Volatility of daily and Weekly Crude Oil Prices – A Statistical Perspective

Kanthala Sampath Kumar¹ , V. Swapna² , S.A. JyothiRani³ , V.V. Hara Gopal⁴

Section:Research Paper, Product Type: Isroset-Journal
Vol.5 , Issue.5 , pp.108-113, Oct-2018

CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v5i5.108113

Online published on Oct 31, 2018

Copyright © Kanthala Sampath Kumar, V. Swapna, S.A. JyothiRani , V.V. Hara Gopal . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Crude oil is a key commodity for the economy of any country. The changes in Crude oil prices are very complex and therefore, seem to be unpredictable. The effect of increasing price and its daily fluctuations affects not only the economies and financial markets but extends to reach individuals. This is because an increase in oil prices has a direct effect on petrol prices, in addition, it also affects the prices of other goods and services. Therefore, forecasting crude oil is a very important task to reduce the impact of price fluctuations, and help investors, hedgers, and individuals to make decisions when dealing with energy markets. However, one of the main challenges of the econometric models is to forecast such a seemingly unpredictable economic series. The econometric models have not been promising when used for forecasting, particularly in the case of complex series such as oil prices. Although linear and nonlinear time series models have performed much better job in forecasting oil prices, there is yet room for an improvement. If the data generating process is nonlinear, applying linear models could result in large forecast errors. Model specification in nonlinear modeling can also be very case dependent and time-consuming. This article proposes a novel technique for forecasting crude oil price based on Neural Networks. The study adopts the data on crude oil price of West Texas Intermediate (WTI). To evaluate the performance of the model, the study employs three measures, RMSE, MAE and MAPE. In this study, the forecasting accuracy of two models of neural networks, BPN and RNN are compared with the traditional econometric model. The results reveal that the proposed method outperforms the other in terms of forecast accuracy

Key-Words / Index Term :
Neural networks, root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE),Recurrent Neural Networks(RNN),Multi-Layer Perceptron (MLP),Oil Price and ARIMA.

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