George Tauchen | Duke University (original) (raw)

Papers by George Tauchen

Journal of Econometrics, 2022

We consider specification and inference for the stochastic scale of discretely-observed pure-jump... more We consider specification and inference for the stochastic scale of discretely-observed pure-jump semimartingales with locally stable Lévy densities in the setting where both the time span of the data set increases, and the mesh of the observation grid decreases. The estimation is based on constructing a nonparametric estimate for the empirical Laplace transform of the stochastic scale over a given interval of time by aggregating high-frequency increments of the observed process on that time interval into a statistic we call realized Laplace transform. The realized Laplace transform depends on the activity of the driving pure-jump martingale, and we consider both cases when the latter is known or has to be inferred from the data.

We derive a nonparametric test for constant beta over a fixed time interval from high-frequency o... more We derive a nonparametric test for constant beta over a fixed time interval from high-frequency observations of a bivariate \Ito semimartingale. Beta is defined as the ratio of the spot continuous covariation between an asset and a risk factor and the spot continuous variation of the latter. The test is based on the asymptotic behavior of the covariation between the risk factor and an estimate of the residual component of the asset, that is orthogonal (in martingale sense) to the risk factor, over blocks with asymptotically shrinking time span. Rate optimality of the test over smoothness classes is derived.

The copyright to this Article is held by the Econometric Society. It may be downloaded, printed a... more The copyright to this Article is held by the Econometric Society. It may be downloaded, printed and reproduced only for educational or research purposes, including use in course packs. No downloading or copying may be done for any commercial purpose without the explicit permission of the Econometric Society. For such commercial purposes contact the Office of the Econometric Society (contact information may be found at the website

JEL No. G0,G00,G1,G10,G12 The paper estimates and examines the empirical plausibiltiy of asset pr... more JEL No. G0,G00,G1,G10,G12 The paper estimates and examines the empirical plausibiltiy of asset pricing models that attempt to explain features of financial markets such as the size of the equity premium and the volatility of the stock market. In one model, the long run risks model of Bansal and Yaron (2004), low frequency movements and time varying uncertainty in aggregate consumption growth are the key channels for understanding asset prices. In another, as typified by Campbell and Cochrane (1999), habit formation, which generates time-varying risk-aversion and consequently time-variation in risk-premia, is the key channel. These models are fitted to data using simulation estimators. Both models are found to fit the data equally well at conventional significance levels, and they can track quite closely a new measure of realized annual volatility. Further scrutiny using a rich array of diagnostics suggests that the long run risk model is preferred.

This online supplement contains all proofs for the results in the main text. ∗Department of Econo... more This online supplement contains all proofs for the results in the main text. ∗Department of Economics, Duke University, Durham, NC 27708; e-mail: jl410@duke.edu. †Department of Finance, Kellogg School of Management, Northwestern University, Evanston, IL 60208; e-mail: v-todorov@northwestern.edu. ‡Department of Economics, Duke University, Durham, NC 27708; e-mail: george.tauchen@duke.edu.

We developed a model-free Bayesian extraction procedure for the stochastic discount factor under ... more We developed a model-free Bayesian extraction procedure for the stochastic discount factor under a yield curve prior. Previous methods in the literature directly or indirectly use some particular parametric asset-pricing models such as with long-run risks or habits as the prior. Here, in contrast, we used no such model, but rather, we adopted a prior that enforces external information about the historically very low levels of U.S. short- and long-term interest rates. For clarity and simplicity, our data were annual time series. We used the extracted stochastic discount factor to determine the stripped cash flow risk premiums on a panel of industrial profits and consumption. Interestingly, the results align very closely with recent limited information (bounded rationality) models of the term structure of equity risk premiums, although nowhere did we use any theory on the discount factor other than its implied moment restrictions.

