SMU SOE Seminar (Nov 21, 2018): Generalized Jump Regressions for Local Moments

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TOPIC:

GENERALIZED JUMP REGRESSIONS FOR LOCAL MOMENTS

ABSTRACT We develop new high-frequency-based inference procedures for analyzing the relationship between jumps in instantaneous moments of stochastic processes. The estimation consists of two steps: the nonparametric determination of the jumps as differences in local averages, followed by a minimum-distance type estimation of the parameters of interest under general loss functions that include both least-square and more robust quantile regressions as special cases. The resulting asymptotic distribution of the estimator, derived under an infill asymptotic setting, is highly non-standard and generally not mixed normal. We establish the validity of a novel bootstrap algorithm for making feasible inference including bias-correction, and further justify its practical use through a series of Monte Carlo simulation experiments. We apply the new methods to study the relationship between trading intensity and spot volatility in the U.S. equity market at the time of important macroeconomic news announcement, as well as the relationship between these jumps and announcement surprises. Keywords: High-frequency data, Jumps, Robust regression, Semimartingale, News announcements, News surprises, Investor disagreement, Volume, Volatility. Click here to view the paper. Click here to view the CV.
		PRESENTER Jia Li Duke University
		RESEARCH FIELDS Econometrics Financial Economics
		DATE: 21 November 2018 (Wednesday)
		TIME: 4pm - 5.30pm
		VENUE: Meeting Room 5.1, Level 5 School of Economics Singapore Management University 90 Stamford Road Singapore 178903

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