The purpose of this paper is to provide new empirical evidence on frontier efficiency measurement in the international insurance industry, a topic of great interest in the academic literature during the last several years. A broad efficiency comparison of 6462 insurers from 36 countries is conducted. Different methodologies, countries, organizational forms, and company sizes are compared, considering life and non-life insurers. We find a steady technical and cost efficiency growth in international insurance markets from 2002 to 2006, with large differences across countries. Denmark and Japan have the highest average efficiency, whereas the Philippines is the least efficient. Regarding organizational form, the results are not consistent with the expense preference hypothesis, which claims that mutuals should be less efficient than stocks due to higher agency costs. Only minor variations are found when comparing different frontier efficiency methodologies (data envelopment analysis, stochastic frontier analysis).

In this paper we prove that partial-moments-based performance measures (e.g., Omega, Kappa, upside-potential ratio, Sortino–Satchell ratio, Farinelli–Tibiletti ratio), value-at-risk-based performance measures (e.g., VaR ratio, CVaR ratio, Rachev ratio, generalized Rachev ratio), and other admissible performance measures are a strictly increasing function in the Sharpe ratio. The theoretical basis of this result is the location and scale property and two other plausible and mild conditions. Our result provides a decision-theoretic foundation for all these frequently used performance measures. Moreover, it might explain the empirical finding that all these measures typically lead to very similar rankings.

The least restrictive sufficient condition for expected utility to imply Sharpe ratio rankings is the location and scale (LS) property (see Sinn, 1983, Meyer, 1987). The normal, the extreme value, and many other distributions commonly used in finance satisfy this property. We argue that the LS property is also sufficient for expected utility to imply drawdown-based performance measure rankings, because for investment funds satisfying the LS condition, the Sharpe ratio and drawdown-based performance measures result in identical rankings. Hence, the same conditions that provide an expected utility foundation for the Sharpe ratio also provide a foundation for drawdown-based performance measures. We conclude that from a decision-theoretic perspective, drawdown-based performance measures are as good as the Sharpe ratio.

Use of short selling and derivatives is limited in most emerging markets because such instruments are not as readily available as they are in developed capital markets. These limitations raise questions about the value added provided by hedge funds, especially compared to traditional mutual funds active in these markets. We use five existing performance measurement models plus a new asset-style factor model to identify the return sources and the alpha generated by both types of funds. We analyze subperiods, different market environments, and structural breaks. Our results indicate that some hedge funds generate significant positive alpha, whereas most mutual funds do not outperform traditional benchmarks. We find that hedge funds are more active in shifting their asset allocation. The higher degree of freedom that hedge funds enjoy in their investment style might thus be one explanation for the differences in performance.

The Sharpe ratio is adequate for evaluating investment funds when the returns of those funds are normally distributed and the investor intends to place all his risky assets into just one investment fund. Hedge fund returns differ significantly from a normal distribution. For this reason, other performance measures for hedge fund returns have been proposed in both the academic and practice-oriented literature. In conducting an empirical study based on return data of 2763 hedge funds, we compare the Sharpe ratio with 12 other performance measures. Despite significant deviations of hedge fund returns from a normal distribution, our comparison of the Sharpe ratio to the other performance measures results in virtually identical rank ordering across hedge funds.

Investment managers specializing in insurance-linked securities (ILS) generate returns that behave unlike those of any other asset class. We introduce four ILS-specific factor models, which explain their time-series and cross-sectional variation. Despite a strong fit, we are left with positive-significant alphas for about one quarter of the funds in our sample, some of which can be attributed to industry loss warranty (ILW) exposures. In addition, they are related to fund size, age, and performance fees. Although we do not find evidence for market timing abilities, we can rule out luck as a cause of outperformance by controlling for false discoveries.

We conduct a comprehensive asset pricing analysis for the U.S. property/liability insurance industry using monthly data from 1988 to 2015. We find that state-of-the-art models such as the Fama and French (2015) five-factor model cannot explain the returns of property/liability insurance stocks in a satisfactory way. We adapt the model proposed by Adrian et al. (2015) for financial institutions and define an insurance-specific five-factor asset pricing model (INS5), which can explain the cross-section of property/liability insurance-stock returns better than competing models. The priced factors are the market return, the book-to-market ratio, return on equity, short-term reversal, and the spread between the property/liability insurance sector and the market return.

We estimate economies of scale and scope as well as cost and revenue efficiency to explain the structure of the global reinsurance market, where large reinsurers dominate but both diversified and specialized reinsurers are competitive. The costs and benefits of size and product diversification are particularly relevant to the reinsurance industry, as risk diversification is central to the industry's business model. We find that reinsurers with total assets less than USD 2.9 billion exhibit scale economies, while those with total assets greater than USD 15.5 billion do not. Large reinsurers are characterized by high cost efficiency, while small reinsurers exhibit superior efficiency only when specialized. Large reinsurers also exhibit revenue scope economies when operating both life and nonlife reinsurance. Moreover, the evidence is in line with the efficient structure hypothesis: cost-efficient reinsurers can charge lower prices without sacrificing profitability.