Trading and liquidity in the catastrophe bond market

Herrmann, Markus; Hibbeln, Martin

doi:10.1111/jori.12407

Cited by 9 publications

(3 citation statements)

References 68 publications

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“…Additionally, extensive literature exists on the empirical pricing of CAT bonds in both primary and secondary markets, CAT bonds' liquidity premium, and insurance‐linked securities (ILS) fund returns. Noteworthy contributions include the works of Braun (2016), Braun et al (2019, 2022), Herrmann and Hibbeln (2021, 2023), and Beer and Braun (2022). However, limited attention has been given specifically to modeling and hedging wildfire risk in isolation.…”

Section: Introductionmentioning

confidence: 99%

Mitigating wildfire losses via insurance‐linked securities: Modeling and risk management perspectives

Li,

2023

J of Risk & Insurance

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This paper investigates the use of catastrophe (CAT) bonds as a risk management tool for wildfires. We introduce a set of Bayesian dynamic models designed to accurately represent wildfire losses, allowing a thorough examination of wildfire CAT bond pricing and hedge effectiveness. Our model captures crucial attributes of wildfire data, such as zero inflation, overdispersion, temporal fluctuations, and spatial dependence. Employing extensive quantitative analyses of US wildfire data, we highlight that CAT bonds can substantially mitigate tail risk associated with insurers' liability. Importantly, index‐based CAT bonds, drawing their payouts from aggregate wildfire losses over a larger geographical scope than an insurer's operational area, also provide effective hedges. Our research underscores the potential of wildfire CAT bonds as an enhancement to traditional reinsurance strategies, offering insurers an improved means to manage and mitigate wildfire exposures amidst inherent uncertainties.

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Section: Introductionmentioning

confidence: 99%

Mitigating wildfire losses via insurance‐linked securities: Modeling and risk management perspectives

Li,

2023

J of Risk & Insurance

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“…Several regions in one country had insured their catastrophic risks in catastrophe bonds, summarized in Table 1. 1 shows that, in 2013, RCBs were issued in Florida, a state of the United States of America (USA), for hurricane financing [10,11]. The bonds were issued by Sunshine Re 2013-1 as a special-purpose vehicle (SPV) and had a face value of 20 million US Dollars (USD).…”

Section: Introductionmentioning

confidence: 99%

A Regional Catastrophe Bond Pricing Model and Its Application in Indonesia’s Provinces

Sukono,

Napitupulu,

Riaman

et al. 2023

Mathematics

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The national scale of catastrophic losses risk linked to state catastrophe bonds (SCB) is enormous. It can reduce investors’ interest in buying them because the capital required and the loss probability are also significant. To overcome this, the SCB can be made on a smaller regional scale, known as a regional catastrophe bond (RCB). Through RCBs, the catastrophic loss risk investors bear becomes smaller, which can increase investors’ interest in buying them. Unfortunately, RCB issuance faced a fundamental obstacle, where its complex pricing model needed further study. Therefore, this study aims to model it. The model uniquely involves the inflation rate modeled using the Fisher equation and the nonbinary scheme of coupon and redemption value payments modeled by a compound Poisson process. In addition, the model is applied to Indonesia’s catastrophe data, resulting in all provinces’ RCB price estimation and the effects of several variables on RCB price. This research can guide the RCB pricing process of the country’s regions. The estimated RCB prices can be used by Indonesia’s government if RCBs are to be issued one day. Finally, the effects of the inflation rate, catastrophe intensity, and geographical location on RCB prices can guide investors in selecting bond portfolios.

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“…Thus, we followGötze and Gürtler (2018), who suggest that the risk characteristics of CAT bonds without a rating resemble those of "B"-rated bonds. Hence, the spreads of "B" corporate bonds are matched to CAT bonds without a rating.8 According toHerrmann and Hibbeln (2022), we use the distribution of arrival frequencies for US Hurricanes and European Winter Storms modeled by AIR. In this way, each calendar month is assigned a number that describes the relative proportion of arrival frequency during a calendar year.…”

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confidence: 99%

Forecasting accuracy of machine learning and linear regression: evidence from the secondary CAT bond market

2023

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The main challenge in empirical asset pricing is forecasting the future value of assets traded in financial markets with a high level of accuracy. Because machine learning methods can model relationships between explanatory and dependent variables based on complex, non-linear, and/or non-parametric structures, it is not surprising that machine learning approaches have shown promising forecasting results and significantly outperform traditional regression methods. Corresponding results were achieved for CAT bond premia forecasts in the primary market. However, since secondary market data sets have a panel data structure, it is unclear whether the results of primary market studies can be applied to the secondary market. Against this background, this study aims to build the first out-of-sample forecasting model for CAT bond premia in the secondary market, comparing different modeling approaches. We apply random forest and neural networks as representatives of machine learning methods and linear regression based on a comprehensive data set of CAT bond issues and across various forecasting settings and show that random forest forecasts are significantly more precise. Because the lack of transparency of machine learning methods may limit their applicability, especially for institutional investors, we show ways to identify important variables in the context of random forest price forecasting.

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Trading and liquidity in the catastrophe bond market

Cited by 9 publications

References 68 publications

Mitigating wildfire losses via insurance‐linked securities: Modeling and risk management perspectives

Mitigating wildfire losses via insurance‐linked securities: Modeling and risk management perspectives

A Regional Catastrophe Bond Pricing Model and Its Application in Indonesia’s Provinces

Forecasting accuracy of machine learning and linear regression: evidence from the secondary CAT bond market

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