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High-Frequency Runs and Flash-Crash Predictability

Irene Aldridge
The Journal of Portfolio Management Spring 2014, 40 (3) 113-123; DOI: https://doi.org/10.3905/jpm.2014.40.3.113
Irene Aldridge
is managing partner for research, development, and implementation of high-frequency trading algorithms at ABLE Alpha Trading, Ltd., and Big Data Initiatives, Inc., in New York, NY.
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  • For correspondence: ialdridge@ablealpha.com
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Abstract

This article describes research into the short-term nature of movements in price data. The study’s key finding is that asset returns do not evolve at the Gaussian increments commonly assumed by continuous pricing models. Instead, prices exhibit strong autocorrelation, often resulting in predictable one-directional sequences, or runs. These runs are more pronounced ahead of market crashes. Identifying these runs can help predict impending flash crashes as much as a day before a crash. The research further contributes to asset pricing and derivatives literature by deriving discreet and continuous closed-form expressions for the probability of flash crashes.

TOPICS: Exchanges/markets/clearinghouses, in markets, statistical methods

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The Journal of Portfolio Management: 40 (3)
The Journal of Portfolio Management
Vol. 40, Issue 3
Spring 2014
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High-Frequency Runs and Flash-Crash Predictability
Irene Aldridge
The Journal of Portfolio Management Apr 2014, 40 (3) 113-123; DOI: 10.3905/jpm.2014.40.3.113

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High-Frequency Runs and Flash-Crash Predictability
Irene Aldridge
The Journal of Portfolio Management Apr 2014, 40 (3) 113-123; DOI: 10.3905/jpm.2014.40.3.113
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  • Article
    • Abstract
    • RUNS: EMPIRICAL EVIDENCE
    • STATISTICAL PROPERTIES OF RUNS
    • FLASH-CRASH MODELING
    • ALTERNATIVE MODELING OF RUNS AND FLASH CRASHES
    • CONCLUSION
    • ENDNOTES
    • REFERENCES
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