Coupling High-Frequency Data with Nonlinear Models in Multiple-Step-Ahead Forecasting of Energy Markets' Volatility

34 Pages Posted: 27 Apr 2014

See all articles by Jozef Baruník

Jozef Baruník

Charles University in Prague - Department of Economics; Institute of Information Theory and Automation, Prague

Tomas Krehlik

Charles University in Prague; Academy of Sciences of the Czech Republic

Date Written: April 25, 2014

Abstract

In the past decade, the popularity of realized measures and various linear models for volatility forecasting has attracted attention in the literature on the price variability of energy markets. However, results that would guide practitioners to a specific estimator and model when aiming for the best forecasting accuracy are missing. This paper contributes to the ongoing debate with a comprehensive evaluation of multiple-step-ahead volatility forecasts of energy markets using several popular high-frequency measures and forecasting models. To capture the complex patterns hidden to linear models commonly used to forecast realized volatility, this paper also contributes to the literature by coupling realized measures with artificial neural networks as a forecasting tool. Forecasting performance is compared across models as well as realized measures of crude oil, heating oil, and natural gas volatility during three qualitatively distinct periods covering the pre-crisis period, recent global turmoil of markets in 2008, and the most recent post-crisis period. We conclude that coupling realized measures with artificial neural networks results in both statistical and economic gains, reducing the tendency to over-predict volatility uniformly during all tested periods. Our analysis favors the median realized volatility, as it delivers the best performance and is a computationally simple alternative for practitioners.

Keywords: artificial neural networks, realized volatility, multiple-step-ahead forecasts, energy markets

JEL Classification: C14, C53, G17

Suggested Citation

Barunik, Jozef and Krehlik, Tomas, Coupling High-Frequency Data with Nonlinear Models in Multiple-Step-Ahead Forecasting of Energy Markets' Volatility (April 25, 2014). Available at SSRN: https://ssrn.com/abstract=2429487 or http://dx.doi.org/10.2139/ssrn.2429487

Jozef Barunik (Contact Author)

Charles University in Prague - Department of Economics ( email )

Opletalova 26
Prague 1, 110 00
Czech Republic

HOME PAGE: http://ies.fsv.cuni.cz/en/staff/barunik

Institute of Information Theory and Automation, Prague ( email )

Pod vodarenskou vezi 4
CZ-18208 Praha 8
Czech Republic

HOME PAGE: http://staff.utia.cas.cz/barunik/home.htm

Tomas Krehlik

Charles University in Prague ( email )

Praha 1
Czech Republic

Academy of Sciences of the Czech Republic ( email )

Narodni 3, 111 42
Praha 1, 117 20
Czech Republic

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