Characterizing financial crises using high-frequency data

被引：0

作者：

Dungey, Mardi ^{[1
,2
,3
]}

Holloway, Jet ^{[1
]}

Yalaman, Abdullah ^{[3
,4
]}

Yao, Wenying ^{[5
]}

机构：

[1] Univ Tasmania, Dept Business & Econ, Hobart, Tas, Australia

[2] Univ Cambridge, Ctr Financial Anal & Policy, Cambridge, England

[3] Australian Natl Univ, Ctr Appl Macroecon Anal, Canberra, ACT, Australia

[4] Eskisehir Osmangazi Univ, Dept Business Adm, Eskisehir, Turkey

[5] Deakin Univ, Dept Econ, Burwood, Australia

来源：

QUANTITATIVE FINANCE | 2022年 / 22卷 / 04期

基金：

澳大利亚研究理事会;

关键词：

High-frequency data; Tail behavior; Financial crisis; US Treasury markets; HEDGE FUND CONTAGION; JUMPS; VOLATILITY; COMPONENTS; RETURNS; MODELS;

D O I：

10.1080/14697688.2022.2027504

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

Recent advances in high-frequency financial econometrics enable us to characterize which components of the data generating processes change in crisis, and which do not. This paper introduces a new statistic which captures large discontinuities in the composition of a given price series. Monte Carlo simulations suggest that this statistic is useful in characterizing the tail behavior across different sample periods. An application to US Treasury market provides evidence consistent with identifying periods of stress via flight-to-cash behavior which results in increased abrupt price falls at the short end of the term structure and decreased negative price jumps at the long end.

引用

页码：743 / 760

页数：18

共 46 条

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