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Microstructure Models with Short-Term Inertia and Stochastic Volatility

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  • Michael A. Kouritzin

Abstract

Partially observed microstructure models, containing stochastic volatility, dynamic trading noise, and short-term inertia, are introduced to address the following questions: (1) Do the observed prices exhibit statistically significant inertia? (2) Is stochastic volatility (SV) still evident in the presence of dynamical trading noise? (3) If stochastic volatility and trading noise are present, which SV model matches the observed price data best? Bayes factor methods are used to answer these questions with real data and this allows us to consider volatility models with very different structures. Nonlinear filtering techniques are utilized to compute the Bayes factor on tick-by-tick data and to estimate the unknown parameters. It is shown that our price data sets all exhibit strong evidence of both inertia and Heston-type stochastic volatility.

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  • Michael A. Kouritzin, 2015. "Microstructure Models with Short-Term Inertia and Stochastic Volatility," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-17, October.
    • Handle: RePEc:hin:jnlmpe:323475
    DOI: 10.1155/2015/323475
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    Cited by:

    1. Dashti Moghaddam, M. & Serota, R.A., 2021. "Combined multiplicative–Heston model for stochastic volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).

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