Empirical Description of Markets Around Shocks

A broad range of research works tries to give an empirical description for price jumps and analyze their statistical properties and the behaviour of market quantities around such events.

The attempt to compare shocks on different time scales is relatively little explored. Fan and Wang (2007), for example, used wavelets to identify jumps on multiple time scales, but the method is not used to compare shocks on different scales, but to detect shocks using a multiscale tool. On the other hand the attempt to investigate shocks and pre – and aftershock market behaviour is not novel. Lillo and Mantegna studied the relaxation dynamics of the occurrence of large volatility after volatility shocks (Lillo and Mantegna 2004), Zawadowski et al (2004) examined the evolution of price, volatility and the bid-ask spread after extreme 15 min intraday price changes on the New York Stock Exchange and the NASDAQ, Ponzi et al. (2009) studied possible market strategies around large events and they found that the bid-ask spread and the mid-price decay very slowly to the normal values when conditioned to a sudden variation of the spread. Sornette found that the implied variance of the Standard and Poor’s 500 Index after the Black Monday decays as a power law with log-periodic oscillations (Sornette et al. 1996).

Mu et al. (2010) study the dynamics of order flows around large intraday price changes using ultra-high-frequency data from the Shenzhen Stock Exchange. They find a significant reversal of price for both intraday price decreases and increases with a permanent price impact. The volatility, the volume of different types of orders, the bid-ask spread, and the volume imbalance increase before the extreme events and decay slowly as a power law, which forms a well established peak. They also study the relative rates of different types of orders and find differences in the dynamics of relative rates between buy orders and sell orders and between individual investors and institutional investors. There is evidence showing that institutions behave very differently from individuals and that they have more aggressive strategies. Combing these findings, they conclude that institutional investors are more informed and play a more influential role in driving large price fluctuations.

Novotny (2010) tries to determine if there is any increase in market volatility and any change in the behaviour of price jumps during the recent financial crisis. He employs data on 16 highly traded stocks and one Exchange Traded Fund (ETF) from the North American exchanges found in the TAQ database from January 2008 to July 2009. It was found that the overall volatility significantly increased in September 2008 when Lehman Brothers filed for bankruptcy protection, the periods immediately after this announcement reveal significantly higher levels of volatility. However, the ratio between the regular noise and price jump components of volatility does not change significantly during the crisis. The results suggest individual cases where the ratio increases as well as decreases.

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