Cat Bond Pricing Under a Product Probability Measure With Pot Risk Characterization

Tang, Qihe; Yuan, Zhongyi

doi:10.1017/asb.2019.11

Cited by 28 publications

(39 citation statements)

References 55 publications

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“…VaR) covered by the insurers, we consider a zoning insurance fund for the moderate insurance cover (between 85% VaR and 90% CVaR) in Section 3.2. While for the higher loss level, the insurance companies may generally transfer the larger risk to reinsurance as well as the financial markets in terms of various financial securities ( [14,18]). Thus, in Section 3.3 we investigate the price of flooding catastrophe bond.…”

Section: Resultsmentioning

confidence: 99%

“…Also, some funds can be sent to the sponsor as a reimbursement for the claims. Here we follow the product pricing scheme proposed by [14] together with a compound Poisson trigger process similar to the 2015 Acorn Re earthquake CAT bond. The detailed workflow is presented in Figure 8 .…”

Section: Ranking Of Flooding Vulnerability Via Grey Relational Analys...mentioning

confidence: 99%

“…Extreme precipitations, as one of the extreme weather events, usually come along with management threats such as severe floods. In Asian monsoon season, the precipitations usually will trigger floods, especially in the basin of the largest river in China ( [9], [14]). The historic large floods in China in the past three decades have caused over 200 billion dollars per decade ( [9]).…”

Section: Introductionmentioning

confidence: 99%

“…14) is fitted by the 4 year 3-month China treasury bond rates and 4 year 3-month Shanghai Inter-bank Offered Rate (Shibor). Estimated parameters are put into the pricing measure that combines a distorted pricing measure represent catastrophe insurance risk and risk-neutral pricing measure for the arbitrage financial market.…”

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confidence: 99%

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Extremal Analysis of Flooding Risk and Management

Ling¹,

Li²,

Liu³

et al. 2021

Preprint

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Catastrophic losses caused by natural disasters receive a growing concern about the severe rise in magnitude and frequency. The constructions of insurance and financial management scheme become increasingly necessary to diversify the disaster risks. Given the frequency and severity of floods in China, this paper investigates the extreme analysis of flood-related huge losses and extreme precipitations using Peaks-Over-Threshold method and Point Process (PP) model. These findings are further utilized for both designs of flood zoning insurance and flooding catastrophic bond: (1) Using the extrapolation approach in Extreme Value Theory (EVT), the estimated Value-at-Risk (VaR) and conditional VaR (CVaR) are given to determine the cross-regional insurance premium together with the Grey Relational Analysis (GRA) and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The flood risk vulnerability and threat are analyzed with both the geography and economic factors into considerations, leading to the three layered premium levels of the 19 flood-prone provinces. (2) To hedge the risk for insurers and reinsurers to the financial market, we design a flooding catastrophe bond with considerate trigger choices and the pricing mechanism to balance the benefits of both reinsurers and investors. To reflect both the market price of catastrophe risk and the lowcorrelated financial interest risk, we utilize the pricing mechanism of Tang and Yuan (2021) to analyze the pricing sensitivity against the tail risk of the floods' disaster and the distortion magnitude and the market risk through the distortion magnitude involved in Wang's transform. Additionally, our trigger process is carefully designed using a compound Poisson process modelling both the frequency and the layered intensity of the flood disasters. Finally, constructive suggestions and policies are proposed concerning the flood risk warning and prevention.

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Section: Resultsmentioning

confidence: 99%

Section: Ranking Of Flooding Vulnerability Via Grey Relational Analys...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Extremal Analysis of Flooding Risk and Management

Ling¹,

Li²,

Liu³

et al. 2021

Preprint

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“…The important implication here is that the aggregate loss processes (i.e., intensity and severity of losses) retain their original distributional characteristics after been transformed from the physical probability measure to the risk-neutral measure (Lee and Yu (2002)). A similar argument supported by the zero-risk premium is that catastrophic risk has marginal impact on the overall economy and therefore does not pose a "systematic risk" to the market (Tang and Zhongyi (2019)). However, there is empirical evidence to suggest that catastrophic events may in fact have a substantial systematic impact on the market (see, e.g., Gürtler et al (2016)).…”

Section: Introductionmentioning

confidence: 91%

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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Cat Bond Pricing Under a Product Probability Measure With Pot Risk Characterization

Cited by 28 publications

References 55 publications

Extremal Analysis of Flooding Risk and Management

Extremal Analysis of Flooding Risk and Management

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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