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

TD n. 578 2010

Eric Hillebrand, Marcelo Medeiros.

http://dx.doi.org/10.1080/07350015.2014.985828

We study the simultaneous occurrence of long memory and nonlinear effects, such as parameter changes and threshold effects, in time series models and apply our modeling framework to daily realized measures of integrated variance. 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 in financial volatility. An out-of-sample analysis shows that modeling these effects can improve forecast performance.

A ser publicado em Journal of Business & Economic Statistics

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