Volatility forecasting and volatility-timing strategies: A machine learning approach
Year: 2024
Type: article
Abstract: Recent increases in stock price volatility have generated renewed interest in volatility-timing strategies. Based on high-dimensional models including machine learning, we predict stock market volatility and apply them to improve the performance of volatility-timing portfolios. Using various evaluation methods, we verify that those machine learning models have better prediction performances relative to the standard volatility models. Asset allocation results suggest that volatility-timing portfolios constructed using machine learning models tend to outperform the market, with higher average returns during the volatile market period. Our empirical evidence supports the application of machine learning in the construction of volatility-timing portfolios.
Authors: Dohyun Chun, Hoon Cho, Doojin Ryu
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,370
OpenAlex ID: W4405775385