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Discrete-valued Levy processes and low latency financial econometrics

Author

Listed:
  • Neil Shephard
  • David G. Pollard
  • Ole E. Barndorff-Nielsen
Abstract
Motivated by features of low latency data in finance we study in detail discrete-valued Levy processes as the basis of price processes for high frequency econometrics. An important case of this is a Skellam process, which is the difference of two independent Poisson processes. We propose a natural generalisation which is the difference of two negative binomial processes. We apply these models in practice to low latency data for a variety of different types of futures contracts.

Suggested Citation

  • Neil Shephard & David G. Pollard & Ole E. Barndorff-Nielsen, 2010. "Discrete-valued Levy processes and low latency financial econometrics," Economics Series Working Papers 490, University of Oxford, Department of Economics.
    • Handle: RePEc:oxf:wpaper:490
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    References listed on IDEAS

    as
    1. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2008. "Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise," Econometrica, Econometric Society, vol. 76(6), pages 1481-1536, November.
    2. Ole E. Barndorff‐Nielsen & Neil Shephard, 2002. "Econometric analysis of realized volatility and its use in estimating stochastic volatility models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 253-280, May.
    3. repec:oxf:wpaper:264 is not listed on IDEAS
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    More about this item

    Keywords

    Futures markets; High frequency econometrics; Low latency data; Negative binomial; Skellam distribution;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • 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

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