Asymmetries, breaks, and long-range dependence: An estimation framework for daily realized volatility

Eric Hillebrand () and Marcelo Medeiros ()
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Eric Hillebrand: Department of Economics, Louisiana State University

No 578, Textos para discussão from Department of Economics PUC-Rio (Brazil)

Abstract: We study the simultaneous occurrence of long memory and nonlinear effects, such as structural breaks and thresholds, in autoregressive moving average (ARMA) time series models and apply our modeling framework to series of daily realized volatility. Asymptotic theory for the quasi-maximum likelihood estimator is developed and a sequence of model specification tests is described. Our framework allows for general nonlinear functions, including smoothly changing intercepts. The theoretical results in the paper can be applied to any series with long memory and nonlinearity. We apply the methodology to realized volatility of individual stocks of the Dow Jones Industrial Average during the period 1995 to 2005. We find strong evidence of nonlinear effects and explore different specifications of the model framework. A forecasting exercise demonstrates that allowing for nonlinearities in long memory models yields significant performance gains.

Keywords: Realized volatility; structural breaks; smooth transitions; nonlinear models; long memory; persistence. (search for similar items in EconPapers)
Pages: 37p
Date: 2010-10
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:rio:texdis:578

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