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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F16%3A00456185" target="_blank" >RIV/67985556:_____/16:00456185 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11230/16:10323719

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.eswa.2016.02.008" target="_blank" >http://dx.doi.org/10.1016/j.eswa.2016.02.008</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eswa.2016.02.008" target="_blank" >10.1016/j.eswa.2016.02.008</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

AH - Ekonomie

OECD FORD obor

—

Projekt

<a href="/cs/project/GBP402%2F12%2FG097" target="_blank" >GBP402/12/G097: DYME-Dynamické modely v ekonomii</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Expert Systems With Applications

ISSN

0957-4174

e-ISSN

—

Svazek periodika

55

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

36

Strana od-do

222-242

Kód UT WoS článku

000374811000017

EID výsledku v databázi Scopus

2-s2.0-84960075958