1 / 11

Sampling Frequency and Jump Detection Mike Schwert ECON201FS 2/27/08

Sampling Frequency and Jump Detection Mike Schwert ECON201FS 2/27/08. This Week’s Approach Last time: Counted jump days at various sampling frequencies Volatility signature plots for RV and BV This week: Semivariance calculations for GE price data

fiorello-lappin
Download Presentation

Sampling Frequency and Jump Detection Mike Schwert ECON201FS 2/27/08

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Sampling Frequency and Jump Detection Mike Schwert ECON201FS 2/27/08

  2. This Week’s Approach • Last time: • Counted jump days at various sampling frequencies • Volatility signature plots for RV and BV • This week: • Semivariance calculations for GE price data • Volatility signature plots for RJ, RS- and RS+ • Counted common jump days between different sampling frequencies • Correlation matrices for ZQP-max statistics at different sampling frequencies

  3. Realized Semivariance • Introduced by Barndorff-Nielsen, Kinnebrock, and Shephard • Separates positive and negative components of realized variance Summary Statistics – GE Price Data, 5-minute sampling frequency

  4. Volatility Signature Plots • Introduced by Andersen, Bollerslev, Diebold, and Labys (1999). • Calculate RJ, RS-, RS+ at 1, 2, …, 30 minute sampling frequencies • Plot relationship between sampling frequency and mean RJ, mean RS • RJ and RS are higher for high-frequency samples because returns are distorted by microstructure noise such as bid-ask bounce • Must be wary of using too low of a sampling frequency, as sampling variation will affect volatility calculations

  5. Volatility Signature Plot – Relative Jump

  6. Volatility Signature Plot – Negative Semivariance

  7. Volatility Signature Plot – Positive Semivariance

  8. Contingency and Correlation Matrices • Calculated ZQP-max statistics and counted jump days for GE, ExxonMobil, AT&T, and S&P 500 at 5, 10, 15 and 20 minute sampling frequencies • Counted common jump days between each sampling frequency and organized in contingency matrices • Surprisingly few common jump days exist between sampling frequencies • Calculation of jump days seems to depend a great deal on sampling choices • ZQP-max statistics are relatively uncorrelated between sampling frequencies • GE data minute-by-minute for 1997 – 2007 (2670 days) • ExxonMobil data minute-by-minute for 1999 – 2008 (2026 days) • AT&T data minute-by-minute for 1997 – 2008 (2680 days) • S&P data every 5 minutes, 1985 – 2007 (5545 days, excluding short days)

  9. Contingency Tables GE S&P 500 Exxon Mobil AT&T

  10. Correlation Matrices - Z-Statistics GE S&P 500 – N/A Exxon Mobil AT&T

  11. Possible Extensions • Try other jump test statistics to see if one outperforms the others in detecting jumps consistently between sampling frequencies • Regress z-statistics on changes in daily volume to see if days with high volume correspond to jump days, common jump days between samples • Other ways to formally analyze effects of sampling frequency on jump detection? • Any way to separate negative jumps from positive jumps? • Effect of sampling frequency on volatility forecasts?

More Related