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Portfolio insurance strategies in a low interest rate environment: A simulation based study

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  • Ariful Hoque
  • Robin Kämmer
  • Frieder Meyer-Bullerdiek

Abstract

The aim of this study is to ascertain through a simulation process how low and even negative interest rates affect the performance of different portfolio insurance (PI) methodologies and which concepts are successful in different assumed scenarios. In the past, many papers have been published providing empirical evidence on the benefits of PI strategies in different markets. However, hardly any paper focuses on the impact of low interest rates on the performance of PI strategies although interest rates are currently at an all-time low throughout the OECD. In this paper we run Monte Carlo simulations for the buy-and-hold (B&H), Constant Mix, Stop Loss, Constant Proportion Portfolio Insurance (CPPI), and Time Invariant Portfolio Protection (TIPP) strategies. We show that lower interest rates have an impact on the ranking of these strategies according to different performance measures such as Sharpe Ratio, Treynor Ratio, Sortino Ratio, or Lower Partial Moment (LPM) performance measures. B&H and Constant Mix perform relatively well in respect of the Sharpe and Treynor Ratio. However, when considering the Sortino Ratio or LPM performance measures these concepts are particularly badly affected by the reduction in interest rates, especially when it comes to negative rates. Here, the strength of the CPPI strategy becomes obvious. JEL classification number: G11Keywords: Portfolio insurance, Constant Proportion Portfolio Insurance, CPPI, Time Invariant Portfolio Protection, TIPP, Buy-and-Hold, Constant Mix, Stop Loss, downside protection, Monte Carlo Simulation

Suggested Citation

  • Ariful Hoque & Robin Kämmer & Frieder Meyer-Bullerdiek, 2018. "Portfolio insurance strategies in a low interest rate environment: A simulation based study," Journal of Finance and Investment Analysis, SCIENPRESS Ltd, vol. 7(3), pages 1-2.
    • Handle: RePEc:spt:fininv:v:7:y:2018:i:3:f:7_3_2
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    References listed on IDEAS

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    1. Annaert, Jan & Osselaer, Sofieke Van & Verstraete, Bert, 2009. "Performance evaluation of portfolio insurance strategies using stochastic dominance criteria," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 272-280, February.
    2. Dichtl, Hubert & Drobetz, Wolfgang, 2011. "Portfolio insurance and prospect theory investors: Popularity and optimal design of capital protected financial products," Journal of Banking & Finance, Elsevier, vol. 35(7), pages 1683-1697, July.
    3. Cesari, Riccardo & Cremonini, David, 2003. "Benchmarking, portfolio insurance and technical analysis: a Monte Carlo comparison of dynamic strategies of asset allocation," Journal of Economic Dynamics and Control, Elsevier, vol. 27(6), pages 987-1011, April.
    4. Bawa, Vijay S. & Lindenberg, Eric B., 1977. "Abstract: Capital Market Equilibrium in a Mean-Lower Partial Moment Framework," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 12(4), pages 635-635, November.
    5. Ron Bird & Davis Dennis & Mark Tippett, 1990. "A stop loss approach to portfolio insurance," Published Paper Series 1990-1, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    6. Bawa, Vijay S. & Lindenberg, Eric B., 1977. "Capital market equilibrium in a mean-lower partial moment framework," Journal of Financial Economics, Elsevier, vol. 5(2), pages 189-200, November.
    7. Pézier, Jacques & Scheller, Johanna, 2013. "Best portfolio insurance for long-term investment strategies in realistic conditions," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 263-274.
    8. repec:bla:jfinan:v:43:y:1988:i:2:p:283-99 is not listed on IDEAS
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