Beleña, L.; Curbelo, E.; Martino, L.; Laparra, V. Second-Moment/Order Approximations by Kernel Smoothers with Application to Volatility Estimation. Mathematics2024, 12, 1406.
Beleña, L.; Curbelo, E.; Martino, L.; Laparra, V. Second-Moment/Order Approximations by Kernel Smoothers with Application to Volatility Estimation. Mathematics 2024, 12, 1406.
Beleña, L.; Curbelo, E.; Martino, L.; Laparra, V. Second-Moment/Order Approximations by Kernel Smoothers with Application to Volatility Estimation. Mathematics2024, 12, 1406.
Beleña, L.; Curbelo, E.; Martino, L.; Laparra, V. Second-Moment/Order Approximations by Kernel Smoothers with Application to Volatility Estimation. Mathematics 2024, 12, 1406.
Abstract
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.
Keywords
Quantile regression; kernel smoothers; times series; heteroscedasticity; nearest neighbours
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.