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Does Diversification Drive Down Risk-adjusted Returns? A Quantile Regression Approach

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  • Shim Jeungbo

    (Business School, University of Colorado Denver, 1475 Lawrence Street, Denver, CO80202, USA)

Abstract

This study examines diversification-performance relationship in the U.S. property-liability insurance industry over the period of 1996–2010. Unlike prior studies that rely on the conditional mean estimation method, we employ quantile regression, which captures the heterogeneous effects of diversification on conditional return distribution. The results show that diversification does not necessarily drive down risk-adjusted returns and its effects vary along return distribution. We find that there is a diversification discount for firms in the lower levels of return distribution, whereas a diversification premium exists for firms in the upper levels of return distribution. We provide evidence that the relations between risk-adjusted returns and other explanatory variables are not constant, but vary over the quantiles of return distribution. Our results are robust to alternative measures of an insurer’s performance and product diversification.

Suggested Citation

  • Shim Jeungbo, 2017. "Does Diversification Drive Down Risk-adjusted Returns? A Quantile Regression Approach," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 11(2), pages 1-32, July.
    • Handle: RePEc:bpj:apjrin:v:11:y:2017:i:2:p:32:n:1
    DOI: 10.1515/apjri-2016-0015
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    More about this item

    Keywords

    diversification; performance; property-liability insurers; quantile regression;
    All these keywords.

    JEL classification:

    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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