Forecasting Realized Volatility With Kernel Ridge Regression

22 Pages Posted: 22 Aug 2018

See all articles by Blake LeBaron

Blake LeBaron

Brandeis University - International Business School

Date Written: August 9, 2018

Abstract

This paper explores a common machine learning tool, the kernel ridge regression, as applied to financial volatility forecasting. It is shown that kernel ridge provides reliable forecast improvements to both a linear specification, and a fitted nonlinear specification which represents well known empirical features from volatility modeling. Therefore, the kernel ridge specification is still finding some nonlinear improvements that are not part of the usual volatility modeling toolkit. Various diagnostics show it to be a reliable and useful tool. Finally, the results are applied in a dynamic volatility control trading strategy. The kernel ridge results again show improvements over linear modeling tools when applied to building a dynamic strategy.

Keywords: Machine Learning, Realized Volatility, Kernel Ridge Regression

JEL Classification: C58, C32, G12

Suggested Citation

LeBaron, Blake D., Forecasting Realized Volatility With Kernel Ridge Regression (August 9, 2018). Available at SSRN: https://ssrn.com/abstract=3229272 or http://dx.doi.org/10.2139/ssrn.3229272

Blake D. LeBaron (Contact Author)

Brandeis University - International Business School ( email )

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