1. Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity
- Author
-
Isao Ishida and Virmantas Kvedaras
- Subjects
Economics and Econometrics ,Realized variance ,jel:C01 ,forecasting ,jel:B23 ,jel:C ,jel:C00 ,non-linearity ,realized volatility ,test ,Econometrics ,ddc:330 ,jel:C1 ,jel:C2 ,C58 ,jel:C3 ,Autoregressive integrated moving average ,jel:C4 ,jel:C5 ,jel:C8 ,Mathematics ,Series (mathematics) ,lcsh:HB71-74 ,moving quantiles ,lcsh:Economics as a science ,SETAR ,Nonlinear system ,Autoregressive model ,STAR model ,C22 ,Quantile - Abstract
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.
- Published
- 2015
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