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Category Archives: George Tauchen

Assessing the Effects of Earnings Surprise on Returns and Volatility with High Frequency Data

By Sam Lim

This paper aims to explore how “earnings surprise”—the difference between earnings
estimates and the actual announced earnings—affects a stock’s volatility and returns using
high frequency data. The results show that earnings surprise is significantly correlated with volatility and overnight returns. Furthermore, an earnings surprise is significantly correlated with an increase in volatility in the trading period immediately following the earnings announcement, but there is no bias indicating which directions prices will go. Even with no “surprise”, the announcement tends to be followed by this increase in volatility. The findings suggest the importance of earnings on equity price valuation.

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Advisor: George Tauchen, Tim Bollerslev

An Investigation into the Interdependency of the Volatility of Technology Stocks

By Zoraver Lamba

This paper examines the contemporaneous and dynamic relationships between the volatilities of the technology stocks in the S&P 100 index. Factor analysis and heterogeneous autoregressive regressions are used to examine contemporaneous and dynamic, inter-temporal relationships, respectively. Both techniques utilize high frequency data by measuring stock prices every 5 minutes from 1997-2008. We find that a strong industry effect explains the bulk of the volatility of the technology stocks and that the market’s volatility has very low correlation with the stocks’ volatility. Further, we find the market’s volatility has insignificant predictive content for the stocks’ volatility. The stocks themselves contain large quantities of unique predictive content for each other’s volatilities.

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Advisor: George Tauchen

Testing the Relationship between Oil Equities and Oil Futures with High-Frequency Data: A Look at Returns, Jumps, and Volatility

By Brian Jansen

This paper looks at simultaneous returns, jumps, and volatilities of oil futures, oil equities, and other equities in the S&P 100 using high-frequency data. Through this method, a market factor is found to affect the overall level of returns across the equities and the likelihood that two given equities to jump simultaneously. A second factor is found to affect the returns and jumps that uniquely describes the variation in the oil equity and futures data. Volatility in oil futures and equities is not found to have a common factor due to the differences in types and motivations of traders.

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Advisor: George Tauchen

Hop, Skip and Jump – What Are Modern “Jump” Tests Finding in Stock Returns?

By Michael Schwert

This paper applies several jump detection tests to intraday stock price data sampled at various frequencies. It finds that the choice of sampling frequency has an effect on both the amount of jumps detected by these tests, as well as the timing of those jumps. Furthermore, although these tests are designed to identify the same phenomenon, they find different amounts and timing of jumps when performed on the same data. These results suggest that these jump detection tests are probably identifying different types of jump behavior in stock price data, so they are not really substitutes for one another.

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Advisor: George Tauchen

The Impact of Sector and Market Variance on Individual Equity Variance

By Haoming Wang

This paper investigates how changes in measures of sector and market variance affect equity variance by examining forecasts of equity variance over 1, 5, and 22 day time horizons. These forecasts were generated using heterogeneous autoregressive regressions that included measures of sector and market variance. The results demonstrate that sector and market variance both play an important role in determining equity variance. Further, the inclusion of measures of sector and market variance improves goodness of fit and decreases forecasting errors. These results imply that the inclusion of these measures could improve predictive models of equity variance.

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Advisor: George Tauchen

Analyzing and Applying Existing and New Jump Detection Methods for Intraday Stock Data

By William Warren Davis

This paper attempts to explore two recent statistics used to identify jumps in stock prices, as well as to propose a modification to one of the statistics to increase its accuracy by adding a second stage with a different estimator of local volatility. After identifying potential jump days, a study of Bristol-Myers Squibb Co. stock was performed, identifying the types of company-specific events that occurred on these days that seemed to cause jumps in the price. Also, the new proposed statistic was found to be more accurate by a using method of changing the significance levels used in each stage, as well as in samples with an extremely high jump frequency.

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Advisor: George Tauchen

Patterns Within the Trading Day: Volatility and Jump Discontinuities in High Frequency Equity Price Series

by Peer Van Tassel

This paper identities systematic patterns within the trading day by analyzing high frequency data from a market index and nine individual stocks. Empirical results expand on the previously documented U-shape in intraday equity volatility by implementing non-parametric statistics to test for patterns in the jump and diffusive components of volatility. Additional results indicate that a recently developed non-parametric jump detection scheme may under-report the number of returns flagged as statistically significant jumps in the middle of the day while exaggerating the number of statistically significant jumps in the early morning and late afternoon. The paper concludes by investigating whether incorporating the observed patterns into a historical forecasting model can improve performance.

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Advisor: George Tauchen

The Informational Content of Implied Volatility in Individual Stocks and the Market

By Andrey Fradkin

We examine the informational content of historical and implied measures of variance through an evaluation of forecasts over horizons ranging from 1 to 22 days. These forecasts use heterogeneous autoregressive (HAR) regressions which are constructed with high-frequency data. Our results show that the t and forecasting ability of models based on historical realized variance (RV) increases with the addition of implied volatility in the regression model. We find that robust regression is better than OLS in forecasting RV outside of the estimation sample. The paper evaluates data from individual equities and the S&P 500.

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Advisor: George Tauchen

Questions?

Undergraduate Program Assistant
Jennifer Becker
dus_asst@econ.duke.edu

Director of the Honors Program
Michelle P. Connolly
michelle.connolly@duke.edu