This document contains three supplemental appendices for the main text. Supplemental Appendix A p... more This document contains three supplemental appendices for the main text. Supplemental Appendix A presents additional theoretical results. Supplemental Appendix B contains some numerical analysis for the econometric procedures proposed in the main text. Supplemental Appendix C contains all proofs. JEL classification: C51, C52, G12. ∗Department of Economics, Duke University, Durham, NC 27708; e-mail: jl410@duke.edu. †Department of Finance, Kellogg School of Management, Northwestern University, Evanston, IL 60208; e-mail: v-todorov@northwestern.edu. ‡Department of Economics, Duke University, Durham, NC 27708; e-mail: george.tauchen@duke.edu. Supplemental Appendix A: Additional theoretical results SA.1 Inference when some jumps arrive at deterministic times In this subsection, we extend the results in the main text to a setting where a subset of jump times can be identified using prior information. Examples of such jump events are the ones caused by pre-scheduled macro announcements (And...

We propose nonparametric estimators of the occupation measure and its density of the diffusion co... more We propose nonparametric estimators of the occupation measure and its density of the diffusion coefficient (stochastic volatility) of a discretely observed Itô semimartingale on a fixed interval when the mesh of the observation grid shrinks to zero asymptotically. In a first step we estimate the volatility locally over blocks of shrinking length and then in a second step we use these estimates to construct a sample analogue of the volatility occupation time and a kernel-based estimator of its density. We prove the consistency of our estimators and further derive bounds for their rates of convergence. We use these results to estimate nonparametrically the quantiles associated with the volatility occupation measure. Annals of Statistics: forthcoming.

We propose using the price range, a recently-neglected volatility proxy with a long history in fi... more We propose using the price range, a recently-neglected volatility proxy with a long history in finance, in the estimation of stochastic volatility models. We show both theoretically and empirically that the log range is approximately Gaussian, in sharp contrast to popular volatility proxies, such as log absolute or squared returns. Hence Gaussian quasi-maximum likelihood estimation based on the range is not only simple, but also highly efficient. We illustrate and enrich our theoretical results with a Monte Carlo study and a substantive empirical application to daily exchange rate volatility. Our empirical work produces sharp conclusions. In particular, the evidence points strongly to the inadequacy of one-factor volatility models, favoring instead two-factor models with one highly persistent factor and one quickly mean reverting factor. Acknowledgments: This work was supported by the National Science Foundation. Siem Koopman graciously shared both his wisdom and his Ox routines. We...

This web-based appendix contains additional empirical results, robustness checks, and calibration... more This web-based appendix contains additional empirical results, robustness checks, and calibrations that compliment the findings reported in the paper. ∗Department of Economics, Duke University, Durham, NC 27708, and NBER and CREATES, Email: boller@duke.edu. †Department of Economics, Rice University, Houston, TX 77251, Email: natalia.sizova@rice.edu. ‡Department of Economics, Duke University, Durham, NC 27708, Email: george.tauchen@duke.edu. 1 Impulse Response Analysis A reviewer noted that “.. a way to think of the left-side of the cross-correlation diagram between returns and volatility is as an impulse-response function (with time running from right to left).” We agree. To further elaborate on this point, Figure A1 shows the observed orthogonalized VAR impulse response functions for the the variance risk premium and the VIX, respectively, to a one standard deviation shock in the return in a system comprised of the return, the variance premium, and the VIX, in that order. Figure A2...

Journal of Econometrics, 2018

Quantitative Economics, 2019

We develop tests for deciding whether a large cross‐section of asset prices obey an exact factor ... more We develop tests for deciding whether a large cross‐section of asset prices obey an exact factor structure at the times of factor jumps. Such jump dependence is implied by standard linear factor models. Our inference is based on a panel of asset returns with asymptotically increasing cross‐sectional dimension and sampling frequency, and essentially no restriction on the relative magnitude of these two dimensions of the panel. The test is formed from the high‐frequency returns at the times when the risk factors are detected to have a jump. The test statistic is a cross‐sectional average of a measure of discrepancy in the estimated jump factor loadings of the assets at consecutive jump times. Under the null hypothesis, the discrepancy in the factor loadings is due to a measurement error, which shrinks with the increase of the sampling frequency, while under an alternative of a noisy jump factor model this discrepancy contains also nonvanishing firm‐specific shocks. The limit behavior ...

SSRN Electronic Journal, 2015

SSRN Electronic Journal, 2012

Journal of Business & Economic Statistics, 2017

Journal of Econometrics, 2017

Journal of the American Statistical Association, 2017