Munich Personal RePEc Archive

Exchange rate volatility: A forecasting approach of using the ARCH family along with ARIMA SARIMA and semi-structural-SVAR in Turkey.

Ganbold, Batzorig and Akram, Iqra and Fahrozi Lubis, Raisal (2017): Exchange rate volatility: A forecasting approach of using the ARCH family along with ARIMA SARIMA and semi-structural-SVAR in Turkey. Published in: Uluslararası Ekonomi, Finans ve Ekonometri Öğrenci Sempozyumu (EFEOS) , Vol. 1, No. ISBN: 978-605-82381-1-4 (17 May 2017): pp. 144-182.

[img]
Preview
PDF
MPRA_paper_84447.pdf

Download (1MB) | Preview

Abstract

The ability to predict the volatility of Exchange rate is an enormous challenge when it comes to economic and financial considerations. In this context, it is important to be able to predict the exchange rate volatility in financial markets and the world economy. This paper proposes a heightened approach to modeling and forecasting of exchange rate volatility in Turkey. For past recent years, Turkey experienced political turbulence that the possibility of effecting exchange rate, thus create uncertainty volatility of exchange rate. Therefore daily exchange rate data have been taken from 2005-2017 and applied autoregressive conditional heteroskedasticity ARCH and GARCH families (EGARCH, IGARCH, and PARCH) to forecast exchange rate volatility. The proposed methodology able to calculate the breakpoint by including dummy variables. The result is more confined after including dummy that EGARCH (1,1) is best performing to forecast exchange rate volatility and successfully overcome the leverage effect on the exchange rate. Moreover, this paper also investigates the monthly data forecasting by applying ARIMA SARIMA along with SVAR technique for next few months. And Exchange rate pass-through also encounter it, which indicates the pass-through is more pronounced in PPI than CPI. The forecast result of SARIMA and SVAR distribute the same direction of fluctuation in the exchange rate that is declining of the current exchange rate in the future. However, ARIMA’s forecast tends to increase and different with two models.

UB_LMU-Logo
MPRA is a RePEc service hosted by
the Munich University Library in Germany.