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Senior Honors Thesis Proposal Peter Van Tassel Duke University Durham, North Carolina

Investigation of NYSE High Frequency Financial Data for Intraday Patterns in Jump Components of Equity Returns. Senior Honors Thesis Proposal Peter Van Tassel Duke University Durham, North Carolina 10 September 2007. Agenda.

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Senior Honors Thesis Proposal Peter Van Tassel Duke University Durham, North Carolina

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  1. Investigation of NYSE High Frequency Financial Data for Intraday Patterns in Jump Components ofEquity Returns Senior Honors Thesis Proposal Peter Van Tassel Duke University Durham, North Carolina 10 September 2007

  2. Agenda • Summarize research from Econ 201FS: Research Seminar and Lab on High Frequency Financial Data Analysis for Duke Economics Juniors • Motivation • Data • Non-parametric statistics: BNS and LM • Intraday patterns in Jump Components of Equity Returns • Moving forward, open discussion in regard to developing the current hypothesis Financial Econometrics Lunch Workshop, Duke University

  3. Motivation • Well documented U-shaped patterns in intraday equity return volatility dates back to Wood, McInish & Ord (1985) • Literature on FX volatility detects similar patterns, particularly in response to macroeconomic announcements, Engle et. al (1990), Chaboud, Chernenko, Howorka, Krishnasami, Liu, Wright (2004) • Our hypothesis from the background literature is that jump components of volatility are driven by new information and that more information will be released at the start of the trading day • Our results indicate that statistically significant jumps are concentrated around the start of the trading day Financial Econometrics Lunch Workshop, Duke University

  4. Data • High frequency data from TAQ • Arranged with the help of Tzuo Hann Law • Focus on the SPY as a proxy for the market portfolio • Will consider individual equities such as PEP, KO, BMY • Price series begins on 1 Jan 2001 and ends on 31 Dec 2005, with 771 observations per day and 1241 total days • Sampling frequency used for the purpose of this research is 17.5 minutes unless stated otherwise Financial Econometrics Lunch Workshop, Duke University

  5. SPY Level and Return Plots Financial Econometrics Lunch Workshop, Duke University

  6. The Lee-Mykland Statistic • Stock price evolution modeled as: • μ(t)dt drift term, σ(t)dt geometric Brownian motion, dJ(t) non-homogenous Poisson-type jump process • LM Statistic: Financial Econometrics Lunch Workshop, Duke University

  7. LM applied to SPY Data Financial Econometrics Lunch Workshop, Duke University

  8. LM Applied to Individual Stocks Financial Econometrics Lunch Workshop, Duke University

  9. Sampling Frequency • Admittedly, the LM statistic is not an authority in the non-parametric literature • One problem we find is stabilization at different sampling frequencies • We select a sampling frequency of 17.5 minutes in an attempt to alleviate problems related to market micro-structure noise Financial Econometrics Lunch Workshop, Duke University

  10. Realized and Bi-Power Variation: Alternate Approach and Similar Results • Model for stock price evolution: • μ(t)dt drift term, σ(t)dt geometric Brownian motion, dLj(t) pure jump Lévy process with increments • Returns defined as: • M is our within day sampling frequency Financial Econometrics Lunch Workshop, Duke University

  11. Realized and Bi-Power Variation • Realized Variation • Bi-power Variation Financial Econometrics Lunch Workshop, Duke University

  12. Intraday averages • Define intraday realized variation as, • Define intraday bi-power variation as, • Also consider, Financial Econometrics Lunch Workshop, Duke University

  13. Plots for SPY Data Financial Econometrics Lunch Workshop, Duke University

  14. Plots for Individual Stocks Financial Econometrics Lunch Workshop, Duke University

  15. BNS Statistics • Tri-power variation defined as, • Huang and Tauchen (2005) recommend, Financial Econometrics Lunch Workshop, Duke University

  16. More plots… Financial Econometrics Lunch Workshop, Duke University

  17. Conclusions • LM statistic indicates that jumps are more concentrated at the start of the trading day • Realized variation and Bi-power variation plots also indicate that the jump component is more prominent in the first 35 min of the trading day Future Work • Investigate whether the jump component measured early in the day is helpful in forecasting volatility, Financial Econometrics Lunch Workshop, Duke University

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