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Minimal model of financial stylized facts

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  • Danilo Delpini
  • Giacomo Bormetti

Abstract

In this work we afford the statistical characterization of a linear Stochastic Volatility Model featuring Inverse Gamma stationary distribution for the instantaneous volatility. We detail the derivation of the moments of the return distribution, revealing the role of the Inverse Gamma law in the emergence of fat tails, and of the relevant correlation functions. We also propose a systematic methodology for estimating the parameters, and we describe the empirical analysis of the Standard & Poor 500 index daily returns, confirming the ability of the model to capture many of the established stylized fact as well as the scaling properties of empirical distributions over different time horizons.

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  • Danilo Delpini & Giacomo Bormetti, 2010. "Minimal model of financial stylized facts," Papers 1011.5983, arXiv.org, revised Mar 2011.
    • Handle: RePEc:arx:papers:1011.5983
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    Cited by:

    1. Giacomo Bormetti & Sofia Cazzaniga, 2014. "Multiplicative noise, fast convolution and pricing," Quantitative Finance, Taylor & Francis Journals, vol. 14(3), pages 481-494, March.
    2. Giacomo Bormetti & Sofia Cazzaniga, 2011. "Multiplicative noise, fast convolution, and pricing," Papers 1107.1451, arXiv.org.
    3. D. Delpini & G. Bormetti, 2015. "Stochastic volatility with heterogeneous time scales," Quantitative Finance, Taylor & Francis Journals, vol. 15(10), pages 1597-1608, October.

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