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Time Series Simulation With Quasi-Monte Carlo Methods

Author

Listed:
  • Peter Winker

    (University of Manheim)

  • Jenny Li

    (The Pennsylvania State University)

Abstract

The purpose of this paper is to compare the use of quasi-Monte Carlo methods, in particular the so--called $(t,m,s)-nets$ technique, versus classical Monte Carlo approaches for the simulation of econometric time series models. Some theoretic results indicate the superiority of quasi-Monte Carlo methods. Successful applications already exist in image processing, physics, and the evaluation of finance derivatives. However, so far, quasi--Monte Carlo methods are rarely used in the field of econometrics. In this paper, we apply both traditional Monte Carlo and quasi--Monte Carlo simulation methods to time series models as they typically arise in macroeconometrics. The numerical evidence demonstrates that quasi--Monte Carlo methods outperform the traditional Monte Carlo for many time series models including non-linear and multivariate models.

Suggested Citation

  • Peter Winker & Jenny Li, 2000. "Time Series Simulation With Quasi-Monte Carlo Methods," Computing in Economics and Finance 2000 151, Society for Computational Economics.
    • Handle: RePEc:sce:scecf0:151
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    Cited by:

    1. Awadelkarim, Elsiddig & Caffarel, Michel & Del Moral, Pierre & Jasra, Ajay, 2025. "On the particle approximation of lagged Feynman–Kac formulae," Stochastic Processes and their Applications, Elsevier, vol. 188(C).
    2. Okten, Giray & Eastman, Warren, 2004. "Randomized quasi-Monte Carlo methods in pricing securities," Journal of Economic Dynamics and Control, Elsevier, vol. 28(12), pages 2399-2426, December.
    3. Dag Kolsrud, 2008. "Stochastic Ceteris Paribus Simulations," Computational Economics, Springer;Society for Computational Economics, vol. 31(1), pages 21-43, February.
    4. Yu-Ying Tzeng & Paul M. Beaumont & Giray Ökten, 2018. "Time Series Simulation with Randomized Quasi-Monte Carlo Methods: An Application to Value at Risk and Expected Shortfall," Computational Economics, Springer;Society for Computational Economics, vol. 52(1), pages 55-77, June.
    5. Johannes Paha, 2010. "Simulation and Prosecution of a Cartel with Endogenous Cartel Formation," MAGKS Papers on Economics 201007, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).

    More about this item

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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