MODELING ROMANIAN EXCHANGE RATE EVOLUTION WITH GARCH, TGARCH, GARCH- IN MEAN MODELS
Annals of the University of Oradea: Economic Science
In this paper we analyze the return of exchange rate in order to test and analyze the best models which are capable of forecasting accurately there evolution. We apply the GARCH family models on the exchange rate return in order to obtain the best models for there volatility. Financial time series often exhibit abnormal characteristics, such as: serial correlation, non-stationarity, heteroskedasticity, asymmetric and are leptokurtic. Due to these characteristics autoregressive models such as
... e models such as autoregressive (AR), moving average (MA) and autoregressive integrated moving-average (ARIMA) are unable to capture the evolution of financial series, to represent the special characteristic of financial a hole new range of models where developed : generalized autoregressive conditional heteroskedasticity (GARCH), which are taking into account the heteroskedasticity of the errors term. The GARCH model allows for lags in the autoregressive term and in the variance term incorporates lags of the previous variance and also for the errors. The GARCH family has expanded in the last years in order to incorporate for asymmetry (Threshold GARCH, TGARCH) and risk (GARCH -in Mean). We analyze the evolution of exchange rate for: Euro/RON, dollar/RON, yen/RON, British pound/RON, Swiss franc/RON for a period of five years from 2005 till 2011, we observe that in the analyzed period there are 2 sub-periods: 2005-2007 in which the RON appreciated constantly, and 2007-2011 in which the trend is depreciation for RON in respect to all the five currencies and the volatility was sensible higher than in the previous period. We obtain the returns on exchange rate by using the following transformation r=log(curs_t)-log(curs_t-1); the five analyzed series display an leptokurtic and asymmetric behavioral. Using the GARCH, TGARCH and GARCH-in Mean models, we explicit the evolution of volatility throw this period, choosing the best model using the following : minimizing the value of the sum of squared errors, Akaike and Bayesian Information Criterion.