Showing papers by "George Tauchen published in 2015"

Efficient Indirect Bayesian Estimation: With Application to the Dominant Generalized Blumenthal-Getoor Index of an Ito Semimartingale

Abstract: We use the Gallant and Tauchen EMM method to estimate the generalized Blumenthal-Getoor index of S&P 500 index options and futures over the period January 3, 2007, to March 22, 2011 using the Andersen et al. (2013) data set. The options extend over a wide range of moneyness, including deep out of the money puts. As explained by high frequency financial econometrics, the semi-nonparametric density estimates of pre-averaged 5-minute returns on options are are strongly consistent with a pure jump model with β-stable jumps, 1 < β < 2. When fitted via EMM the β-stable gives estimates close to 1 (Cauchy) for deep out of the money options and estimates around 1.80 for at the money. The pattern is consistent with earlier evidence, but our estimates suggest a more important than previously thought role for jump-like behavior and diminished, if not absent, diffusive component. One anomaly is that the S&P futures price also appears to be pure jump, contrary to several other studies of highly liquid aggregate indices. The results are thus specific to the data set and need to be considered in light of the heavy turbulence in financial markets over the sample period. Nonetheless, our results in combination with other recent studies reveal the importance of considering pure jump models with many very small or moderate jumps, plus rare jumps, instead of the classic continuous model comprised of a continuous diffusion plus rate jumps.

Data-Driven Jump Detection Thresholds for Application in Jump Regressions

Abstract: This paper develops a method to select the threshold in threshold-based jump detection methods. The method is motivated by an analysis of threshold-based jump detection methods in the context of jump-diffusion models. We show that over the range of sampling frequencies a researcher is most likely to encounter that the usual in-fill asymptotics provide a poor guide for selecting the jump threshold. Because of this we develop a sample-based method. Our method estimates the number of jumps over a grid of thresholds and selects the optimal threshold at what we term the “take-off” point in the estimated number of jumps. We show that this method consistently estimates the jumps and their indices as the sampling interval goes to zero. In several Monte Carlo studies we evaluate the performance of our method based on its ability to accurately locate jumps and its ability to distinguish between true jumps and large diffusive moves. In one of these Monte Carlo studies we evaluate the performance of our method in a jump regression context. Finally, we apply our method in two empirical studies. In one we estimate the number of jumps and report the jump threshold our method selects for three commonly used market indices. In the other empirical application we perform a series of jump regressions using our method to select the jump threshold.