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Machine Learning Based Semiparametric Time Series Conditional Variance: Estimation and Forecasting

Author

Listed:
  • Justin Dang

    (UCR)

  • Aman Ullah

    (Department of Economics, University of California Riverside)

Abstract

This paper proposes a new combined semiparametric estimator of the conditional variance that takes the product of a parametric estimator and a nonparametric estimator based on machine learning. A popular kernel based machine learning algorithm, known as kernel regularized least squares estimator, is used to estimate the nonparametric component. We discuss how to estimate the semiparametric estimator using real data and how to use this estimator to make forecasts for the conditional variance.Simulations are conducted to show the dominance of the proposed estimator in terms of mean squared error. An empirical application using S&P 500 daily returns is analyzed, and the semiparametric estimator effectively forecasts future volatility.

Suggested Citation

  • Justin Dang & Aman Ullah, 2021. "Machine Learning Based Semiparametric Time Series Conditional Variance: Estimation and Forecasting," Working Papers 202204, University of California at Riverside, Department of Economics, revised Jan 2022.
    • Handle: RePEc:ucr:wpaper:202204
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    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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