Modeling autoregressive processes with moving-quantiles-implied nonlinearity - Isao Ishida; Virmantas Kvedaras (Econometrics, 2015)
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 & Poor'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.
Keywords: C22; C58; 330; forecasting; moving quantiles; non-linearity; realized volatility; test