Risk, Jumps, and Diversification

Abstract

We test for price discontinuities, or jumps, in a panel of high-frequency intraday stock returns and an equiweighted index constructed from the same stocks. Using a new test for common jumps that explicitly utilizes the cross-covariance structure in the returns to identify non-diversifiable jumps, we find strong evidence for many modest-sized, yet highly significant, cojumps that simply pass through standard jump detection statistics when applied on a stock-by-stock basis. Our results are further corroborated by a striking within-day pattern in the significant cojumps, with a sharp peak at the time of regularly scheduled macroeconomic news announcements. r

Jumps and betas: A new framework for disentangling and estimating systematic risks

“…Instead of attempting to adopt any of these procedures to the present context, here we simply follow most of the literature in the use of an intermediate sampling frequency as a way to strike a reasonable balance between the desire for as finely sampled prices as possible on the one hand and the desire not to overwhelm the measures by market microstructure effects on the other. While the magnitude and the impact of the ''noise'' obviously differs across stocks and across time, the analysis in Bollerslev et al (2008) suggests that a conservative sampling frequency of 22.5 min strikes such a balance and effectively mitigates the impact of the ''noise'' for all of the forty stocks in the sample. 10 10 For simplicity we decided to maintain the identical sampling frequency for all of of the stocks throughout the sample.…”

“…The name and ticker symbols for each of the individual stocks are given in the tables below. The same data has previously been analyzed by Bollerslev et al (2008) from a very different perspective, and we refer to the discussion therein for further details concerning the methods and filters employed in cleaning the raw price data.…”

Threshold bipower variation and the impact of jumps on volatility forecasting

“…A partial list of recent studies on this topic includes the test specification of Aït-Sahalia (2004), Jiang and Oomen (2008), Barndorff-Nielsen and Shephard (2006), Lee and Mykland (2008) and Aït-Sahalia and Jacod (2009), as well as the empirical studies of Bollerslev et al (2008), Maheu and McCurdy (2004), Bollerslev et al (2009), and Cartea and Karyampas (2010); non-parametric estimation in the presence of jumps, as in Bandi and Nguyen (2003), Johannes (2004) and Mancini and Renò (forthcoming); option pricing as in Duffie et al (2000), Eraker et al ✩ A previous version of this paper circulated with the title Volatility forecasting: the jumps do matter. All the code used for implementing threshold multipower variation and the C-Tz is available from the authors upon request.…”

ADAGE: a framework for supporting user-driven ad-hoc data analysis processes

“…High-frequency data has been widely used by finance researchers to study market behavior [29]. Studies have used high-frequency data to analyze many aspects of market microstructure including volatility and price jumps [30,31]. Therefore, highfrequency data is valuable and has been used in many empirical works related to financial markets research.…”