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Econometrics 2015, 3(1), 2-54;

Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity

Faculty of Economics, Konan University, 8-9-1 Okamoto, Higashinada-Ku, Kobe 658-8501, Japan
Department of Econometric Analysis, Faculty of Mathematics and Informatics, Vilnius University, Naugarduko 24, Vilnius LT-03225, Lithuania
Author to whom correspondence should be addressed.
Academic Editor: Timo Teräsvirta
Received: 31 July 2014 / Accepted: 19 December 2014 / Published: 16 January 2015
(This article belongs to the Special Issue Non-Linear Regression Modeling)
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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. View Full-Text
Keywords: forecasting; moving quantiles; non-linearity; realized volatility; test forecasting; moving quantiles; non-linearity; realized volatility; test

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Ishida, I.; Kvedaras, V. Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity. Econometrics 2015, 3, 2-54.

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