- NEP-CMP-2010-11-27 (Computational Economics)
- NEP-ECM-2010-11-27 (Econometrics)
- NEP-ETS-2010-11-27 (Econometric Time Series)
- NEP-ORE-2010-11-27 (Operations Research)
- NEP-SEA-2010-11-27 (South East Asia)
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