TITLE:
Unravelling the Cipher of Indian Rupee’s Volatility: Testing the Forecasting Efficacy of the Rolling Symmetric and Asymmetric GARCH Models
AUTHORS:
Shalini Talwar, Aparna Bhat
KEYWORDS:
Diebold-Mariano Test, Exchange Rate Volatility, GARCH Models, Generalized Error Distribution, Heteroscedasticity
JOURNAL NAME:
Theoretical Economics Letters,
Vol.8 No.6,
April
23,
2018
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.