Modeling autoregressive processes with moving-quantiles-implied nonlinearity

Isao Ishida and Virmantas Kvedaras

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.

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Year of publication:	2015
Authors:	Ishida, Isao ; Kvedaras, Virmantas
Published in:	Econometrics : open access journal. - Basel : MDPI, ISSN 2225-1146, ZDB-ID 2717594-7. - Vol. 3.2015, 1, p. 2-54
Subject:	forecasting \| moving quantiles \| non-linearity \| realized volatility \| test \| Volatilität \| Volatility \| Nichtlineare Regression \| Nonlinear regression \| Prognoseverfahren \| Forecasting model \| Zeitreihenanalyse \| Time series analysis \| Schätzung \| Estimation \| Autokorrelation \| Autocorrelation \| ARCH-Modell \| ARCH model \| Kapitaleinkommen \| Capital income \| Stochastischer Prozess \| Stochastic process \| Schätztheorie \| Estimation theory