G
George Tauchen
Researcher at Duke University
Publications - 139
Citations - 19742
George Tauchen is an academic researcher from Duke University. The author has contributed to research in topics: Stochastic volatility & Volatility (finance). The author has an hindex of 51, co-authored 138 publications receiving 18952 citations. Previous affiliations of George Tauchen include Northwestern University.
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Finite state markov-chain approximations to univariate and vector autoregressions
TL;DR: In this article, the authors developed a procedure for finding a discrete-valued Markov chain whose sample paths approximate well those of a vector autoregression, which has applications in economics, finance, and econometrics where approximate solutions to integral equations are required.
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The price variability-volume relationship on speculative markets
George Tauchen,Mark Pitts +1 more
TL;DR: In this article, the relationship between the variability of the daily price change and the daily volume of trading on the speculative markets was investigated and the results of the estimation can reconcile a conflict between the price variability-volume relationship for this market and the relationship obtained by previous investigators for other speculative markets.
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Stock Prices and Volume
TL;DR: In this paper, a comprehensive investigation of price and volume co-movement using daily New York Stock Exchange data from 1928 to 1987 is conducted, where the authors adjust the data to take into account well-known calendar effects and long-run trends.
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Expected Stock Returns and Variance Risk Premia
TL;DR: This article found that the difference between implied and realized variances, or the variance risk premium, is able to explain more than fifteen percent of the ex-post time series variation in quarterly excess returns on the market portfolio over the 1990 to 2005 sample period, with high premia predicting high (low) future returns.
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Alternative models for stock price dynamics
TL;DR: In this article, the role of various volatility specifications, such as multiple stochastic volatility (SV) factors and jump components, in appropriate modeling of equity return distributions is evaluated.