Structural exchange rate models explain only a small part of the movements in dollar exchange rate. Recent empirical work has focused on the failure to account for nonlinearities in the data generating mechanism, as an explanation of this bad performance. Here two bivariate threshold autoregressive models for the spot and forward exchange rates are considered. In the first model the regimes are determined by the log difference of the two rates; in the second one the regimes are driven by the forward spot no-arbitrage condition. These processes are able to capture the ‘swing’ behaviour observed in the exchange rate market. Finally the forecasting ability of the models for the dollar/DM exchange rate is evaluated by stochastic simulation.
Most non-linear techniques give good in-sample fits to exchange rate data but are usually outperformed by random walks or random walks with drift when used for out-of-sample forecasting. In the case of regime-switching models it is possible to understand why forecasts based on the true model can have higher mean squared error than those of a random walk or random walk with drift. In this paper we provide some analytical results for the case of a simple switching model, the segmented trend model. It requires only a small misclassification, when forecasting which regime the world will be in, to lose any advantage from knowing the correct model specification. To illustrate this we discuss some results for the DM/dollar exchange rate. We conjecture that the forecasting result is more general and describes limitations to the use of switching models for forecasting. This result has two implications. First, it questions the leading role of the random walk hypothesis for the spot exchange rate. Second, it suggests that the mean square error is not an appropriate way to evaluate forecast performance for non-linear models. Copyright (C) 1999 John Wiley & Sons, Ltd.
Linear models of market performance may be misspecified if the market is subdivided into distinct regimes exhibiting different behavior. Price movements in the United States real estate investment trusts and United Kingdom property companies markets are explored using a threshold autoregressive (TAR) model with regimes defined by the real rate of interest. In both U.S. and U.K. markets, distinctive behavior emerges, with the TAR model offering better predictive power than a more conventional linear autoregressive model. The research points to the possibility of developing trading rules to exploit the systematically different behavior across regimes.
The question of dependence of returns has been investigated in many ways. This paper proposes a matrix that sheds some light on many of these dependencies. In particular, overreaction and shock persistence and delayed reaction seem to play important roles and could well explain the presence of nonlinearities in the return series.
Linear models of market performance may be misspecified if the market is subdivided into distinct regimes exhibiting different behavior. Price movements in the United States real estate investment trusts and United Kingdom property companies markets are explored using a threshold autoregressive (TAR) model with regimes defined by the real rate of interest. In both U.S. and U.K. markets, distinctive behavior emerges, with the TAR model offering better predictive power than a more conventional linear autoregressive model. The research points to the possibility of developing trading rules to exploit the systematically different behavior across regimes.
Linear models of market performance may be misspecified if the market is subdivided into distinct regimes exhibiting different behaviour. Price movements in the US Real Estate Investment Trusts and UK Property Companies Markets are explored using a Threshold Autoregressive (TAR) model with regimes defined by the real rate of interest. In both US and UK markets, distinctive behaviour emerges, with the TAR model offering better predictive power than a more conventional linear autoregressive model. The research points to the possibility of developing trading rules to exploit the systematically different behaviour across regimes.
Abstract. Linear models of market performance may,be misspecified if the market is subdivided into distinct regimes exhibiting different behavior. Price movements,in the United States real estate investment trusts and United Kingdom property companies markets are explored using a threshold autoregressive (TAR) model with regimes defined by the real rate of interest. In both U.S. and U.K. markets, distinctive behavior emerges, with the TAR model offering better predictive power than a more conventional linear autoregressive model. The research points to the possibility of developing trading rules to exploit the systematically different behavior across regimes.
