Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/258762 
Year of Publication: 
2022
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 15 [Issue:] 1 [Article No.:] 38 [Publisher:] MDPI [Place:] Basel [Year:] 2022 [Pages:] 1-12
Publisher: 
MDPI, Basel
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 the 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.
Subjects: 
conditional variance
nonparametric estimator
semiparametric models
forecasting
machine learning
kernel-regularized least squares
JEL: 
C01
C14
C51
C53
C58
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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