Measure of Volatility and Its Forecasting: Evidence from Naira / Dollar Exchange Rate
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In the last five decades, Box Jenkins methodology has been in existence to model
univariate time series data but fails or has limitations on modeling volatility. Most
financial time series data do exhibit heavy tail and thick distribution, to this effect
various parametric and semi-parametric non –linear time series models have been
proposed two or three decades ago to capture volatility. However, this research entails
measuring volatility and its forecasting using time series exchange rate annual data over
the period from 1981 to 2020 (wide periodicity). The exchange rate was transformed to
return, and parametric non –linear time series was modeled on it. It was found out that
GARCH (1,2) reveals continuous volatility for short while and was the best model to
predict the exchange rate volatility based on the evidence from measurement volatility
tool; RMSE, MAE, MAPE among other extensions of GARCH models; EGARCH and
TGARCH. EGARCH (1, 4) captures the asymmetry effect revealing that negative
shocks will persistently have an effect on the volatility of the naira/dollar exchange rate.
Keywords
QA Mathematics