Nonlinearity, Breaks, and Long-Range Dependence in Time-Series Models
AbstractWe study the simultaneous occurrence of long memory and nonlinear effects, such as parameter changes and threshold effects, in ARMA time series models and apply our modeling framework to daily realized volatility. Asymptotic theory for parameter estimation is developed and two model building procedures are proposed. The methodology is applied to stocks of the Dow Jones Industrial Average during the period 2000 to 2009. We find strong evidence of nonlinear effects.
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Bibliographic InfoPaper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2012-30.
Date of creation: 12 Jun 2012
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Web page: http://www.econ.au.dk/afn/
Smooth transitions; long memory; forecasting; realized volatility.;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-07-14 (All new papers)
- NEP-ECM-2012-07-14 (Econometrics)
- NEP-ETS-2012-07-14 (Econometric Time Series)
- NEP-FOR-2012-07-14 (Forecasting)
- NEP-ORE-2012-07-14 (Operations Research)
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