Abstract
Modelling exchange rate volatility is crucially important because of its diverse implications on the profitability of corporations and decisions of policy makers. This paper empirically investigates exchange rate volatility of India’s currency by applying rolling symmetric and asymmetric GARCH models to the USDINR and EURINR daily exchange rates for a period spanning April 1, 2006 through January 31, 2018, resulting in total observations of 2861. To estimate GARCH (1,1) and EGARCH (1,1) models, the data window is rolled over five years with nearly 1200 observations and one month is used as forecast period for each window. Both, in-sample criteria like the log likelihood criteria, Akaike information criterion (AIC), the Bayesian information criterion (SIC) and Hannan Quinn criterion (HQC) as well as the out-of-sample criteria like Mean Squared Error (MSE) and Mean Absolute Error (MAE) have been used to test model fit and forecast accuracy of the models. To test the robustness of the findings, Diebold-Mariano test is used to compare the predictive accuracy of both the models. Further, the forecasting accuracy of the two models has also been tested by splitting the sample period into periods of tranquility and volatility in Indian exchange rate. Results show that GARCH (1,1) model with generalized error distribution is adequate to capture the mean and volatility process of USDINR and EURINR exchange rate returns.
Highlights
The assumption of homoscedasticity or constancy of variance over time is inappropriate as it is an established fact that the variance of financial time series like exchange rate and stock price data is not constant
This paper empirically investigates exchange rate volatility of India’s currency by applying rolling symmetric and asymmetric GARCH models to the USDINR and EURINR daily exchange rates for a period spanning April 1, 2006 through January 31, 2018, resulting in total observations of 2861
The results show that the GARCH (1,1) clearly scores better than the Exponential GARCH (EGARCH) (1,1) model on the Mean Squared Error (MSE) criterion
Summary
The assumption of homoscedasticity or constancy of variance over time is inappropriate as it is an established fact that the variance of financial time series like exchange rate and stock price data is not constant. The volatility of any financial time series is dynamic and time-varying and any attempt to forecast it with acceptable accuracy requires application of models that have heteroscedasticity as their underlying assumption. Models based Autoregressive Conditional Heteroscedasticity (ARCH) given by [1] and its generalized variant (GARCH) given by [2] capture the dynamic nature of time series volatility quite effectively. The use of GARCH family models has become quite popular among researchers and analysts for modelling volatility of financial time series. The world was recovering quite effectively from the Y2K crisis and it recorded annual GDP growth of 4.31 percent in 2006 and 4.25 percent in 2007 when the global financial crisis of 2008 reared its head. The global economy seems to be getting back on track as the World Economic Outlook report has projected a growth in global output by 3.5 percent in 2017 and 3.6 percent in 2018 [4]
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