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Survival Analysis of Hedge Funds

Author

Listed:
  • Naohiko Baba

    (Bank of Japan)

  • Hiromichi Goko

    (Bank of Japan)

Abstract

This paper applies a survival analysis to individual hedge fund data reported in the Lipper TASS database. We use several methodologies including the non-parametric survival analysis, the Semi-parametric Cox proportional hazard analysis with shared frailty, and the logit analysis to assess the effects of both fund-specific characteristics and the dynamic performance properties on survival probabilities of hedge funds. Estimation results are summarized as follows. (i) Funds with higher returns, assets under management (AUM), and recent fund flows, and funds with lower volatilities and higher skewness of returns and AUM have higher survival probabilities. (ii) Incentive scheme matters for survival probabilities, and the directions of the effects differ depending on the measures: funds with higher incentive fees have lower survival probabilities, while those with a high water mark have higher survival probabilities. (iii) Cancellation policies as proxies for liquidity constraints matter: funds with a longer redemption notice period and a lower redemption frequency have higher survival probabilities. (iv) As the number of total hedge funds becomes larger, survival probability significantly falls. (v) On the other hand, leverage does not significantly influence survival probabilities.

Suggested Citation

  • Naohiko Baba & Hiromichi Goko, 2006. "Survival Analysis of Hedge Funds," Bank of Japan Working Paper Series 06-E-5, Bank of Japan.
    • Handle: RePEc:boj:bojwps:06-e-5
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    File URL: http://www.boj.or.jp/en/research/wps_rev/wps_2006/data/wp06e05.pdf
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    References listed on IDEAS

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    Cited by:

    1. Mark A. Carlson & Jason Steinman, 2008. "Market conditions and hedge fund survival," Finance and Economics Discussion Series 2008-28, Board of Governors of the Federal Reserve System (U.S.).
    2. Rebeca Peláez & Ricardo Cao & Juan M. Vilar, 2022. "Bootstrap Bandwidth Selection and Confidence Regions for Double Smoothed Default Probability Estimation," Mathematics, MDPI, vol. 10(9), pages 1-25, May.
    3. Haghani, Shermineh, 2014. "Modeling hedge fund lifetimes: A dependent competing risks framework with latent exit types," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 291-320.
    4. Pruchnicka-Grabias Izabela, 2016. "Maximum Drawdown Measures in Hedge Fund Efficiency Appraisal," Financial Internet Quarterly (formerly e-Finanse), Sciendo, vol. 12(4), pages 83-91, December.
    5. Peláez, Rebeca & Van Keilegom, Ingrid & Cao, Ricardo & Vilar, Juan M., 2024. "Probability of default estimation in credit risk using mixture cure models," Computational Statistics & Data Analysis, Elsevier, vol. 189(C).

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    More about this item

    Keywords

    Hedge funds; High Water Mark; Incentive Fees; Survival Analysis; Panel Logit;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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