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Modelling of Volatility in Nigeria Crude Oil Price Using symmetric and asymmetric GARCH models
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Accurate modelling of volatility is important in finance, particularly as it relates to the modelling and forecasting of crude oil prices. This paper examines which of the model can best handle the price volatility of the Nigeria Crude Oil market. The models consider in this work are the Generalized Autoregressive Conditional Heteroskedasticity (GARCH), Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) and Power Autoregressive Conditional Heteroskedasticity (PARCH). The analysis displays the evaluation of the enactment of Nigerian crude oil price for GARCH (1, 1), EGARCH (1, 1) and PARCH (1, 1) models. The data used was collected from the Central Bank of Nigeria website for twelve (12) years (2010 – 2021) which make up of 2422 observations. Time Series was carried out using Eviews 9 and the result shows that the GARCH model outperforms EGARCH and PARCH models based on the value of Akaike Information Criteria.
Title: Modelling of Volatility in Nigeria Crude Oil Price Using symmetric and asymmetric GARCH models
Description:
Accurate modelling of volatility is important in finance, particularly as it relates to the modelling and forecasting of crude oil prices.
This paper examines which of the model can best handle the price volatility of the Nigeria Crude Oil market.
The models consider in this work are the Generalized Autoregressive Conditional Heteroskedasticity (GARCH), Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) and Power Autoregressive Conditional Heteroskedasticity (PARCH).
The analysis displays the evaluation of the enactment of Nigerian crude oil price for GARCH (1, 1), EGARCH (1, 1) and PARCH (1, 1) models.
The data used was collected from the Central Bank of Nigeria website for twelve (12) years (2010 – 2021) which make up of 2422 observations.
Time Series was carried out using Eviews 9 and the result shows that the GARCH model outperforms EGARCH and PARCH models based on the value of Akaike Information Criteria.
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