Second Moment/Order Approximations by Kernel Smoothers with Application to Volatility Estimation

León Beleña Lamor; Ernesto Curbelo Benitez; Luca Martino; Valero Laparra Perez-Muelas

doi:10.20944/preprints202404.0080.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 31 March 2024 / Approved: 1 April 2024 / Online: 2 April 2024 (02:31:54 CEST)

Volatility estimation and quantile regression are relevant active research areas in statistics, machine learning and econometrics. In this work, we propose two procedures to estimate local variances in generic regression problems by using of kernel smoothers. The proposed schemes can be applied in multidimesional scenarios (not just for time series analysis) and easily in a multi-output framework, as well. Moreover, they allow the possibility of providing uncertainty estimation using a generic kernel smoother technique. Several numerical experiments show the benefits of the proposed methods, even comparing with benchmark techniques. One of these experiment involves a real dataset analysis.

Quantile regression; kernel smoothers; times series; heteroscedasticity; nearest neighbours

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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Second Moment/Order Approximations by Kernel Smoothers with Application to Volatility Estimation

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