Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity

Isao Ishida and Virmantas Kvedaras
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Virmantas Kvedaras: Department of Econometric Analysis, Faculty of Mathematics and Informatics, Vilnius University, Naugarduko 24, Vilnius LT-03225, Lithuania

Econometrics, 2015, vol. 3, issue 1, 1-53

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.

Keywords: forecasting; moving quantiles; non-linearity; realized volatility; test (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2015
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