Forecasting Interest Rate Volatility In Nigeria In The Arch-Garch Family Models

Abstract

Modeling the volatility of interest rates is essential for many areas in finance. However, it is well known that interest rate series exhibit non-normal characteristics that may not be captured with the standard GARCH model with a normal error distribution. But which GARCH model and error distribution to use is still open to question, especially where the model that best fits the in-sample data may not give the most effective out-of-sample volatility forecasting ability, which we use as the criterion for the selection of the most effective model from among the alternatives. In this work, the GARCH family models were employed in modeling interest rate volatility in Nigeria. A time series of data spanning January 2000 to December 2018 (in-sample data) was used to fit the models and out-of-sample data running from January to December 2019 to determine the best conditional volatility forecast. Twenty-four symmetric and eighteen asymmetric models were estimated and compared using three distribution errors; the normal, student's t, and the generalized error distributions; while four error loss functions, namely, RMSE, MAE, MAPE, and TIC, were adopted to determine the best fit and conditional volatility forecast. The result shows that the symmetric GED-GARCH (1,1) model was considered the overall best fit in both the symmetric and asymmetric models. The best-fitted GED-GARCH (1,1) model exhibited volatility persistence. The in-sample and out-of-sample volatility forecast of the GED-GARCH (1,1) model reveals that unconditional mean and variance will be achieved in the third month of 2019. Some transmission spillover effects running from the exchange and inflation rates to interest rates were also detected.