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TOPIC:
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GENERALIZED JUMP REGRESSIONS FOR LOCAL MOMENTS
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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.
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Click here to view the CV.
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PRESENTER
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RESEARCH FIELDS
Econometrics
Financial Economics
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DATE:
21 November 2018 (Wednesday)
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TIME:
4pm - 5.30pm
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VENUE:
Meeting Room 5.1, Level 5
School of Economics
Singapore Management University
90 Stamford Road
Singapore 178903
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