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Preliminary Data Analysis

This workshop report from Duke University (Feb 10, 2010) presents a preliminary analysis of financial data for Exxon (XOM), Google (GOOG), and Wal-Mart (WMT), encompassing multiple methodologies for volatility measurement from December 1999 to January 2009. We focus on log returns, outlier detection, and realized volatility, utilizing techniques like Z-scores and jump detection. Emphasis is placed on practical MATLAB applications, providing insights into volatility risks and potential areas for further research, such as the Volatility Risk Premium.

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Preliminary Data Analysis

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  1. Preliminary Data Analysis Angela Ryu Economics 201FS Honors Junior Workshop: Finance Duke University February 10, 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)

  3. Getting hands dirty… • Prices, Log Returns, Spotting Outliers • Realized Volatility • Bipower / Tripower / Quadpower Variation • Z-scores Huang-Tauchen (2005)

  4. vs. Prices XOM GOOG WMT

  5. vs. Log Returns (5 min)

  6. vs. RV (5 min, daily)

  7. vs. BV (5 min, daily)

  8. vs. RV & BV (5 min, daily)

  9. Volatility Signature Plot

  10. vs. TP (5 min, daily)

  11. vs. QP (5 min, daily)

  12. XOM: Jump Detection using Z-score (5 min)

  13. GOOG: Jump Detection using Z-score (5 min)

  14. WMT: Jump Detection using Z-score (5 min)

  15. GOOG: Kernel Density (5 min) • Mean: 0.7643 ; Variance: 1.8278 ( ~N(0,1)?)

  16. Results & Plans • Got more used to MATLAB • more efficient data analysis • Better understanding in volatility measures • Possible interests: RCOV, Volatility Risk Premium • Background Reading

  17. Comments & Suggestions

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