Predicting risk in energy markets: Low-frequency data still matter

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14560%2F21%3A00119318" target="_blank" >RIV/00216224:14560/21:00119318 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0306261920315567#" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0306261920315567#</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.apenergy.2020.116146" target="_blank" >10.1016/j.apenergy.2020.116146</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Predicting risk in energy markets: Low-frequency data still matter

Popis výsledku v původním jazyce

Are high-frequency data always needed to generate precise forecasts of risk measures in energy markets? This study attempts to shed light on this question. We study whether energy market participants can rely on low-frequency volatility estimators when interested in two market risks: volatility and expected shortfall. Using ten years of data on four of the world’s most liquid energy futures contracts – the crude oil benchmarks West Texas Intermediate and Brent, as well as natural gas and heating oil futures – we provide conclusive evidence that while realized volatility models lead to much more accurate forecasts in the short term, medium- and longer-term forecasts based on daily ranges are comparable and, in some cases, even more accurate than their high-frequency counterparts that are computationally more intensive and that require costly data. Next, we present an application to predict extreme price declines - expected shortfall - with low-frequency volatility estimates. For that purpose, we propose a novel complete subset quantile regression model to predict multiple-day-ahead expected shortfall . Our back-testing results show that the new model leads to well-specified price decline forecasts, particularly when used with low-frequency volatility estimates. These results show that depending on the forecast horizon and purpose, low-frequency, publicly available, free of cost and easy to process volatility estimators still matter.

Název v anglickém jazyce

Predicting risk in energy markets: Low-frequency data still matter

Popis výsledku anglicky

Are high-frequency data always needed to generate precise forecasts of risk measures in energy markets? This study attempts to shed light on this question. We study whether energy market participants can rely on low-frequency volatility estimators when interested in two market risks: volatility and expected shortfall. Using ten years of data on four of the world’s most liquid energy futures contracts – the crude oil benchmarks West Texas Intermediate and Brent, as well as natural gas and heating oil futures – we provide conclusive evidence that while realized volatility models lead to much more accurate forecasts in the short term, medium- and longer-term forecasts based on daily ranges are comparable and, in some cases, even more accurate than their high-frequency counterparts that are computationally more intensive and that require costly data. Next, we present an application to predict extreme price declines - expected shortfall - with low-frequency volatility estimates. For that purpose, we propose a novel complete subset quantile regression model to predict multiple-day-ahead expected shortfall . Our back-testing results show that the new model leads to well-specified price decline forecasts, particularly when used with low-frequency volatility estimates. These results show that depending on the forecast horizon and purpose, low-frequency, publicly available, free of cost and easy to process volatility estimators still matter.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50206 - Finance

Projekt

<a href="/cs/project/GA18-05829S" target="_blank" >GA18-05829S: Predikce volatility na rozvijících se finančních trzích</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Applied Energy

ISSN

0306-2619

e-ISSN

1872-9118

Svazek periodika

282

Číslo periodika v rámci svazku

Part A

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

17

Strana od-do

1-17

Kód UT WoS článku

000599659800003

EID výsledku v databázi Scopus

2-s2.0-85096475837