Detecting market crashes by analysing long-memory effects using high-frequency data

DOI10.1080/14697688.2012.664937zbMath1278.91110OpenAlexW2005386276MaRDI QIDQ2873035

Ernest Barany, Maria P. Beccar-Varela, Indranil SenGupta, Ionut Florescu

Publication date: 17 January 2014

Published in: Quantitative Finance (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/14697688.2012.664937

zbMATH Keywords

Lévy processesdetrended fluctuation analysisHurst parametertruncated Lévy flightlong memory effectsdata sampled with high frequency

Mathematics Subject Classification ID

Statistical methods; risk measures (91G70) Stochastic models in economics (91B70)

Related Items (5)

Stochastic differential equations applied to the study of geophysical and financial time series ⋮ Analysis of the Lehman Brothers collapse and the flash crash event by applying wavelets methodologies ⋮ Modeling high frequency stock market data by using stochastic models ⋮ Stochastic dominance statistics for risk averters and risk seekers: an analysis of stock preferences for USA and China ⋮ Did long-memory of liquidity signal the European sovereign debt crisis?

Cites Work

• The Pricing of Options and Corporate Liabilities
• Computing the implied volatility in stochastic volatility models
• Stochastic Process with Ultraslow Convergence to a Gaussian: The Truncated Lévy Flight
• Financial Modelling with Jump Processes
• More stylized facts of financial markets: leverage effect and downside correlations

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