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Generalized Jump Regressions for Local Moments

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 nonstandard and generally not mixed normal. In addition, we establish the validity of a novel bootstrap algorithm for making feasible inference including bias-correction. The new methods are applied in a study on the relationship between trading intensity and spot volatility in the U.S. equity market at the time of important macroeconomic news announcement.

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Informasi Detail

Judul Seri

JOURNAL OF BUSINESS AND ECONOMIC STATISTICS; Vol.39 No.4 October 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 1015 -1025

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

ROBUST REGRESSION
VOLUME
VOLATILITY
JUMPS
HIGH-FREQUENCY DATA
NEWS ANNOUNCEMENTS

Info Detail Spesifik

https://doi.org/10.1080/07350015.2020.1753526

Pernyataan Tanggungjawab

Tim Bollerslev, Jia Li, Leonardo Salim Saker Chaves

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