Seasonality in catastrophe bonds and market‐implied catastrophe arrival frequencies

Herrmann, Markus; Hibbeln, Martin

doi:10.1111/jori.12335

Cited by 16 publications

(21 citation statements)

References 34 publications

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“…By comparison, some research results support that CAT bond markets efficiently incorporate useful information related to future CAT risks (e.g. Zhao and Yu, 2020;Chang et al, 2019;Herrmann and Hibbeln, 2021). For example, Zhao and Yu (2020) find from the CAT bond market's price discovery efficacy that future CAT losses are able to explain high-yield CAT bond spreads.…”

Section: Introductionmentioning

confidence: 94%

“…Based on the market efficiency of CAT markets, Zhao and Yu (2020) use information content of CAT bond prices to propose a market-based forecast that can predict future CAT losses at both 12-and 24-month forecast horizons. Herrmann and Hibbeln (2021) extend the study of Chang et al (2019) and employ secondary market spreads to obtain reliable market-implied distributions of arrival frequencies of hurricane events. Therefore, the information aggregation of CAT bond markets helps manage and forecast CAT risk.…”

Section: Cat Bondmentioning

confidence: 99%

“…(e.g. Lee and Yu, 2002;Braun, 2016;G€ urtler et al, 2016;Zhao and Yu, 2019;Herrmann and Hibbeln, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…***, ** and * denote statistical significance at the 1, 5, and 10% levels, respectively Determinants of CAT bond spreads upon issuance bonds should not be regarded as zero-beta securities. Considering the potential relationship between a critical CAT and a market crash, replacing simple CAT bonds with hybrid CAT bonds combining a traditional CAT bond and protection against a simultaneous drop in stock market prices can increase market volume(Barrieu and Louberg e, 2009) Herrmann and Hibbeln (2021). indicate that seasonality in the probability of CAT bonds being triggered can explain up to 47% of market fluctuations in CAT bond spreads.…”

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confidence: 99%

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Catastrophe risk, reinsurance and securitized risk-transfer solutions: a review

Zhao

Lee

2021

CFRI

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PurposeCatastrophe (CAT) events associated with natural catastrophes and man-made disasters cause profound impacts on the insurance industry. This research thus reviews the impact of CAT risk on the insurance industry and how traditional reinsurance and securitized risk-transfer instruments are used for managing CAT risk.Design/methodology/approachThis research reviews the impact of CAT risk on the insurance industry and how traditional reinsurance and securitized risk-transfer instruments are used for managing CAT risk. Apart from many negative influences, CAT events can increase the net revenue of the insurance industry around CAT events and improve insurance demand over the post-CAT periods. The underwriting cycle of reinsurance causes inefficiencies in transferring CAT risks. Securitized risk-transfer instruments resolve some inefficiencies of the reinsurance market, but are subject to moral hazard, basis risk, credit risk, regulatory uncertainty, etc. The authors introduce some popular securitized solutions and use Merton's structural framework to demonstrate how to value these CAT-linked securities. The hybrid solutions by combining reinsurance with securitized CAT instruments are expected to offer promising applications for CAT risk management.FindingsThe authors introduce some popular securitized solutions and use Merton's structural framework to demonstrate how to value these CAT-linked securities. The hybrid solutions by combining reinsurance with securitized CAT instruments are expected to offer promising applications for CAT risk management.Originality/valueThis research reviews a broad array of impacts of CAT risks on the (re)insurance industry. CAT events challenge (re)insurance capacity and influence insurers' supply decisions and reconstruction costs in the aftermath of catastrophes. While losses from natural catastrophes are the primary threat to property–casualty insurers, the mortality risk posed by influenza pandemics is a leading CAT risk for life insurers. At the same time, natural catastrophes and man-made disasters cause distinct impacts on (re)insures. Man-made disasters can increase the correlation between insurance stocks and the overall market, and natural catastrophes reduce the above correlation. It should be noted that huge CAT losses can also improve (re)insurance demand during the postevent period and thus bring long-term effects to the (re)insurance industry.

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Section: Introductionmentioning

confidence: 94%

Section: Cat Bondmentioning

confidence: 99%

“…(e.g. Lee and Yu, 2002;Braun, 2016;G€ urtler et al, 2016;Zhao and Yu, 2019;Herrmann and Hibbeln, 2021).…”

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Catastrophe risk, reinsurance and securitized risk-transfer solutions: a review

Zhao

Lee

2021

CFRI

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“…The current ratio of single peril to multi-peril CAT bond outstanding is 4:6 (see, e.g.,Herrmann and Hibbeln (2021)). …”

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confidence: 99%

A Unified Bayesian Framework for Pricing Catastrophe Bond Derivatives

Domfeh¹,

Chatterjee²,

Dixon³

2022

Preprint

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Catastrophe (CAT) bond markets are incomplete and hence carry uncertainty in instrument pricing. As such various pricing approaches have been proposed, but none treat the uncertainty in catastrophe occurrences and interest rates in a sufficiently flexible and statistically reliable way within a unifying asset pricing framework. Consequently, little is known empirically about the expected risk-premia of CAT bonds. The primary contribution of this paper is to present a unified Bayesian CAT bond pricing framework based on uncertainty quantification of catastrophes and interest rates. Our framework allows for complex beliefs about catastrophe risks to capture the distinct and common patterns in catastrophe occurrences, and when combined with stochastic interest rates, yields a unified asset pricing approach with informative expected risk premia. Specifically, using a modified collective risk model -Dirichlet Prior-Hierarchical Bayesian Collective Risk Model (DP-HBCRM) framework -we model catastrophe risk via a model-based clustering approach. Interest rate risk is modeled as a CIR process under the Bayesian approach. As a consequence of casting CAT pricing models into our framework, we evaluate the price and expected risk premia of various CAT bond contracts corresponding to clustering of catastrophe risk profiles. Numerical experiments show how these clusters reveal how CAT bond prices and expected risk premia relate to claim frequency and loss severity.

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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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Seasonality in catastrophe bonds and market‐implied catastrophe arrival frequencies

Cited by 16 publications

References 34 publications

Catastrophe risk, reinsurance and securitized risk-transfer solutions: a review

Catastrophe risk, reinsurance and securitized risk-transfer solutions: a review

A Unified Bayesian Framework for Pricing Catastrophe Bond Derivatives

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

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