Forecasting petroleum futures markets volatility: The role of regimes and market conditions

Nomikos, Nikos K.; Pouliasis, Panos K.

doi:10.1016/j.eneco.2010.11.013

Cited by 120 publications

(75 citation statements)

References 72 publications

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“…We may conclude that for all the tested futures, crude oil, heating oil, and natural gas, the models tend to over-predict slightly, but only by approximately 55% on average (with maximum levels of over-predictions for natural gas under 60%), whereas in many occasions, models yield an equal number of over-and under-predictions. This is an important finding, as, in comparison to GARCH-type models that strongly over-predict volatility (Nomikos and Pouliasis, 2011;Wang and Wu, 2012), high-frequency data appear to yield substantial improvement in this respect.…”

Section: Forecasting Performance Before the Crisismentioning

confidence: 70%

“…Nonetheless, the asymmetry of forecast error is important for the practitioners, as it alerts us to whether the modeling strategy tends to over-predict or under-predict the volatility. Testing the forecasts of energy commodities, Nomikos and Pouliasis (2011) confirm the strong tendency of GARCH type models to over-predict the volatility of crude oil, heating oil, and natural gas. This finding was further confirmed by Wang and Wu (2012), who find multivariate GARCH-type models to suffer from over-predictions as well.…”

Section: Statistical Evaluation Of the Forecastsmentioning

confidence: 71%

“…As discussed by Nomikos and Pouliasis (2011), these metrics do not provide information about the asymmetry of the errors commonly found by the literature, especially for the parametric GARCH models. Nonetheless, the asymmetry of forecast error is important for the practitioners, as it alerts us to whether the modeling strategy tends to over-predict or under-predict the volatility.…”

Section: Statistical Evaluation Of the Forecastsmentioning

confidence: 94%

“…These error levels also translate to economic benefits in terms of Value-at-Risk. One of the loss functions we use in the exercise allows us to assess whether the models tend to over-predict the volatility, as commonly found by the GARCH-type models (e.g., see Nomikos and Pouliasis (2011), who confirm the strong tendency of GARCH-type models to over-predict the volatility of crude oil, heating oil, and gasoline, further confirmed by Wang and Wu (2012), who find multivariate GARCH-type models to suffer from over-predictions as well). A uniform finding is that coupling neural networks with highfrequency data brings large reductions in over-estimation tendency compared with the previous literature.…”

Section: Introductionmentioning

confidence: 99%

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

Baruník

Křehlík

2014

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract: Terms of use: Documents inIn 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 specifc 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 precrisis period, recent global turmoil of markets in 2008, and the most recent post-crisis period. We conclude that coupling realized measures with arti_cial 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.

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Section: Forecasting Performance Before the Crisismentioning

confidence: 70%

Section: Statistical Evaluation Of the Forecastsmentioning

confidence: 71%

Section: Statistical Evaluation Of the Forecastsmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations