Department of Economics and Business Economics

A Jump Diffusion Model for Volatility and Duration

Research output: Working paperResearch

  • Wei Wei
  • Denis Pelletier, North Carolina State University, United States
This paper puts forward a stochastic volatility and stochastic conditional duration
with cojumps (SVSDCJ) model to analyze returns and durations. In high
frequency data, transactions are irregularly spaced, and the durations between
transactions carry information about volatility as suggested by the market microstructure theory. Traditional measures of volatility do not utilize durations. I
adopt a jump diffusion process to model the persistence of intraday volatility and
conditional duration, and their interdependence. The jump component is disentangled from the continuous part of the price, volatility and conditional duration process. I develop a MCMC algorithm for the inference of irregularly spaced multivariate process with jumps. The algorithm provides smoothed estimates of the latent variables such as spot volatility, jump times and jump sizes. I apply this model to IBM data and I find meaningful relationship between volatility and conditional duration. Also, jumps play an important role in the total variation, but the jump variation is smaller than traditional measures that use returns sampled at lower frequency.
Original languageEnglish
Place of publicationAarhus
PublisherInstitut for Økonomi, Aarhus Universitet
Number of pages41
Publication statusPublished - 2013

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