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Regulator's Determination of Return on Equity in the Absence of Public Firms: The Case of Automobile Insurance in Ontario
Author(s) -
Lazar Fred,
Prisman Eliezer Z.
Publication year - 2015
Publication title -
risk management and insurance review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.386
H-Index - 16
eISSN - 1540-6296
pISSN - 1098-1616
DOI - 10.1111/rmir.12039
Subject(s) - actuarial science , line of business , equity (law) , rate of return , automobile insurance , econometrics , economics , private equity , business , business model , finance , marketing , business relationship management , political science , electronic business , law
In a regulated market, such as automobile insurance (AI), regulators set the return on equity that insurers are allowed to achieve. Most insurers are engaged in a variety of insurance lines of business, and thus the full information beta methodology (FIB) is commonly employed to estimate the AI beta. The FIB uses two steps: first, the beta of each insurer is estimated, and then the beta of each line of business is estimated, as the beta of an insurer is a weighted average of the betas of the lines of business. When there are a sufficient number of public companies, company and market returns are used. Otherwise, researchers have resorted to using accounting data in the FIB. Theoretically, the two steps are not separable and the estimation should be done with one step. We introduce the one‐step methodology in our article. The one‐step and two‐step methodologies are compared empirically for the Ontario market of AI. Insurers in Ontario are predominantly private companies; thus, accounting data are used to estimate the AI beta. We show that a significant bias is introduced by the traditional, two‐step FIB methodology in estimating the betas for different lines of business, while insurers’ betas are very similar under both methods. This has a significant application to the estimation of betas of “pure players” in classic corporate finance. It implies that their betas and hence the resulting, required rates of return used in the net present value calculations should be estimated based on the one‐step method that we develop in this article.