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Intraday Volatility Patterns and Their Relation to Jump Arrivals in the High Frequency SPY Data

Intraday Volatility Patterns and Their Relation to Jump Arrivals in the High Frequency SPY Data. Peter Van Tassel 18 April 2007 Final Econ 201FS Presentation Duke University. Outline. Motivation Intuition Preliminary results

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Intraday Volatility Patterns and Their Relation to Jump Arrivals in the High Frequency SPY Data

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  1. Intraday Volatility Patterns and Their Relation to Jump Arrivals in the High Frequency SPY Data Peter Van Tassel 18 April 2007 Final Econ 201FS Presentation Duke University

  2. Outline • Motivation • Intuition • Preliminary results • Jumps in Financial Markets: A New Nonparametric Test and Jump Dynamics Suzanne S. Lee and Per A. Mykland • Extension to BNS Statistics • The Relative Contribution of Jumps to Total Price Variance Xin Huang and George Tauchen. The Journal of Financial Econometrics August 2005 • End of the semester and goals for the fall Patterns in Intraday Volatility

  3. Motivation • Use high frequency data from heavily traded stocks on the NYSE to improve our knowledge of how financial markets operate • Investigate “jump” components in financial asset prices • Implications for derivative valuation, risk measurement and management, asset allocation • Motivation for this presentation is to discuss “jump” arrival • How do so called jumps in heavily traded stocks affect patterns in daily volatility? • At what time do jumps arrive? • Is there a relation to information flow, volume, market microstructure noise? Patterns in Intraday Volatility

  4. Intuition • Well documented U-shaped pattern in return volatility over the day • An Investigation of Transactions Data for NYSE Stocks Wood, McInish, & Ord (1985), Harris (1986) • Public Information Arrival Thomas D. Berry and Keith M. Howe (1994) • Macroeconomic announcements: Ederington and Lee (1993), Chaboud, Chernenko, Howorka, Krishnasami, Liu, Wright (2004) • Large literature on fx volatility, Andersen, Bollerslev (1998) Engle et. al (1990) Hamao et. al (1990) Patterns in Intraday Volatility

  5. SPY Data • 17.5 minute prices were used to calculate SPY returns • Cleaned up data by removing returns greater (lower) than 1.5% followed by a return lower (greater) than -1.5% Patterns in Intraday Volatility

  6. The Lee Mykland Statistic • The adjustment term of pi/2 was multiplied by sigma to standardize the statistic. Patterns in Intraday Volatility

  7. Statistic Dynamics • Zaxis: Flagged jumps across sample • Yaxis: Time at NYSE • Xaxis: Window Size • 10am: Consumer Confidence, Factory Orders, ISM Index, New Existing Home Sales • ≈2:15pm: Federal Open Market Committee announcements Patterns in Intraday Volatility

  8. Different Perspectives Patterns in Intraday Volatility

  9. Particular Window Size • 17.5 minutes, K = 100 • 320 Flagged Jumps • 247 Different Days • ≈20% (2.9) of sample days • ≈1% (2.9) of the statistics flagged as significant • 20 Match with BNS Days at 17.5 Minutes out of 37 flagged by BNS 2001          10          12        2002           3          28        2002           5           1        2002           9          18        2002          10          24        2002          12          19        2003           5           6        2003          12          22        2004           1           6        2004           1           7        2004           1          29        2004           2           2        2004           3          24        2004           4           7        2004           9          21        2005           1          18        2005           7          22        2005           9          29        2005          11          28        2005          12          29 Patterns in Intraday Volatility

  10. RV vs. BV: Patterns in Daily Volatility Patterns in Intraday Volatility

  11. BNS: The Model • Dynamics of the model: • Returns: • Huang, Tauchen slide 4 Patterns in Intraday Volatility

  12. Tri-Power Statistic • Realized variance: • Realized bipower variation: • TP,t: • ZTP,t: Patterns in Intraday Volatility

  13. BNS Applications to Intraday Volatility Patterns in Intraday Volatility

  14. Lee Mykland: Volume and Volatility Patterns in Intraday Volatility

  15. Summary Results • The vast majority of jumps seem to be flagged in the morning, close to macroeconomic announcements at 10am • The difference between RV and BV seems to follow a U-shaped pattern, suggesting the jump component in RV is higher at the open and close than the middle hours of the trading day • Relationships between volume and flagged jumps seem less clear • Jumps arrive rarely and do not make a significant contribution to the daily pattern in volatility. One interpretation of this result could be that underlying market structure is influencing jump arrival and dynamics. Patterns in Intraday Volatility

  16. End of the Semester and Goals for the Fall • Spring Semester • Report current research • Summer • Get the full data set before classes end • Continue to explore the literature • Investigate the relationship between volume and flagged jumps • Implement more robust methods to support claims • Fall • Begin and complete writing of senior thesis Patterns in Intraday Volatility

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