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Exploring the Connection Between Captive Insurance and Cash Flow: Evidence from S&P Global 100 Companies

  • David Russell , Jiun-Lin Chen and Mu-Sheng “Shane” Chang EMAIL logo
Published/Copyright: May 23, 2025

Abstract

A captive insurance subsidiary (or captive for short) has been increasingly accepted as a valid risk management solution, formed primarily to insure the loss exposures of its parent company. This study assesses the effect of captive formation on cash flow from an international perspective based on the 2020 S&P Global 100 index constituents. As a wholly-owned insurance company that underwrites the risks faced by its parent company, a captive performs a dual purpose of self-insurance and mutualization that theoretically helps improve cash flow because recaptured premiums can be retained within the corporate structure and efficiently invested. However, our empirical results fall short of evidence to endorse the recent assertion that captives produce better cash flow, implying that recaptured premiums do increase cash hoard under captives at a given point in time, but do not necessarily lead to improved cash flow on an annual basis at the corporate level. In all, our analysis suggests that the financial incentives to form captives may lie not in increasing shareholder value, but in serving corporate risk-financing needs, addressing perceptions of high commercial premiums and maximizing managerial preferences.

JEL Classification: G22; G32; G41

Corresponding author: Mu-Sheng “Shane” Chang, Department of Finance, Financial Planning, and Insurance, David Nazarian College of Business and Economics, California State University, Northridge, CA, USA, E-mail: 

Acknowledgments

The authors would like to thank W. Jean Kwan for his insights on variations in captive operations in the real world. We benefit a lot from the valuable comments from two reviewers. We also appreciate the support from CSUN’s Department of Finance, Financial Planning, and Insurance for subscribing to the Captive Insurance Database accessed for this study.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None.

  5. Conflict of interest: Not applicable.

  6. Research funding: Not applicable.

  7. Data availability: Not applicable.

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Received: 2024-10-29
Accepted: 2025-04-18
Published Online: 2025-05-23

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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