The stable non-Gaussian asset allocation

Yesim Tokat; Svetlozar T. Rachev; Eduardo Schwartz

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SPEPS Preprint

Author(s):	Yesim Tokat, University of California Svetlozar T. Rachev, University of California Eduardo Schwartz, University of California
Title:	The stable non-Gaussian asset allocation – A comparison with the classical Gaussian approach
Date of Acceptance:	28.11.2000
Submission Date:	01.11.2000
Series Title:	Stochastic Programming E-Print Series (SPEPS)
Editors:	Julie L. Higle; Werner Römisch; Surrajeet Sen
Complete Preprint:	pdf (urn:nbn:de:kobv:11-10057859)
Keywords (eng):	scenario generation, dynamic portfolio optimization, stable distribution
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Abstract (eng):

We analyze a multistage stochastic asset allocation problem with decision rules. The uncertainty is modeled using economic scenarios with Gaussian and stable Paretian non-Gaussian innovations. The optimal allocations under these alternative hypothesis are compared. If the agent has very low or very high risk aversibility, then the Gaussian and stable non-Gaussian scenarios result in similar allocations. When the risk aversion of the agent is between these two extreme cases, then the two distributional assumptions may result in very different asset allocations. Our calculations suggest that the allocations may be up to 85% different depending on the utility function and the level of risk aversion of the agent.

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