Random Walk or Switching Regimes in Stock Prices? Evidence from Out-of-Sample Forecasts

We use monthly observations on general stock price indices, over January 2001–August 2013, in order to assess simple stochastic time series models in terms of out-of-sample forecasts. Specifically, we examine the relative strength of out-of-sample forecasts of a random walk, with and without drift, against that of a non-linear segmented trends model where the switch between states is governed by a Markov chain. The forecasting performance of these processes is assessed by the root mean squared error of short- and long-term out-of-sample forecasts, varying from 1- to 12-month horizons. We obtain compelling evidence in favor of the Markov switching process in forecasting stock prices over short and medium-term horizons and across all countries considered. These results are most likely due to risk averse behavior of investors which has been amplified by the recent financial crisis