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. Patterns in Intraday Volatility
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
18 April 2007
Patterns in Intraday Volatility

3. Patterns in Intraday Volatility
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. Patterns in Intraday Volatility
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)
Data includes all news releases sent by Reuter’s News Service over their North American Securities News wire form May 1990 to April The High-Frequency Effects of US Macroeconomic Data Releases on Prices and Trading Activity in the Global Interdealer Foreign Exchange Market November 2004, The Federal Reserve Board, International Finance Discussion Papers, Chaboud, Chernenko, Howorka, Krishnasami, Liu, Wright
18 April 2007
Patterns in Intraday Volatility

5. Patterns in Intraday Volatility
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%
18 April 2007
Patterns in Intraday Volatility

6. The Lee Mykland Statistic
The adjustment term of pi/2 was multiplied by sigma to standardize the statistic.
18 April 2007
Patterns in Intraday Volatility

7. Patterns in Intraday Volatility
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
18 April 2007
Patterns in Intraday Volatility

8. 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
18 April 2007
Patterns in Intraday Volatility

10. RV vs. BV: Patterns in Daily Volatility
RV and Bipower were averaged across sample to produce these graphs. The anomalous effects in the middle of the trading day are due to outliers.
18 April 2007
Patterns in Intraday Volatility

11. Patterns in Intraday Volatility
BNS: The Model
Dynamics of the model:
Returns:
Huang, Tauchen slide 4
18 April 2007
Patterns in Intraday Volatility

12. Patterns in Intraday Volatility
Tri-Power Statistic
Realized variance:
Realized bipower variation:
TP,t:
ZTP,t:
18 April 2007
Patterns in Intraday Volatility

13. BNS Applications to Intraday Volatility
18 April 2007
Patterns in Intraday Volatility

14. Lee Mykland: Volume and Volatility
Here the RVs were summed and divided by the number of jumps that correspond. The one figure on the bottom left is the example where I didn’t divide by the number of hits. Column sums to 322, the number of jumps. Volume guy looks at volumes across days that were flagged as jumps.
18 April 2007
Patterns in Intraday Volatility

15. Patterns in Intraday Volatility
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.
18 April 2007
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
18 April 2007
Patterns in Intraday Volatility