Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity

Econometrics

Isao Ishida¹  Virmantas Kvedaras² 

[1] Faculty of Economics, Konan University, 8-9-1 Okamoto, Higashinada-Ku, Kobe 658-8501, Japan; E-Mail:;Department of Econometric Analysis, Faculty of Mathematics and Informatics, Vilnius University, Naugarduko 24, Vilnius LT-03225, Lithuania

关键词: forecasting;    moving quantiles;    non-linearity;    realized volatility;    test;   

DOI  :  10.3390/econometrics3010002

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.