MODELLING MONTHLY WIND SPEED IN NORTH WEST NIGERIA: ExAR-FIGARCH AND ExAR-GARCH COMPARATIVE ANALYSIS

Abstract

Understanding wind speed is the key planning renewable energy projects studying climate patterns and forecasting weather. In our study we explore how monthly wind speeds behave in North West Nigeria using an advanced model known as the Exponential Autoregressive-Fractional Integrated Generalized Autoregressive Conditional Heteroscedasticity (ExAR-FIGARCH). This model not only captures the lingering effects of past wind speeds but also accounts for unpredictable shifts over time. To see how its stacks up, we compare its performance against the more traditional ExAR-GARCH model, which mainly focuses on short-term fluctuations. We estimated two models and results showed the ExAR-FIGARCH model is better based on serial correlation analysis, efficient parameters and measures of accuracy, along with their ability to forecast future values. Our findings suggest that embracing long-memory effects in wind speed analysis could provide better insights into the region’s wind energy potential. 

Key words:  Wind speed, ExpAR-FIGARCH, ExpAR-GARCH model Long Memory, Volatility modelling.