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Parameter Estimation for Stock Models with Non-Constant Volatility Using Markov Chain Monte Carlo Methods

  • Conference paper
Operations Research Proceedings 2006

Part of the book series: Operations Research Proceedings ((ORP,volume 2006))

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Abstract

We consider a model for a financial market where the asset prices satisfy a stochastic differential equation. For the volatility no new source of randomness is introduced, but the volatility at each time depends deterministically on all previous price fluctuations. Such non-constant volatility models preserve the completeness of the market while they allow for many attractive features.

supported by FWF Project P17947-N12

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References

  1. M. Hahn, S. Frühwirth-Schnatter, and J. Sass, Markov chain Monte Carlo methods for parameter estimation in multidimensional continuous time Markov switching models, preprint (2005).

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  2. M. Hahn, W. Putschögl, and J. Sass, Portfolio optimization with non-constant volatility and partial information, Brazilian Journal of Probability and Statistics (2006), to appear.

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  3. D. G. Hobson and L. C. G. Rogers, Complete models with stochastic volatility, Mathematical Finance 8 (1998), no. 1, 27–48.

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  4. E. Jacquier, N. G. Polson, and P. E. Rossi, Bayesian analysis of stochastic volatility models, Journal of Business & Economic Statistics 12 (1994), no. 4, 371–89.

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  5. A. Platania and L. C. G. Rogers, Putting the Hobson-Rogers model to the test, preprint (2005).

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Author information

Authors and Affiliations

  1. RICAM, Austrian Academy of Sciences, Linz, Austria

    Markus Hahn, Wolfgang Putschögl & Jörn Sass

Authors
  1. Markus Hahn
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  2. Wolfgang Putschögl
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  3. Jörn Sass
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Editor information

Editors and Affiliations

  1. Institut für Wirtschaftstheorie und Operations Research, Universität Karlsruhe (TH), Kaiserstraße 12, 76131, Karlsruhe, Germany

    Karl-Heinz Waldmann  & Ulrike M. Stocker  & 

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© 2007 Springer-Verlag Berlin Heidelberg

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Hahn, M., Putschögl, W., Sass, J. (2007). Parameter Estimation for Stock Models with Non-Constant Volatility Using Markov Chain Monte Carlo Methods. In: Waldmann, KH., Stocker, U.M. (eds) Operations Research Proceedings 2006. Operations Research Proceedings, vol 2006. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69995-8_38

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