%0 Journal Article
%T Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity
%A Isao Ishida
%A Virmantas Kvedaras
%J Econometrics
%P 2-54
%D 2015
%I MDPI AG
%R 10.3390/econometrics3010002
%X We introduce and investigate some properties of a class of nonlinear time series models based on the moving sample quantiles in the autoregressive data generating process. We derive a test fit to detect this type of nonlinearity. Using the daily realized volatility data of Standard & PoorĄ¯s 500 (S&P 500) and several other indices, we obtained good performance using these models in an out-of-sample forecasting exercise compared with the forecasts obtained based on the usual linear heterogeneous autoregressive and other models of realized volatility.
%K forecasting
%K moving quantiles
%K non-linearity
%K realized volatility
%K test
%U http://www.mdpi.com/2225-1146/3/1/2