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Performance evaluation of portfolio insurance strategies using stochastic dominance criteria

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  • J. ANNAERT

    ()

  • S. VAN OSSELAER

    ()

  • B. VERSTRAETE
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    Abstract

    The continuing creation of portfolio insurance applications as well as the mixed research evidence suggests that so far no consensus has been reached about the effectiveness of portfolio insurance. Therefore, this paper provides a performance evaluation of the stop-loss, synthetic put and constant proportion portfolio insurance techniques based on a block-bootstrap simulation. Apart from more traditional performance measures, we consider the Value-at-risk and Expected Shortfall of the strategies, which are more appropriate in an insurance context. An additional performance evaluation is given by means of the stochastic dominance framework where we account for sampling error. A sensitivity analysis is performed in order to examine the impact on performance of a change in a specific decision variable (ceteris paribus). The results indicate that a buy-and-hold strategy does not dominate the portfolio insurance strategies at any stochastic dominance order. Moreover, both for the stop-loss and synthetic put strategy a 100% floor value outperforms lower floor values. For the CPPI strategy we find that a higher CPPI multiple enhances the upward potential of the CPPI strategies, but harms the protection level in return. As regards the optimal rebalancing frequency, daily rebalancing should be preferred for the synthetic put and CPPI strategy, despite the higher transaction costs.

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    File URL: http://www.feb.ugent.be/nl/Ondz/wp/Papers/wp_07_473.pdf
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    Bibliographic Info

    Paper provided by Ghent University, Faculty of Economics and Business Administration in its series Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium with number 07/473.

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    Length: 25 pages
    Date of creation: Jun 2007
    Date of revision:
    Handle: RePEc:rug:rugwps:07/473

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    Keywords: Portfolio insurance; Performance evaluation; Stochastic dominance; Block-bootstrap simulation;

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