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Simulation-Based Estimation Methods for Financial Time Series Models

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Handbook of Computational Finance

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Abstract

This 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 difficult to use. They are also useful for improving the finite sample performance of the traditional methods. Both frequentist and Bayesian simulation-based methods are reviewed. Frequentist’s simulation-based methods cover various forms of simulated maximum likelihood (SML) methods, simulated generalized method of moments (SGMM), efficient method of moments (EMM), and indirect inference (II) methods. 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 to implement the simulation-based methods. In particular, we apply SML 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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Acknowledgements

I gratefully acknowledge financial support from the Ministry of Education AcRF Tier 2 fund under Grant No. T206B4301-RS. Data and program code used in this paper can be download from my website at http://www.mysmu.edu/faculty/yujun/research.html.

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Authors and Affiliations

  1. School of Economics, Lee Kong Chian School of Economics and Sim Kee Boon Institute for Financial Economics, Singapore Management University, 90 Stamford Road, Singapore, 178903, Singapore

    Jun Yu

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Correspondence to Jun Yu .

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Editors and Affiliations

  1. , Risk Management Institute, National University of Singapore, 21 Heng Mui Keng Terrace, Level 4, Singapore, 119613, Singapore

    Jin-Chuan Duan

  2. L.v.Bortkiewicz Chair of Statistics, C.A.S.E. Centre f. Appl. Stat. & Econ., Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin, 10099, Germany

    Wolfgang Karl Härdle

  3. , Department of Computational and Data Sci, George Mason University, University Drive 4400, Fairfax, 22030, Virginia, USA

    James E. Gentle

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Yu, J. (2012). Simulation-Based Estimation Methods for Financial Time Series Models. In: Duan, JC., Härdle, W., Gentle, J. (eds) Handbook of Computational Finance. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17254-0_15

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