期刊名称：Documents de Travail du Centre d'Economie de la Sorbonne
印刷版ISSN：1955-611X
出版年度：2017
出版社：Centre d'Economie de la Sorbonne
摘要：The extraction of the jump component in the dynamics of asset prices has witnesseda considerably growing body of literature. Of particular interest is the decomposition ofreturns’ quadratic variation between their continuous and jump components. Recent con-tributions highlight the importance of this component in forecasting volatility at differenthorizons. In this article, we extend a methodology developed in Maheu and McCurdy(2011) to exploit the information content of intraday data in forecasting the density ofreturns at horizons up to sixty days. We follow Boudt et al. (2011) to detect intradayreturns that should be considered as jumps. The methodology is robust to intra-weekperiodicity and further delivers estimates of signed jumps in contrast to the rest of theliterature where only the squared jump component can be estimated. Then, we estimatea bivariate model of returns and volatilities where the jump component is independentlymodeled using a jump distribution that fits the stylized facts of the estimated jumps.Our empirical results for S&P 500 futures, U.S. 10-year Treasury futures, USD/CADexchange rate and WTI crude oil futures highlight the importance of considering the con-tinuous/jump decomposition for density forecasting while this is not the case for volatilitypoint forecast. In particular, we show that the model considering jumps apart from thecontinuous component consistently deliver better density forecasts for forecasting horizonsranging from 1 to 30 days.
