Date of Award

Spring 1996

Document Type

Thesis

Degree Name

Master of Science in Management - (M.S.)

Department

School of Industrial Management

First Advisor

Theologos Homer Bonitsis

Second Advisor

John Malindretos

Third Advisor

Iftekhar Hasan

Abstract

This paper discusses the important aspects of efficiency, expectations, and risk in the foreign exchange market. First, a brief presentation of the existing single-equation structural models of exchange-rate determination is given. A mathematical efficiency specification model is defined which employs of a system of interrelated equations testing the random walk and unbiasedness hypothesis. The model is validated by analyzing fluctuations in the spot and forward foreign exchange rates. Utilizing a regression estimation and many different specification and diagnostic tests for the series and the error terms (residuals), this study addresses the efficiency of the English, Canadian and French foreign exchange markets. The unbiased hypothesis is so prevalent in the finance literature that many tests for it have been developed. The study examines common tests and uses the regression results to demonstrate why each of these results does or does not reject the null hypothesis of unbiasedness. Furthermore, I compared two sample spans to test the intertemporal behavior of the spot and forward rates. In addition, the Johancen procedure (1991), which tests for cointegration in a system of equations, is applied to test for Efficient Market Hypothesis (EMH). The existence of such long run or cointegration relationships directly violates the EMI-I in a speculative efficient market (Granger 1986). In my sample testing cointegration was found to be present for the British Pound, Canadian Dollar, and French Franc. The random walk hypothesis as well has failed to be rejected for all three major currencies, however the unbiased forward rate hypothesis has been failed to be accepted for the British Pound and French Franc. However, more researches are needed in this area to be able to achieve better statistical inferences.

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