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Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity

Author

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  • Isao Ishida

    (Faculty of Economics, Konan University, 8-9-1 Okamoto, Higashinada-Ku, Kobe 658-8501, Japan)

  • Virmantas Kvedaras

    (Department of Econometric Analysis, Faculty of Mathematics and Informatics, Vilnius University, Naugarduko 24, Vilnius LT-03225, Lithuania)

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.

Suggested Citation

  • Isao Ishida & Virmantas Kvedaras, 2015. "Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity," Econometrics, MDPI, vol. 3(1), pages 1-53, January.
    • Handle: RePEc:gam:jecnmx:v:3:y:2015:i:1:p:2-54:d:44835
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