intraday volatility patterns and their relation to jump arrivals in the high frequency spy data
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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 - PowerPoint PPT PresentationTRANSCRIPT
Intraday Volatility Patterns and Their Relation to Jump Arrivals in the High Frequency SPY Data
Peter Van Tassel 18 April 2007Final Econ 201FS Presentation Duke University
18 April 2007 Patterns in Intraday Volatility 2
Outline1. Motivation
2. Intuition
3. Preliminary results • Jumps in Financial Markets: A New Nonparametric Test and Jump
Dynamics Suzanne S. Lee and Per A. Mykland
4. 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
5. End of the semester and goals for the fall
18 April 2007 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?
18 April 2007 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)
18 April 2007 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%
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The Lee Mykland Statistic
• The adjustment term of pi/2 was multiplied by sigma to standardize the statistic.
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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
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Different Perspectives
18 April 2007 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
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RV vs. BV: Patterns in Daily Volatility
18 April 2007 Patterns in Intraday Volatility 11
BNS: The Model
• Dynamics of the model:
• Returns:
Huang, Tauchen slide 4
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Tri-Power Statistic•Realized variance:
•Realized bipower variation:
•TP,t:
•ZTP,t:
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BNS Applications to Intraday Volatility
18 April 2007 Patterns in Intraday Volatility 14
Lee Mykland: Volume and Volatility1 272 493 264 185 76 137 98 59 410 411 312 213 514 515 616 1517 2118 1519 2420 1721 3122 14
18 April 2007 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.
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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