Exchange Rate Forecasting Based on Fundamental Macroeconomic Variables in a Floating Exchange Rate Regime: Evidence from an Emerging Economy

Exchange Rate Forecasting Based on Fundamental Macroeconomic Variables in a Floating Exchange Rate Regime: Evidence from an Emerging Economy

Yesim Helhel (Suleyman Demirel University, Turkey) and Seref Kalayci (Akdeniz University, Turkey)
DOI: 10.4018/jsesd.2012070102
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Abstract

Developing countries had a fixed exchange rate regime and avoided financial liberalization until the 1990’s. In the early 2000’s however, most of the developing countries abandoned their fixed exchange rate regimes in favor of floating rate regimes which in turn increased the importance of exchange rate forecasting in the emerging market economies. This paper intends to explain TR/USD (Turkish Lira/American Dollar) exchange rates by using macroeconomic fundamentals for the period between February 2001 and December 2009 on a monthly basis. A Vector Auto Regression (VAR) method is used. Among the macroeconomic Fundamentals, United States Federal Reserve Benchmark interest rates, one month Turkish Treasury Bill yields, Turkish import/export rates, m2 money supply and foreign direct investment explain the changes in TR / USD exchange rates.
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2. Literature Review

The Meese and Rogoff (1983) study is one of the most important articles on the topic of exchange rate forecasting in the literature. On the basis of empirical evidence, Meese and Rogoff concluded that random walk model is a successful forecasting model for exchange rates.

Following Meese and Rogoff, results of the studies of Somanath -1986; Alexander and Thomas – 1987; Boothe and Glassman – 1987; Wolff – 1987 and 1988 suggest that econometric forecasting models of exchange rates failed to outperform the random walk model in forecasting variations in exchange rates.

There are also studies which contradict the earlier findings. Results of the studies of Woo (1985), Schinasi and Swami (1989), Kuan and Liu (1995), Brooks (1997), Gencay (1999), Hogan et al. (2001), Cuaresma et al. (2004), Kumar and Thenmozhi (2004, 2005), Hongxing et al. (2007), and Chen et al. (2008), show that their models outperform the random walk model for certain time periods and currencies. These studies conclude that the presence of nonlinearity, volatility, non-stationarity in the exchange rates was not handled properly in previous empirical models. This resulted in the random walk scoring over other models.

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