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Econometrics 2015, 3(1), 2-54; doi:10.3390/econometrics3010002

Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity

1
Faculty of Economics, Konan University, 8-9-1 Okamoto, Higashinada-Ku, Kobe 658-8501, Japan
2
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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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. View Full-Text
Keywords: forecasting; moving quantiles; non-linearity; realized volatility; test forecasting; moving quantiles; non-linearity; realized volatility; test
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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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