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Beta Estimate of High Frequency Data

Beta Estimate of High Frequency Data. Angela Ryu Economics 201FS Honors Junior Workshop: Finance Duke University March 3, 2010. Data. XOM (Exxon Mobile) Dec 1 1999 – Jan 7 2009 (2264 days) GOOG (Google) Aug 20 2004 – Jan 7 2009 (1093 days) WMT (Wal-Mart)

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Beta Estimate of High Frequency Data

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  1. Beta Estimate of High Frequency Data Angela Ryu Economics 201FS Honors Junior Workshop: Finance Duke University March 3, 2010

  2. Data • XOM (Exxon Mobile) • Dec 1 1999 – Jan 7 2009 (2264 days) • GOOG (Google) • Aug 20 2004 – Jan 7 2009 (1093 days) • WMT (Wal-Mart) • Apr 9 1997 – Jan 7 2009 (2921 days)  FOR ALL 3 stocks

  3. Motivation • Multivariate Measures: Beta • Problem of balancing bias/precision • High frequency sampling: biased, due to microstructure noise • Low frequency sampling: imprecise • Theoretical approach requires more background knowledge  approach empirically!

  4. Preparation • Interday returns are excluded • Beta calculated from: (for βX = Y, X,Y stock prices) • Sampling intervals: 1 to 20 minutes • Beta Calculation intervals: 1 to 50 days • Mean Squared Error calculated for each Beta interval • MSE of GOOG(X) vs. XOM(Y) , 30 days interval?= Average of Squared Errors of each days predicted by using β i.e. ypre_day31 = βday1_30 * xact_day31 SEday31 = (ypre_day31 – yact_day31 )2 ypre_day32 = βday2_31 * xact_day32  SEday32 = (ypre_day32 – yact_day32 )2 … MSE30 = avg(SEday31 ,SEday32 , ... SEday1093 )

  5. WMT vs. XOM (2 min.)

  6. WMT vs. XOM (5 min.)

  7. WMT vs. XOM (10 min.)

  8. WMT vs. XOM (15 min.)

  9. WMT vs. XOM (20 min.)

  10. XOM vs. WMT (2 min.)

  11. XOM vs. WMT (5 min.)

  12. XOM vs. WMT (10 min.)

  13. XOM vs. WMT (15 min.)

  14. XOM vs. WMT (20 min.)

  15. GOOG vs. WMT (2 min.)

  16. GOOG vs. WMT (5 min.)

  17. GOOG vs. WMT (10 min)

  18. GOOG vs. WMT (15 min.)

  19. GOOG vs. WMT (20 min.)

  20. Results • 5 – 15 days interval for Beta gave least MSE for many stock pairs, for most sampling intervals • As the sampling interval increased, MSE for shorter Beta intervals increased rapidly • For 20 min. sampling interval, there is less increase of MSE as increase in Beta interval compared to shorter sampling intervals

  21. Analysis • Against our intuition: why would more information harm prediction of the price? • Possible interpretation • Given a sampling interval, after a certain range of “information” gather for Beta estimation, say 5 – 15 days, more information distorts the prediction • On the other hand, some short Beta intervals (e.g. 1 day, 2 days) for longer sampling intervals may be insufficient and result in high MSE

  22. Questions & Further Steps • Theoretical evidence? Any relevant papers? • Is the estimator biased? Why? • What is the role of Microstructure noise? • Check calculations. Try with other stocks or possibly portfolios (industry/macroeconomic factors) • Use Realized Beta and compare the results Andersen, Bollerslev, Diebold and Wu (2003)

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