Univariate forecasting of Indian exchange rates: a comparison

The objective of this paper is to search for a univariate forecasting model that can provide the most accurate forecasts of monthly exchange rates of Indian rupee over the period August 1994-April 2014. A random walk model is used as the benchmark model. Using Box-Jenkins methodology, ARIMA structures of exchange rates are identified. The presence of heteroscedastic variance of ARIMA residuals has been accomplished through the re-estimation of ARIMA models including autoregressive conditional heteroscedasticity (ARCH)/generalised autoregressive conditional heteroscedasticity (GARCH) parameters. From the estimated models, in-sample (April 1994-July 2010) and out-of-sample (August 2010-April 2014), forecasts for 1-, 3- and 6-month horizon are generated. The forecasting ability of the estimated models is accessed by forecast error statistics. The paper finds that in general the random walk model outperforms ARIMA and ARCH/GARCH models for forecasting exchange rates of Indian rupee under the in-sample and the out-of-sample period.