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Real-Time Trading Models and Statistical Properties of Foreign Exchange Rates
by
Richard Olsen
Olsen and Associates, Zurich, Switzerland
Coauthors: G. Ballocchi (Olsen and Associates, Zurich, Switzerland), M. Dacorogna (Olsen and Associates, Zurich, Switzerland), R. Gencay (Department of Economics, University of Windsor), O. Pictet (Olsen and Associates, Zurich, Switzerland)
Real-time trading models use high frequency live data feeds and their recommendations are transmitted to the traders through data feed lines instantaneously. In this paper, a widely used real-time trading model is used to evaluate the statistical properties of foreign exchange rates. The out-of-sample test period is seven years of five-minute series on three major foreign exchange rates against the US Dollar and one cross rate. Performance of the real-time trading models is measured by the annualized return, two measures of risk corrected annualized return, deal frequency and maximum drawdown. The simulated probability distributions of these performance measures are calculated with the three traditional processes, the random walk, GARCH and AR-GARCH. The null hypothesis that the real-time performances of the foreign exchange series are generated from these traditional processes is tested under the probability distributions of the performance measures. All four currencies yield postive annualized returns in the studied sampling period. These annualized returns are net of transaction costs. The results indicate that the excess returns of the real-time trading models, after taking the transaction costs and correcting for market risk, are not spurious. The results reject the random walk, GARCH(1,1) and AR-GARCH(1,1) processes as the data generating mechanisms for the high frequency foreign exchange rates. One important reason for the rejection of the GARCH type processes as a data generating mechanishm of foreign exchange returns is the aggregation property of the GARCH processes. The GARCH process behaves more like a homoskedastic process at lower frequencies. Since the real-time trading model's trading frequency is less than two deals per week, the trading model does not pick up the five minute level heteroskedastic structure at the weekly frequency. The results indicate that the foreign exchange series may possess a multi-frequency conditional mean and conditional heteroskedastic dynamics. The traditional heteroskedastic models fail to capture the entire dynamics by only capturing a slice of this dynamics at a given frequency. Therefore, a more realistic processes for foreign exchange returns should give consideration to the scaling behavior of returns at different frequencies and this scaling behavior should be taken into account in the construction of a representative process.
Date received: February 8, 1999
Copyright © 1999 by the author(s). The author(s) of this document and the organizers of the conference have granted their consent to include this abstract in Atlas Conferences Inc. Document # cacq-03.