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A Large-Scale Optimization Model for Replicating Portfolios in the Life Insurance Industry

Author

Listed:
  • Qunzhi Zhang

    (ETH Zurich)

  • Didier Sornette

    (Swiss Finance Institute; ETH Zürich - Department of Management, Technology, and Economics (D-MTEC))

  • Mehmet Balcilar

    (Eastern Mediterranean University)

  • Rangan Gupta

    (University of Pretoria - Department of Economics)

  • Zeynel Abidin Ozdemir

    (Gazi University)

  • I. Hakan Yetkiner

    (Izmir University of Economics)

Abstract

The aim of this paper is to present novel tests for the early causal diagnostic of positive and negative bubbles in the S&P 500 index and the detection of End-of-Bubble signals with their corresponding confidence levels. We use monthly S&P 500 data covering the period from August 1791 to August 2014. This study is the first work in the literature showing the possibility to develop reliable ex-ante diagnostics of the frequent regime shifts over two centuries of data. We show that the DS LPPLS (log-periodic power law singularity) approach successfully diagnoses positive and negative bubbles, constructs efficient End-of-Bubble signals for all of the well-documented bubbles, and obtains for the first time new statistical evidence of bubbles for some other events. We also compare the DS LPPLS method to the exponential curve fitting and the generalized sup ADF test approaches and find that DS LPPLS system is more accurate in identifying well-known bubble events, with significantly smaller numbers of false negatives and false positives.

Suggested Citation

  • Qunzhi Zhang & Didier Sornette & Mehmet Balcilar & Rangan Gupta & Zeynel Abidin Ozdemir & I. Hakan Yetkiner, 2016. "A Large-Scale Optimization Model for Replicating Portfolios in the Life Insurance Industry," Swiss Finance Institute Research Paper Series 16-05, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp1605
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    File URL: http://ssrn.com/abstract=2727755
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    More about this item

    Keywords

    S&P 500; LPPL method; stock market bubble; forecast; bubble indicators;
    All these keywords.

    JEL classification:

    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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