Combining high frequency data with non-linear models for forecasting energy market volatility

0456185 - ÚTIA 2017 RIV NL eng J - Journal Article
Baruník, Jozef - Křehlík, Tomáš
Combining high frequency data with non-linear models for forecasting energy market volatility.
Expert Systems With Applications. Roč. 55, č. 1 (2016), s. 222-242. ISSN 0957-4174. E-ISSN 1873-6793
R&D Projects: GA ČR(CZ) GBP402/12/G097
Institutional support: RVO:67985556
Keywords : artificial neural networks * realized volatility * multiple-step-ahead forecasts * energy markets
Subject RIV: AH - Economics
Impact factor: 3.928, year: 2016
http://library.utia.cas.cz/separaty/2016/E/barunik-0456185.pdf

The popularity of realized measures and various linear models for volatility forecasting has been the focus of attention in the literature addressing energy markets' price variability over the past decade. However, there are no studies to help practitioners achieve optimal forecasting accuracy by guiding them to a specific estimator and model. This paper contributes to this literature in two ways. First, to capture the complex patterns hidden in linear models commonly used to forecast realized volatility, we propose a novel framework that couples realized measures with generalized regression based on artificial neural networks. Our second contribution is to comprehensively evaluate multiple-step-ahead volatility forecasts of energy markets using several popular high frequency measures and forecasting models. We compare forecasting performance across models and across realized measures of crude oil, heating oil, and natural gas volatility during three qualitatively distinct periods: the pre-crisis period, the 2008 global financial crisis, and the post-crisis period.
Permanent Link: http://hdl.handle.net/11104/0260445