Econ201FS- New Jump Tests - Duke University

Econ201FS- New Jump Tests - Duke University

Econ201FS- New Jump Test Haolan Cai Lee and Mykland (2007) Introduces a new Jump Test with New test statistic ()

Describes Asymptotic Behavior of Describes Misclassifications of Becomes negligible at high frequencies Tests

jump detection using MCMC Simulation Claims it detects jumps better than Barndoff-Nielsen and Shephard (2006) and Jiang and Oomen (2005) My Contribution Investigation of Jump Test Detection in

real data (GE and Intel) Comparison to existing BN-S tests at varying time intervals What is ? So basically, is the proportion of logged returns to the realized bipower variation over a given window size, K. Claim that using bipower variation for estimating instantaneous volatility is robust.

How is distributed? Ui is a standard normal variable which means its distributed with a mean of zero and variance of 1. C is a constant approximately equal to .7979.

What does K mean? Lee & Mykland (2007) Implementation Data:

1-minute prices from General Electric and Intel Corporation GE: April 9, 2007 9:35 am to Jan 24, 2008 3:59 pm INTC: April 16, 2007 9:35 am to Jan 24, 2008 3:59 pm Test Parameters:

K = 10 (for Lee & Mykland test) Use Quad-power Quarticity (for BN-S test) Time Intervals: 1-10 min. Significance Level: .01% (z-score = 3.09) Results- INTC (in days)

Barndoff-Nielsen & Shephard Lee & Mykland Results- INTC (in percentages) Barndoff-Nielsen & Shephard Lee & Mykland

Similar Results for GE Barndoff-Nielsen & Shephard Lee & Mykland Remarks Lee and Mykland test does seem to be

less affected by increase in time interval Consistently finds the same number/percentage of jump days Are these jumps real jumps or due to microstructure noise? Are the jumps being detected by the L & M test the same as the BN-S test?

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