Measuring Downside Risk Using High-Frequency Data: Realized Downside Risk Measure

DOI10.1080/03610918.2012.655826zbMath1347.62223OpenAlexW2169034381MaRDI QIDQ4921595

Huishan Wu, Tao Bi, Bo Zhang

Publication date: 13 May 2013

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2012.655826

zbMATH Keywords

central limit theoremhigh-frequency datasemimartingaledownside risk

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Statistical methods; risk measures (91G70) Statistics of extreme values; tail inference (62G32) Probabilistic models, generic numerical methods in probability and statistics (65C20)

Related Items (1)

Intraday Serial Correlation,Volatility, and Jump: Evidence from China's Stock Market

Cites Work

• A note on the central limit theorem for bipower variation of general functions
• Microstructure noise in the continuous case: the pre-averaging approach
• Optimal portfolio of safety-first models
• Risky asset pricing based on safety first fund management
• Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise
• The Distribution of Realized Exchange Rate Volatility
• A Tale of Two Time Scales
• Safety First and the Holding of Assets

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