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

Justin Dang () and Aman Ullah
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Justin Dang: UCR

No 202204, Working Papers from University of California at Riverside, Department of Economics

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.

Keywords: Conditional variance; Nonparametric estimator; Semiparametric models; Forecasting; Machine Learning (search for similar items in EconPapers)
JEL-codes: C01 C14 C51 (search for similar items in EconPapers)
Pages: 14 Pages
Date: 2021-01, Revised 2022-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-ets, nep-for and nep-ore
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https://economics.ucr.edu/repec/ucr/wpaper/202204.pdf First version, 2021 (application/pdf)
https://economics.ucr.edu/repec/ucr/wpaper/202204R.pdf Revised version, 2022 (application/pdf)

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Journal Article: Machine-Learning-Based Semiparametric Time Series Conditional Variance: Estimation and Forecasting (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:ucr:wpaper:202204

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