Assessment of Exchange Rate Volatility Using Garch Models: A Case Study from Indian Markets

The present study focuses on the time series behaviour of select currencies using GARCH Models. Exchange rate volatility is a useful measure of uncertainty about the economic environment of a country. Various forms of statistical models have been evolved to capture the volatility effect. These models are often applied to estimate the degree of exchange rate instability. Monthly returns of currency prices exhibit aggressiveness and a high degree of interdependence. In particular, generalized autoregressive conditional heteroscedastic GARCH (1, 1) processes fit to data very satisfactorily. A number of statistically compared out-of-sample estimates of monthly return variances are produced. It is discovered that GARCH model-based forecasts are preferable. This model is predicated on the basic assumptions of linearity and dependency. This paper aims to model the volatility of INR exchange rates against USD for the period from January 2000 to 5 January 2023 using the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models. Both symmetric and asymmetric models have been applied to measure factors that are related to the exchange rate returns such as leverage effect and volatility clustering. Based on the results, the static forecast of GJR-GARCH (1, 1) is the best model for predicting the future pattern for both INR and USD. The sustainability and relative value of a currency fluctuate due to several factors such as changes in demand & supply of goods & services, changes in the cost of the economy, rise & cut of interest rates, fiscal policy, govt.’s anti-inflationary measures, and inflation.