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Showing papers by "George Tauchen published in 2005"


Journal ArticleDOI
TL;DR: In this paper, the authors examine tests for jumps based on recent asymptotic results; they interpret the tests as Hausman-type tests and find that microstructure noise biases the tests against detecting jumps, and a simple lagging strategy corrects the bias.
Abstract: 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.

782 citations


Journal ArticleDOI
TL;DR: In this paper, the authors examine tests for jumps based on recent asymptotic results; they interpret the tests as Hausman-type tests, and they identify a pitfall in applying the approximation over an entire sample.
Abstract: 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 7% of stock market price variance. Copyright 2005, Oxford University Press.

663 citations


Journal ArticleDOI
TL;DR: In this paper, the authors examined the relationship between volatility and past and future returns in high-frequency equity market data and found that the correlations between absolute highfrequency returns and current and past high frequency returns to be negative for several days, while the reverse cross-correlations between absolute returns and future return are generally negligible.
Abstract: We examine the relationship between volatility and past and future returns in high-frequency equity market data Consistent with a prolonged leverage effect, we find the correlations between absolute high-frequency returns and current and past high-frequency returns to be significantly negative for several days, while the reverse cross-correlations between absolute returns and future returns are generally negligible Based on a simple aggregation formula, we demonstrate how the high-frequency data may similarly be used in more effectively assessing volatility asymmetries over longer daily return horizons Motivated by the striking cross-correlation patterns uncovered in the high-frequency data, we investigate the ability of some popular continuous-time stochastic volatility models for explaining the observed asymmetries Our results clearly highlight the importance of allowing for multiple latent volatility factors at very fine time scales in order to adequately describe and understand the patterns in the data

44 citations


01 Jan 2005
TL;DR: In this article, a jump detection method based on bi-power variation and swap variance measures is proposed to identify realized jumps on financial markets and to estimate parametrically the jump intensity, mean, and variance.
Abstract: This paper extends the jump detection method based on bi-power variation and swap variance measures to identify realized jumps on financial markets and to estimate parametrically the jump intensity, mean, and variance. Such an approach does not require specifying and estimating the underlying drift and diusion functions. Finite sample evidence suggests that the jump parameters can be accurately estimated and that the statistical inferences can be reliable relative to the maximum likelihood estimation, under the appropriate choice of jump detection test level and assuming that jumps are rare and large. The bi-power variation approach performs slightly better than the swap variance approach when the jump contribution to total variance is small. Applications to equity market, treasury bond, individual stock, and exchange rate reveal important dierences in jump frequencies and volatilities across asset classes over time. For high investment grade credit spread indices, the estimated jump volatility has a better forecasting power than interest rate factors, volatility factors including option-implied volatility, and Fama-French risk factors.

41 citations


Journal ArticleDOI
TL;DR: This paper developed an empirically highly accurate discrete-time daily stochastic volatility model that explicitly distinguishes between the jump and continuous time components of price movements using nonparametric realized variation and Bipower variation measures constructed from high-frequency intraday data.
Abstract: We develop an empirically highly accurate discrete-time daily stochastic volatility model that explicitly distinguishes between the jump and continuous time components of price movements using nonparametric realized variation and Bipower variation measures constructed from high-frequency intraday data. The model setup allows us to directly assess the structural inter-dependencies among the shocks to returns and the two different volatility components. The model estimates suggest that the leverage effect, or asymmetry between returns and volatility, works primarily through the continuous volatility component. The excellent fit of the model makes it an ideal candidate for an easy-to-implement auxiliary model in the context of indirect estimation of empirically more realistic continuous-time jump diffusion and Levy-driven stochastic volatility models, effectively incorporating the interdaily dependencies inherent in the high-frequency intraday data.

18 citations