In this paper we use a kernel-based approach to Crude Oil price prediction which should allow us to set up efficient risk management strategies. Practitioners find strong evidence that investor flows follow prices so Commodity investments are likely to continue to grow, and we believe this will drive an increasing importance for methodologies like Neural Networks for risk quantification, measurement and management. Crude Oil prices for both Brent and WTI in the last 12 year period are used to provide an accurate analysis for both time series. Four different Neural Network models are used. The superior model is the neurofuzzy network based on Sugeno first-order type rules, also known as the Adaptive Neuro-Fuzzy Inference System method, which provides both an accurate prediction of prices and their probability distribution.
In this paper we use a kernel-based approach to Crude Oil price prediction which should allow us to set up efficient risk management strategies. Practitioners find strong evidence that investor flows follow prices so Commodity investments are likely to continue to grow, and we believe this will drive an increasing importance for methodologies like Neural Networks for risk quantification, measurement and management. Crude Oil prices for both Brent and WTI in the last 12 year period are used to provide an accurate analysis for both time series. Four different Neural Network models are used. The superior model is the neurofuzzy network based on Sugeno first-order type rules, also known as the Adaptive Neuro-Fuzzy Inference System method, which provides both an accurate prediction of prices and their probability distribution.
Crude oil prices and kernel-based models / Panella, Massimo; D'Ecclesia, RITA LAURA; David G., Stack; Barcellona, Francesco. - In: INTERNATIONAL JOURNAL OF FINANCIAL ENGINEERING AND RISK MANAGEMENT. - ISSN 2049-0909. - STAMPA. - 1:3(2014), pp. 214-238. [10.1504/ijferm.2014.058761]
Crude oil prices and kernel-based models
PANELLA, Massimo;D'ECCLESIA, RITA LAURA;BARCELLONA, FRANCESCO
2014
Abstract
In this paper we use a kernel-based approach to Crude Oil price prediction which should allow us to set up efficient risk management strategies. Practitioners find strong evidence that investor flows follow prices so Commodity investments are likely to continue to grow, and we believe this will drive an increasing importance for methodologies like Neural Networks for risk quantification, measurement and management. Crude Oil prices for both Brent and WTI in the last 12 year period are used to provide an accurate analysis for both time series. Four different Neural Network models are used. The superior model is the neurofuzzy network based on Sugeno first-order type rules, also known as the Adaptive Neuro-Fuzzy Inference System method, which provides both an accurate prediction of prices and their probability distribution.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
