Non-linear exchange rate forecasting: The role of market microstructure variables (Canada, United States)

In this dissertation, we conduct a study of exchange rate models for the Canada/U.S. exchange rate. More specifically, we focus on their intra-day (high-frequency) and, subsequently, weekly forecast performances. All attempts to explain equilibrium exchange rates suffer from various problems: structural (macroeconomic) models used for out-of-sample forecasting produce poor forecasts. Given that different market participants trade based on private as well as public information sets, it is natural to assume that equilibrium exchange rate expectations are formed from a combination of macroeconomic fundamentals and market microstructure variables.; Chapter 1 motivates research in the area of non-linear microstructure exchange rate modeling, reviews the recent literature and introduces the general ideas behind this thesis.; Chapter 2 outlines Artificial Neural Networks (ANNs) and other non-linear modeling approaches used in this research.; Chapter 3 introduces a non-linear Canada/U.S. exchange rate microstructure model and provides a strong evidence for the microstructure effects. Our horse race for forecast performance results in a non-linear ANN model as the winner. ANN models outperform random walk and linear models in a number of recursive out-of-sample forecasts. The daily forecasts produced by ANN models are statistically significant according to Diebold and Mariano (1995) statistics. Apart from the nearest neighbours model, other linear and non-linear models are unable to generate significant predictions. The inclusion of a microstructure variable, order flow, substantially improves the predictive power of both the linear and non-linear models. Our findings also indicate the necessity of embodying (in a non-linear sense) information not only from interbank order flows, but also from commercial client and foreign institution transactions. No matter which non-linear model is used, there is always a slight forecast gain when dealer's private order flows are included into a set of explanatory variables.; Chapter 4 describes fuzzy logic technology in the form of approximate reasoning as a method that can be used in economics when dealing with continuous and imprecise economic variables, insufficient data for analysis and when a mathematical model of the process is unknown.; Chapter 5 develops an original and novel approach to generating trading strategies in the foreign exchange (FX) market based on forecasts from the ANN. Neuro-fuzzy (NF) decision-making technology is designed and implemented to obtain the optimal daily currency trading rule. We find that a non-linear ANN exchange rate microstructure model combined with a fuzzy logic controller (FLC) generates a set of trading strategies that, on average, earn a higher rate of return compared to the simple buy-and-hold strategy. We also find that after including transaction costs, the gains from the NF technology do not decline and increase on some periods. Finally, we successfully apply the NF model to the problem of determining the FX market's sentiment as reflected by the chartists' trading signals during periods of strong depreciation.