Fernando Fernández Rodríguez

Non-Linear Forecasting Methods: Some Applications to the Analysis of Financial Series

The evolution of financial data shows a high degree of volatility of the series, coupled with increasing difficulties of forecasting the shorter is the time horizon, when using standard (i.e., based on linear models) forecasting methods. Some alternative forecasting methods for non-linear time series, based on the literature on complex dynamic systems, have been recently developed, which can be particularly useful in the analysis of financial time series. In this paper we present a summary of some of these new techniques, and then show some applications to the analysis of several financial series (i.e., exchange rates, stock prices, and interest rates), which illustrate the usefulness of the approach. Since non-linear forecasting methods require the usage of very long time series, the availability of high-frequency data for these variables make them the best candidates among economic time series for the application of this methodology.

Technical Analysis in Foreign Exchange Markets: Linear Versus Nonlinear Trading Rules

In this paper we assess the economic significance of the nonlinear predictability of EMS exchange rates. To that end, and using daily data for nine EMS currencies covering the 1st January 1978-31st December 1994 period, we consider nearest-neighbour nonlinear predictors, transforming their forecasts into a technical trading rule, whose profitability has been evaluated against the traditional (linear) moving average trading rules, considering both interest rates and transaction costs. Our results suggest that in most of the cases a trading rule based on a nonlinear predictor outperforms the moving average, both in terms of returns and in terms of the ideal profit and the Sharpe ratio profitability indicators.

A New Test for Chaotic Dynamics Using Lyapunov Exponents

We propose a new test to detect chaotic dynamics, based on the stability of the largest Lyapunov exponent from different sample sizes. This test is applied to the data used in the single-blind controlled competition tests for nonlinearity and chaos that were generated by Barnett et al. (1997), as well as to several chaotic series. The results suggest that the new test is particularly effective when compared to other stochastic alternatives (both linear and nonlinear). The test size is one for large samples, although for small sample sizes it diminishes below the nominal size for two out of the three chaotic processes considered, what is not a surprise given some well-known properties of such processes.