Data-Driven Jump Detection Thresholds for Application in Jump Regressions
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2015-09-17
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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.
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George E. Tauchen
George Tauchen is the William Henry Glasson Professor of Economics and professor of finance at the Fuqua School of Business. He joined the Duke faculty in 1977 after receiving his Ph.D. from the University of Minnesota. He did his undergraduate work at the University of Wisconsin. Professor Tauchen is a fellow of the Econometric Society, the American Statistical Association, the Journal of Econometrics, and the Society for Financial Econometrics (SoFie). He is also the 2003 Duke University Scholar/Teacher of the Year. Professor Tauchen is an internationally known time series econometrician. He has developed several important new techniques for making statistical inference from financial time series data and for testing models of financial markets. He has given invited lectures at many places around the world, including London, Paris, Beijing, Taipei, Hong Kong, and Sydney. His current research (with Professor Li of Duke) examines the impact of large jump-like moves in stock market returns on the returns of various portfolios and individual securities. He is a former editor of the Journal of Business and Economic Statistics (JBES) and former associate editor of Econometrica, Econometric Theory, The Journal of the American Statistical Association (JASA), and JBES. He is currently Co-Editor of the Journal of Financial Econometrics.
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