60 Pages Posted: 24 Aug 2005
Date Written: July 2005
We examine tests for jumps based on recent asymptotic results; we interpret the tests as Hausman-type tests. Monte Carlo evidence suggests that the daily ratio z-statistic has appropriate size, good power, and good jump detection capabilities revealed by the confusion matrix comprised of jump classification probabilities. We identify a pitfall in applying the asymptotic approximation over an entire sample. Theoretical and Monte Carlo analysis indicates that microstructure noise biases the tests against detecting jumps, and that a simple lagging strategy corrects the bias. Empirical work documents evidence for jumps that account for seven percent of stock market price variance.
Keywords: Realized variance, quadratic variation, bipower variation, stochastic volatility
JEL Classification: G12, C51, C52
Suggested Citation: Suggested Citation