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.
Author(s)details:-
Jagannayaki
K.
MBA Department, Institute of Aeronautical Engineering, Dundigal,
Hyderabad, Telangana, 500043, India.
Sreekanth
Yerramilli
Department of Management Sciences, Sun Stone-MRU University, India.
Vara
Lakshmi Thavva
MBA Department, Institute of Aeronautical Engineering, Dundigal,
Hyderabad, Telangana, 500043, India.
Nunna
Suresh
MBA Department, Institute of Aeronautical Engineering, Dundigal,
Hyderabad, Telangana, 500043, India.
Please See
the book here :- https://doi.org/10.9734/bpi/crbme/v7/129
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