Identi cation of jumps in nancial price series

The paper outlines and tests, by means of Monte-Carlo simulations, a simple strategy of using existing non-parametric tests for jumps at the daily frequency to identify jumps at higher sampling frequencies. The suggested strategy allow for identi cation of the number of jumps and jump times during a day, as well as, the size and direction (negative or positive) of the jumps. The method is of importance in order to facilitate detailed empirical studies concerning, for example, causes for jumps in nancial price series at ner levels than the daily. The Monte Carlo study reveals that the strategy works reasonably well, particular for lower jump intensities. An application of the studied strategy on the Handelsbanken stock is provided. Keywords: Financial econometrics, jumps, realized variance, bipower variation, stock price. JEL: C14, C15, G12. Financial support from the Wallander Foundation is greatly appreciated by both authors. Comments and suggestions by Kurt Brännäs is greatly acknowledged. 1 Introduction In this paper we study a simple strategy to identify price jumps at ultra-high frequency, e.g. at the second, one or ve minute level. The suggested approach allow for identi cation of the number of jumps and jump times during a day, as well as, the size and direction (negative or positive) of these jumps. In contrast to Lee and Mykland (2008), who consider the similar questions by a new non-parametric test, the considered approach in this paper builds on repeated use of existing non-parametric tests for the presence of jumps in high frequency nancial data (Barndor¤-Nielsen and Shephard, 2004b, 2006). The advent of high-frequency nancial databases has opened new empirical possibilities to study the ner microstructure of nancial markets. A natural question is what type of ( nancial) news that causes nancial prices to jump. In order to draw de nite conclusions regarding what type of news (e.g. rm speci c, industry speci c or macro news) that can cause price jumps, news have to be linked to jumps in asset prices at a ner frequency than at the daily level. A drawback with the mentioned non-parametric tests is that the realized variance and bipower variation, the measures upon which the non-parametric tests are constructed, require a su¢ cient number of inner intervals in nite sample calculations. This means that using the tests at ner levels than the daily will require inner intervals smaller than ve minutes. It is however well known, both theoretically as well as through Monte Carlo evidence, that the tests are biased against detecting jumps due to microstructure noise (spurious serial correlation caused by various market microstructure e¤ects including nonsynchronous trading, discrete price observations, intraday periodic volatility patterns and bidask bounce) when the inner intervals become smaller than ve minutes. Also, since nite sample calculations of the tests require a su¢ cient number of inner intervals they may never be directly used at the highest sampling levels, e.g. at the minute level. Many empirical applications in the literature (e.g. Barndor¤-Nielsen and Shephard, 2004a) use an approach testing for jumps at the daily level and then conditional on nding jumps visually inspecting the "jump-day" for abnormal returns. Usually the largest absolute return is considered to be a jump then identi ed at a ner frequency, e.g. at the ve minute level using ve minute returns. A problem with this strategy is if there are several possible potential