Abstract
The aim of this paper is to investigate the volatility of USD/ALL daily exchange rate using generalized autoregressive conditional heteroscedasticity model. The data set used in this study cover a period from 5 January 2010 to 30 April 2015. Autoregressive conditional heteroscedasticity (ARCH), generalized autoregressive conditional heteroscedasticity (GARCH), threshold generalized autoregressive conditional heteroscedasticity (TGARCH), and exponential GARCH (EGARCH) model are applied to model the volatility of daily exchange rate return. The main result is that volatility of exchange rate return is affected by past volatility, and exchange return of USD/ALL is well modeled by this model.
Highlights
Modeling exchange rate volatility has gained a great importance and a number of models have been developed in empirical finance literature to investigate the volatility of exchange rate in different countries
High volatility is follows by high volatility and low volatility follows by low volatility, i.e. volatility acts in clusters
generalized autoregressive conditional heteroscedasticity (GARCH) models with normal and no normal conditional distributions are estimated and the results showed that GARCH(1,1) model best described the conditional volatility for most nonlinearity data [4]
Summary
Modeling exchange rate volatility has gained a great importance and a number of models have been developed in empirical finance literature to investigate the volatility of exchange rate in different countries. Exchange rate volatility is a measure of the fluctuation in an exchange rate, and it is known as a measure of risk [1]. The volatility of exchange rates is the source of exchange rates risk. Second volatility often is stationary [2] One such a models is the autoregressive conditional heteroscedasticity model (ARCH) introduced by Engle (1982). This model is generalized by Bollerslev (1986) into GARCH models. Nelson (1991) proposes the exponential GARCH (EGARCH) model which allow for asymmetric effect between positive and negative asset returns. Another model which allow for asymmetric is the threshold GARCH model (TGARCH). This model allows having differential impacts on conditional variance of the past shocks
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