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A Jump-Diffusion Model with Stochastic Volatility and Durations

Author

Listed:
  • Wei Wei

    (Aarhus University and CREATES)

  • Denis Pelletier

    (North Carolina State University)

Abstract

Market microstructure theories suggest that the durations between transactions carry information about volatility. This paper puts forward a model featuring stochastic volatility, stochastic conditional duration, and jumps to analyze high frequency returns and durations. Durations affect price jumps in two ways: as exogenous sampling intervals, and through the interaction with volatility. We adopt a bivariate Ornstein-Ulenbeck process to model intraday volatility and conditional duration. We develop a MCMC algorithm for the inference on irregularly spaced multivariate processes with jumps. The algorithm provides smoothed estimates of the latent variables such as spot volatility, conditional duration, jump times, and jump sizes. We apply this model to IBM data and find that volatility and conditional duration are interdependent. We also find that jumps play an important role in return variation, but joint modeling of volatility and conditional duration reduces significantly the need for jumps.

Suggested Citation

  • Wei Wei & Denis Pelletier, 2015. "A Jump-Diffusion Model with Stochastic Volatility and Durations," CREATES Research Papers 2015-34, Department of Economics and Business Economics, Aarhus University.
    • Handle: RePEc:aah:create:2015-34
    as

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    File URL: https://repec.econ.au.dk/repec/creates/rp/15/rp15_34.pdf
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Durations; Stochastic Volatility; Price jumps; High-frequency data; Bayesian inference;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • G1 - Financial Economics - - General Financial Markets

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