As one may be able to guess, one significant drawback in the study was the assumption of normally distributed returns. If one were to expand on the study, the first suggestion would be to utilize tools of analysis that considered the possibility of returns being non- normally distributed. For example, expanding the MVaR analysis on the entirety of a portfolio could give very different results to the ones found in this thesis. Certainly, as insurance-linked security returns are driven by different factors than those of other assets, this may not be optimal either as the MVaR measure is quite sensitive to excess kurtosis and skewness, which are to be expected given that insurance-linked securities incur losses in large quantities, due to natural catastrophes et cetera. Thus, an evaluation of different portfolio optimization techniques and their applicability in the case of insurance-linked securities may also prove to be a fruitful study.
Another thing to note is the usage of freely available, but perhaps not optimal data. For instance, the main index of the study was the Eurekahedge ILS Advisers Index, which is an index of 28 constituent hedge funds. Analysing the performance of hedge funds is quite tricky, as often comparing hedge fund performance is far more complex than simply comparing a fund’s return with an index. (Tran 2006, ch.8) At times, hedge fund indexes can even overstate the returns of the hedge funds within them drastically due to data problems such as the survivorship bias. This refers to the problem of indexes only reporting on the funds that are still in operation, which are the survivors. This obviously leaves out the underperformers who have stopped operating due to lacklustre performance in comparison with the benchmark. (Tran 2006, ch.4) Luckily, both the hedge funds selected for the study seem to contain dead and closed funds, as per the methodology elaborated by Eurekahedge. For more information about the hedge funds used in the study, one should explore the Eurekahedge methodology page.
As the data was chosen from scarce freely available indexes, the reporting frequency was also not necessarily optimal. Monthly returns were used because daily returns were not available. Although monthly returns are as applicable as daily returns, they may leave out important information. Dale Morse (1984, pp.605-623) has studied the use of both monthly and daily returns, testing information content between the two data types. His
findings were that daily returns are preferred with the only exception being in situations where there is uncertainty about the date of information of the announcement. Obviously, the use of daily returns was simply not possible in this scenario, as most of the indexes only reported monthly, but this should be taken into consideration. Thankfully the mean- variance analysis is done with annual returns, and the usage of a monthly or yearly VaR is typical, but there is still room left for improvement in e.g., the descriptive statistics.
Also, for any statistical modelling technique for predicting the volatility of an asset, daily returns should be preferred.
As data also posed issued in terms of reliability and validity, further studies done on the optimization of a portfolio including insurance-linked securities with more frequently reported data, perhaps from a private source, should be considered. Simply studying the returns of a hedge fund that invests in insurance-linked securities should not be considered an accurate depiction of the actual properties of the asset class, as hedge funds in general do pose problems of their own. Thus, if other data sources can be found to depict the risk- return profile of insurance-linked securities as an asset, the use of those to verify the results of this thesis should be encouraged.
Another point to consider is the possible increase of natural catastrophes due to climate change. For example, Morana and Sbrana (2019) have studied the implications of climate change for the catastrophe bond market. Their findings suggest that climate change risk may not have yet been incorporated into cat bond multiples. As natural catastrophes are expected to increase in frequency due to climate change, this also puts into question the future returns and possible increase in downside risk for cat bonds in general. Thus, climate change research and the ILS market go hand-in-hand and understanding the impact of climate change on the volume and severity of natural catastrophes is increasingly important for any investor that is considering ILS products, specifically cat bonds. Thus, simply using historical returns may not give the most realistic depiction of what the actual risk with these products are.
Braun, Ammar and Eling (2019) have also noted that the recurrence periods of the events that are securitized in e.g., cat bonds can be as long as 1000 years. The observation period of 10 years used in this study is therefore only a fraction of a realistic timeframe for such analyses. Unfortunately, the markets for ILS products have not existed for that long, so some shortcuts had to be taken. Therefore, it is unclear what type of risk-return profile
the investors can expect from ILS products in the future. Simulating possible future catastrophic events through advanced models may yield somewhat accurate results, but realistically that is all they are, simulations. The outlook of the markets for ILS are somewhat of a mystery, as the market seems to be growing, but there is yet uncertainty in the riskiness of it all. Future research into these developments in the market are welcome as well.
Although this study was presented from the viewpoint of the investor, it could be argued that insurance-linked securities have a role in socially responsible investing. Seeing as these securities insure risks affecting vulnerable areas and people, it is interesting that ILS and cat bonds are not seeing heavier push from an ESG (environmental, social and governance) perspective. In fact, very few results come up in any database with the keywords “ILS and ESG” or “cat bond* and ESG”, with most being articles. A real scarcity seems to surround the field in academic research, which presents an interesting opportunity for anyone interested in researching the opportunities ILS and cat bonds offer from an ESG point of view. With the markets seemingly being in love with all things ESG, this could present an opportunity to scale the ILS and cat bond markets to more investors looking for investments fitting the ESG criteria.
As mentioned, the insurance-linked securities have not been the beneficiaries of much academic interest. However, the market seems to be growing at an exponential rate, which increases possibilities for future research and industry interest. It is my hope that insurance-linked securities pique the interest of others such as myself, and the asset class is more heavily researched either from the viewpoints that I’ve described, or in other ways. From the findings of this study, it could be argued that the asset class presents interesting opportunities for investors and academics alike, which hopefully leads to an increased understanding of the asset class by the general public in the future.
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