Can the random walk model be beaten in out-of-sample density forecasts? Evidence from intraday foreign exchange rates

@article{citeulike:1141888, abstract = {It has been documented that random walk outperforms most economic structural and time series models in out-of-sample forecasts of the conditional mean dynamics of exchange rates. In this paper, we study whether random walk has similar dominance in out-of-sample forecasts of the conditional probability density of exchange rates given that the probability density forecasts are often needed in many applications in economics and finance. We first develop a nonparametric portmanteau test for optimal density forecasts of univariate time series models in an out-of-sample setting and provide simulation evidence on its finite sample performance. Then we conduct a comprehensive empirical analysis on the out-of-sample performances of a wide variety of nonlinear time series models in forecasting the intraday probability densities of two major exchange rates--Euro/Dollar and Yen/Dollar. It is found that some sophisticated time series models that capture time-varying higher order conditional moments, such as Markov regime-switching models, have better density forecasts for exchange rates than random walk or modified random walk with GARCH and Student-t innovations. This finding dramatically differs from that on mean forecasts and suggests that sophisticated time series models could be useful in out-of-sample applications involving the probability density.}, author = {Hong, Yongmiao and Li, Haitao and Zhao, Feng }, citeulike-article-id = {1141888}, doi = {http://dx.doi.org/10.1016/j.jeconom.2006.11.003}, journal = {Journal of Econometrics}, keywords = {forecast, fx, garch, time-series}, posted-at = {2007-03-05 15:49:00}, priority = {2}, title = {Can the random walk model be beaten in out-of-sample density forecasts? Evidence from intraday foreign exchange rates}, url = {http://dx.doi.org/10.1016/j.jeconom.2006.11.003}, volume = {In Press, Corrected Proof} }

TY - JOUR ID - citeulike:1141888 L3 - citeulike-article-id:1141888 TI - Can the random walk model be beaten in out-of-sample density forecasts? Evidence from intraday foreign exchange rates JF - Journal of Econometrics VL - In Press, Corrected Proof N2 - It has been documented that random walk outperforms most economic structural and time series models in out-of-sample forecasts of the conditional mean dynamics of exchange rates. In this paper, we study whether random walk has similar dominance in out-of-sample forecasts of the conditional probability density of exchange rates given that the probability density forecasts are often needed in many applications in economics and finance. We first develop a nonparametric portmanteau test for optimal density forecasts of univariate time series models in an out-of-sample setting and provide simulation evidence on its finite sample performance. Then we conduct a comprehensive empirical analysis on the out-of-sample performances of a wide variety of nonlinear time series models in forecasting the intraday probability densities of two major exchange rates--Euro/Dollar and Yen/Dollar. It is found that some sophisticated time series models that capture time-varying higher order conditional moments, such as Markov regime-switching models, have better density forecasts for exchange rates than random walk or modified random walk with GARCH and Student-t innovations. This finding dramatically differs from that on mean forecasts and suggests that sophisticated time series models could be useful in out-of-sample applications involving the probability density. KW - forecast KW - fx KW - garch KW - time-series AU - Hong, Yongmiao AU - Li, Haitao AU - Zhao, Feng UR - http://dx.doi.org/10.1016/j.jeconom.2006.11.003 ER -