Abstract
This paper explores the application of artificial neural network in volatility forecasting. A recurrent neural network has been integrated in to GARCH model to form the hybrid model called GARCH-Neural model. The emphasis of the research is to investigate the performance of the variants of Backpropagation algorithms in training the proposed GARCH-neural model. In the first place, EGARCH (3, 3) was identified in this paper most preferred model describing crude oil price volatility in Nigeria. Similarly, Levenberg-Marquardt (LM) training algorithms were found to be fastest in convergence and also provide most accurate predictions of the volatility when to other training techniques.
Highlights
Crude oil is considered to be an important export commodity in Nigeria because of its contribution to the economy of the country
We proposed a hybrid model for forecasting volatility of the crude oil price in Nigeria
The data for this research is obtained from central bank of Nigeria website www.cbn.gov.ng and has been used in [33]
Summary
Crude oil is considered to be an important export commodity in Nigeria because of its contribution to the economy of the country. It was first discovered in 1958 and first produced well in 1956. From a production level of 1.9 million barrels per day in 1958, crude oil exports rose to 2.35 million barrels per day in the early 2000s. It had fluctuated between 2.35 and 2.40 million barrels per day between 2011 and 2015 which was far below the OPEC quota due to the socio-political instability in the oil-producing areas of the country. In terms of its contribution to total revenue, receipts from oil that constituted 26.3 per cent of the federally collected-revenue in 1970, rose to 82.1 per cent in 1974 and 83.0 per cent in 2008 largely on account of a rise in crude oil prices at the international market
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