Recent analysis on the return drivers of insurance-linked securities (ILS) shows that over a 15-year period, ILS funds have recorded a superior historical performance when compared with other asset classes.
In a recent article, Alexander Braun and Martin Eling of the Institute of Insurance Economics, University of St. Gallen in Switzerland and Semir Ben Ammar, Deliotte AG in Switzerland, discuss the return drivers of ILS while at the same time highlighting the inability of traditional factor models to explain the return characteristics of ILS funds. We spoke with them to gain further insight into ILS fund performance.
According to their report, existing factor models cannot explain the returns of ILS funds.
Subsequently, the article introduces four ILS-specific factor models: a single-index model, a ratings-based two-factor model, a perils-based three-factor model, and a spread-based four -factor model. The article then leverages these models to see whether certain ILS funds were able to outperform their peers on a risk-adjusted basis in the past and looks to identify the reasons for abnormal returns.
The report finds that all traditional factor models “fail miserably” and after cross-sectional analysis, identifies the perils-based model as the strictest benchmark for ILS funds.
“The perils model leaves significantly positive alphas for about one quarter of all funds. It is possible to attribute some of these abnormal returns to industry loss warranty (ILW) exposures. Furthermore, they are related to fund size, fund age, and performance fees.
“Finally, we do not find evidence for market timing abilities but can rule out pure luck by controlling for false discoveries,” explains the article.
Ultimately, the analysis indicates a superior historical performance of ILS funds compared to other asset classes, with positive returns being achieved in 89% of all analysed months. Interestingly, this compares with 66% for hedge funds and 71% for corporate bonds.
But despite the impressive historical performance of the asset class, the potential diversification on offer and also the fact the asset class is uncorrelated and thus isn’t influenced by broader financial market turmoil, the article notes that not much is known about the return drivers of ILS.
Based on a perils-based model, Braun, Eling and Ammar, find positive alphas for roughly 25% of all ILS funds.
The trio notes that the most plausible explanation for the abnormal returns are beta exposures to alternative capital instruments that are not captured by their factor models, such as Collateralized Reinsurance and ILW.
“Another reason for the positive abnormal returns might be non-cat bond insurance risk positions that are not captured by the perils model. Indices for such instruments are generally unavailable, the only exception being ILWs,” says the report.
Interestingly, the report also explores if the varied performances of ILS funds has anything to do with certain fund characteristics, such as size, age, performance fees and market timing abilities.
As shown by the Artemis ILS Funds Directory, the assets under management (AuM) varies significantly, from more than $12 billion to as small as $100 million.
Analysis by the trio finds a significant and positive relationship between AuM and abnormal returns.
“On the one hand, it can be reasoned that large funds may capitalize on economies of scale. In addition, they command more resources, which enables them to hire the best analysts and traders that run sophisticated strategies and handle complex assets.
“On the other hand, smaller funds benefit from a higher agility, a clear focus on the best investment ideas, and lesser position sizes that are easier to liquidate, particularly in adverse market environments,” explains the report.
Perhaps even more interesting, is that after plotting alphas against the natural logarithm of AuM, the report finds that AuM of more than $680 million resulted in more underperformers than outperformers. According to the report, this suggests that there might be an optimal size for ILS funds.
“It seems quite plausible that larger ILS funds benefit from lower expense ratios and access to collateralized reinsurance, which, due to its unsecuritized nature and the lack of an active secondary market, is more intricate to handle than cat bonds.
“When growing too large, however, they might suffer from diseconomies of scale because they have to invest a lot of capital in a relatively small market, where highly profitable investment opportunities are scarce,” explains the report.
And it’s not just the fund size that can impact performance, with the report finding significant negative relationship between fund age and alpha returns.
The trio highlights two conceivable explanations for this: either that the younger funds are more skilled than the older funds, or they simply benefit from their ability to select from a far broader set of ILS instruments in their fairly short existence.
The latter is the main reason here, given that instruments such as collateralized reinsurance have only become prevalent in the ILS market since around 2010.
“The time series of older funds, on the contrary, date back to the early days of the ILS market when most of the activity focused almost exclusively on cat bonds. Consequently, a larger part of their historical returns can be well explained by the perils model,” says the report.
Regarding performance fees, the analysis determines that all ILS funds with a significantly negative alpha show no performance fee and, four of the funds with a significantly positive alpha were situated in the highest performance fee bracket of 20%. At the same time, the worst performing ILS fund in the sample analysed has a performance fee of zero.
“These results suggest an effective incentivization of skilled managers,” says the report.
Concluding: “Based on our perils model, we find positive alphas for about one quarter of all ILS funds, which can be partly attributed to ILW exposures. Moreover, 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.”