摘要：In this short paper, I selectively review some recent developments related to the idea that jumps in stock prices incorporate the most valuable information, and thus the quantification of a stock’s exposure to jump events is important for financial risk management and portfolio construction. There are two main methodologies of estimating jump betas: a) the more widely used high or ultra high frequency procedures that rely on the asymptotical behavior of elaborate and sophisticated econometric constructs, such as the bi-power variation or local averaging techniques in order to isolate market microstructure noise at high frequencies, and b) very recently a new non-parametric skew-based methodology that does not rely on the use of high frequency data and is thus immune to market microstructure noise.
关键词：Stock beta; Price jump; Noise trading; Information in stock prices; Return skew; Market co-skew;Information-loading
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