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The Information Content Of Treasury Bond Options Concerning Future Volatility And Price Jumps

Author

Listed:
  • Bent Jesper Christensen

    (University of Aarhus and CREATES)

  • Morten Ø. Nielsen

    (Queen's University and CREATES)

  • Thomas Busch

    (Danske Bank and CREATES)

Abstract

We study the relation between realized and implied volatility in the bond market. Realized volatility is constructed from high-frequency (5-minute) returns on 30 year Treasury bond futures. Implied volatility is backed out from prices of associated bond options. Recent nonparametric statistical techniques are used to separate realized volatility into its continuous sample path and jump components, thus enhancing forecasting performance. We generalize the heterogeneous autoregressive (HAR) model to include implied volatility as an additional regressor, and to the separate forecasting of the realized components. We also introduce a new vector HAR (VecHAR) model for the resulting simultaneous system, controlling for possible endogeneity of implied volatility in the forecasting equations. We show that implied volatility is a biased and inefficient forecast in the bond market. However, implied volatility does contain incremental information about future volatility relative to both components of realized volatility, and even subsumes the information content of daily and weekly return based measures. Perhaps surprisingly, the jump component of realized bond return volatility is, to some extent, predictable, and bond options appear to be calibrated to incorporate information about future jumps in Treasury bond prices, and hence interest rates.

Suggested Citation

  • Bent Jesper Christensen & Morten Ø. Nielsen & Thomas Busch, 2006. "The Information Content Of Treasury Bond Options Concerning Future Volatility And Price Jumps," Working Paper 1188, Economics Department, Queen's University.
  • Handle: RePEc:qed:wpaper:1188
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    More about this item

    Keywords

    Bipower variation; bond futures options; HAR; Heterogeneous Autoregressive Model; implied volatility; jumps; realized volatility; VecHAR; volatility forecasting;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G1 - Financial Economics - - General Financial Markets

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