Forecasting exchange rates: an application to the daily high and low
In this thesis, we study the behaviour and forecastability of exchange rates . Most of the existing literature on the forecasting of exchange rates concentrates on the end of the day price, commonly known as the 'close' price. Meese and Rogoff  show that this price tends to follow the naive random walk model, which implies that the best forecast for the next period is the current observed value. Instead, we study the dynamics and the predictability of the daily high and low prices using real-world data for the currency pairs GBP/USD, EUR/USD and AUD/USD. The daily high and low are the maximum and minimum prices reached for each 24-hour period by the currency pairs. We find strong evidence that the daily close prices lag these highs and lows. We use this knowledge to build an autoregressive distributed lag (ARDL) rolling regression model that produces one day ahead out-of-sample forecasts of these high and low prices. We also build an algorithm that uses already existing dynamic regression methods to correct for the autocorrelation often observed in time-series data. The window size used for the estimation of our model parameters is very important due to the nature of time series data. We propose an empirical method to find the best suitable window size for the estimation of these parameters. The out-of-sample predictability of our regression models is compared to a few benchmark models by using a number of different performance measures. We show that our models outperform these benchmark models in terms of their forecasting ability of high and low prices. Furthermore, a triggering method is developed for trading exchange rates using a saturation-reset linear feedback controller. \ud First, we test our triggering method on an idealized market model, for which we propose a stochastic process. We then apply this triggering method to real-world data in order to study its performance. Finally, we construct trading strategies that combine these methods with our out-of-sample forecasts.
views in local repository
downloads in local repository
The information is available from the following content providers: