CAT Bonds Research Articles

Superior forecasting with simple AR(1) models in a low-volatility environment: evidence from the CAT bond market

AbstractIn the recent literature on asset pricing, advanced machine learning methods often show better predictive quality than simple linear regression models. In this context, machine learning prediction models for bond premiums are usually based on those predictors that have proved to be particularly relevant in explanatory models. However, these models do not take into account that historical premiums of assets with particularly low premium volatilities already contain a high degree of information about future premiums. With this in mind, we consider catastrophe bonds, whose secondary market premiums exhibit low volatility, and include historical premiums in our forecasting models in addition to the usual predictors. In this way, the predictive accuracy of the linear regression is significantly increased and is comparable to that of advanced machine learning methods. Remarkably, a simple linear AR(1) model without additional predictors achieves the highest predictive performance.

Earthquake Insurance via CAT Bonds Utilizing Autoregressive Neural Networks and Active Faults

Earthquake Insurance via CAT Bonds Utilizing Autoregressive Neural Networks and Active Faults

Optimal control under uncertainty: Application to the issue of CAT bonds

We propose a general framework for studying optimal issue of CAT bonds in the presence of uncertainty on the parameters. In particular, the intensity of arrival of natural disasters is inhomogeneous and may depend on unknown parameters. Given a prior on the distribution of the unknown parameters, we explain how it should evolve according to the classical Bayes rule. Taking these progressive prior-adjustments into account, we characterize the optimal policy through a quasi-variational parabolic equation, which can be solved numerically. We provide examples of application in the context of hurricanes in Florida.

Effect of earthquake sequences on risk-based catastrophe bond pricing.

Catastrophe bonds (cat bond in short) are an alternative risk-transfer instrument used to transfer peril-specific financial risk from governments, financial institutions, or (re)insurers, to the capital market. Current approaches for cat bond pricing are calibrated on seismic mainshocks, and thus do not account for potential effects induced by earthquake sequences. This simplifying assumption implies that damage arises from mainshocks only, while aftershocks yield no damage. Postearthquake field surveys reveal that this assumption is inaccurate. For example, in the 2011 Christchurch Earthquake sequence and 2016-2017 Central Italy Earthquake sequence, aftershocks were responsible for higher economic losses when compared to those caused by mainshocks. This article proposes a time-dependent aggregate loss model that takes into account seismicity clustering and damage accumulation effects in the computation of damage. The model is calibrated on the seismic events recorded during the recent 2016-2017 Central Italy Earthquake sequence. Furthermore, the effects of earthquake sequence on cat bond pricing is explored by implementing the proposed model on five Italian municipalities. The investigation showed that neglecting time-dependency may lead to higher difference (up to 45%) in the cat bond price when compared to standardapproaches.

Climate Change Impact Assessment and Disaster Risk Financing Strategies in Mali: A Comprehensive Analysis of Drought and Flood Events

Climate change impact increasingly led to humanitarian assistance increase and needed. To better address climate change impacts mitigation, a suitable financing instrument is essential to facilitate government, humanitarian, and other stakeholders' finance mobilization. However, few studies have been done on disaster risk profiling to guide decision-makers in their choices. Disaster risk profile analysis has been conducted in Mali to facilitate financial resources mobilization and climate finance instrument choice by identifying Historical drought and flood events. To do so, published papers; and some international institution websites dealing with climate hazard events such as Reliefweb, Hazard/risk, climate information services, Relief Web; CRED- EM-DAT; World Bank Climate Knowledge Portal, World Bank- UNDRR -ThinkHazard, WFP ARC/GeoNode/VAM/DataViz addressing Mali country have been assimilated for evidence accumulation and synthesis and presented in a database. Furthermore, national statistics and national reports on hazards have been also reviewed. Before starting the reading exercise, a reading/analytical framework has been elaborated. In Mali the year 1984 to 2019, 21 flood events occurred in Mali and the most exposed regions to floods are Koulikoro, Bamako and Gao. From 1969 to 2020, 21 drought events were registered in in Mali and the most affected regions are Koulikoro, Mopti, Gao, Kayes. The time return period of drought has been estimated to 3 years while the time return of flood has been estimated to be about 2 years. Drought events are less frequent than flood events, however, drought events affect more population than floods. Also, Also, the yearly response cost for drought events is USD million 277.46 with an average US USD 204.37 cost per affected population. For the flood, even the yearly response cost is USD 11.107 million With USD 261.82 per affected population. Macro-insurance and CAT bonds are more suitable disaster risk financing instruments and are recommended to better address drought events while Anticipatory action and government contingency funds are more suitable climate disaster risk financing instruments to better address flood events in Mali.

Hedging climate change risks in Southern Africa's agricultural industry using catastrophe bonds.

The paper shows how CAT bonds can be employed to hedge against climate change risks in agricultural production and to increase (re)insurers´ underwriting capacity. It further discusses the attractiveness of CAT bonds as another investment option for agricultural investors and how to develop and institutionalise a CAT bond market.

Pricing Cat Bonds: Regressions and Machine Learning -Some observations, some lessons

Pricing Cat Bonds: Regressions and Machine Learning -Some observations, some lessons

CAT Bonds: A Suitable Systemic Approach for Handling Catastrophic Risks in the Czech Republic?

CAT Bonds: A Suitable Systemic Approach for Handling Catastrophic Risks in the Czech Republic?

Catastrophe (CAT) bonds:

Catastrophe bonds, a relatively new entry into the bond market, are a form of reinsurance in which insurance firms are able to offset the financial risks from both natural and man-made catastrophes. Although the primary offset is within the reinsurance market, starting in the 1990s insurance firms started using the financial markets to offset catastrophe risks. Anecdotal evidence shows that the entry of CAT bonds made the reinsurance market more efficient and allowed investors an opportunity to participate in what has been a very profitable investment opportunity. Our analysis shows that on average, CAT bonds have generated high returns but with the advantage of diversification when compared with similarly rated corporate debt. Thus, CAT bonds are a viable investment option withina diversified portfolio.

Pricing Pandemic Bonds under Hull–White & Stochastic Logistic Growth Model

Pandemic bonds can be used as an effective tool to mitigate the economic losses that governments face during pandemics and transfer them to the global capital market. Once considered as an “uninsurable” event, pandemic bonds caught the attention of the world with the issuance of pandemic bonds by the World Bank in 2017. Compared to other CAT bonds, pandemic bonds received less attention from actuaries, industry professionals, and academic researchers. Existing research focused mainly on how to bring epidemiological parameters to the pricing mechanism through compartmental models. In this study, we introduce the stochastic logistic growth model-based pandemic bond pricing framework. We demonstrate the proposed model with two numerical examples. First, we calculate what investor is willing to pay for the World Bank issued pandemic bond while accounting for possible future pandemic, but require to have the same yield to maturity when no pandemic is there, and without using COVID-19 data. In the second example, we calculate the fair value of a pandemic bond with characteristics similar to the World Bank issued pandemic bond, but using COVID-19 data. The model can be used as an alternative to epidemic compartmental model-based pandemic bond pricing mechanisms.

Pricing Multi-Event-Triggered Catastrophe Bonds Based on a Copula–POT Model

The constantly expanding losses caused by frequent natural disasters pose many challenges to the traditional catastrophe insurance market. The purpose of this paper is to develop an innovative and systemic trigger mechanism for pricing catastrophic bonds triggered by multiple events with an extreme dependence structure. Due to the bond’s low cashflow contingencies and the CAT bond’s high return, the multiple-event CAT bond may successfully transfer the catastrophe risk to the huge financial markets to meet the diversification of capital allocations for most potential investors. The designed hybrid trigger mechanism helps reduce the moral hazard and increase the bond’s attractiveness with a lower trigger likelihood, displaying the determinants of the wiped-off coupon and principal by both the magnitude and intensity of the natural disaster events involved. As the trigger indicators resulting from the potential catastrophic disaster might be associated with heavy-tailed margins, nested Archimedean copulas are introduced with marginal distributions modeled by a POT-GP distribution for excess data and common parametric models for moderate risks. To illustrate our theoretical pricing framework, we conduct an empirical analysis of pricing a three-event rainstorm CAT bond based on the resulting losses due to rainstorms in China during 2006–2020. Monte Carlo simulations are carried out to analyze the sensitivity of the rainstorm CAT bond price in trigger attachment levels, maturity date, catastrophe intensity, and numbers of trigger indicators.

Providing pandemic business interruption coverage with double trigger cat bonds.

The aim of this paper is to show how qualified investors in cat bonds can offer adequate pandemic business interruption protection in a comprehensive public-private coverage scheme. First, we propose a numerical model to expose how cat bonds can contribute to complement standard re/insurance by improving coverage of cedents even though risks are positively correlated during a pandemic. Second, we introduce double trigger pandemic business interruption cat bonds, which we name PBI bonds, and discuss their precise characteristics to provide efficient coverage. A first trigger should be pulled when the World Health Organization declares a Public Health Emergency of International Concern (PHEIC). The second trigger determines the payout of the bond based on the modelised business interruption losses of an industry in a country. We discuss moral hazard, basis risk, correlation and liquidity issues which are critical in the context of a pandemic. Third, we simulate the life of theoretical PBI bonds in the restaurant industry in France by using data gathered during the COVID-19 pandemic.

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

AbstractThe 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.

A CAT bond-based coverage scheme proposal for Italy

Catastrophe bonds (CAT bonds) are risk-linked securities used by the insurance industry to transfer risks associated with the occurrence of natural disasters to the capital markets. Despite their growing importance, relatively few studies on CAT bond pricing, design and their application are available in the literature. Indeed, existing pricing formulations for pricing analysis do not account for uncertainties in model parameters and are not contextualized in a more general CAT bond coverage design procedure for an area of interest with a distributed portfolio. For these reasons, this paper presents a general procedure for designing a CAT bond-based coverage for a spatially distributed portfolio against losses due to natural hazards. The procedure is then applied to a case study represented by the residential building portfolio in Italy, aiming to design a CAT bond-based coverage scheme against losses induced by seismic events all over the entire national borders.

On the Potential of Sovereign State-Contingent Debt in Contributing to Better Public Debt Management and Enhancing Sustainability Outcomes

AbstractSovereign state-contingent instruments (SCDIs) have been suggested as complements or alternatives to traditional sovereign debt instruments for a long time, but with little uptake. Markets for SCDIs have suffered from low liquidity and issues around measurement. This article argues that the escalating climate and ecological crises provide a strong rationale to reconsider the use of sovereign SCDIs as the physical and transition impacts of climate change and environmental degradation are increasingly altering the risk profile of sovereigns. The use of risk-linked sovereign instruments such as cat bonds or resilience bonds and embedding disaster risk clauses in sovereign debt contracts would be an important way for governments, especially in highly climate-vulnerable countries, to mitigate climate risks and scale up investment in resilience. Moreover, instruments such as sustainability-linked bonds that incentivise sustainability-oriented policies and investments could help to bring about better sustainability outcomes and contribute to greater debt sustainability. SCDIs can also play an important role in facilitating debt restructurings. The international community, supported by key institutions like the IMF and the major multilateral development banks, should make a concerted effort to promote the widespread adoption of sovereign SCDIs to support better public debt management, the climate-proofing of public finances, and the achievement of more ambitious sustainability outcomes.

Securitizing pandemic‐risk insurance

AbstractCatastrophe (CAT) bonds are a means to share economic losses resulting from natural disasters including hurricanes, earthquakes, and—more recently, as used by the World Bank—pandemics. Because these pandemic CAT (PCAT) bonds were subsidized by government donations and, absent the occurrence of covered pandemics, were full recourse to the World Bank, this precedent was not market‐tested for the commercial viability of such instruments. This paper examines PCAT bonds as a means of securitization for pandemic risk through the lenses of reinsurers′ unmet capital needs and the requirements for the potential viability of a PCAT‐bond market. Where the reinsurance market has limited capacity to either absorb or spread the risks of global‐level CATs, risk securitization may be effective for layered risk sharing. The authors explore whether pandemic risk may be insurable by increasing reinsurance capacity to handle losses from business interruptions that are due to unintentional pandemics. Are PCAT bonds a potential means to achieving this protection? Historically, insurers were reluctant to enter this market because the required spreads and associated bond‐issuance expenses were considered prohibitively high. The situation has improved, although there remains much room and need for growth in this market.

Simulating and Pricing CAT Bonds Using the Spectral Method Based on Chebyshev Basis

Simulating and Pricing CAT Bonds Using the Spectral Method Based on Chebyshev Basis

Pricing Cat Bonds for Cloud Service Failures

The use of the cloud to store personal/company data and to run programs is gaining wide acceptance as it is more efficient and cost-effective. However, cloud services may not always be available, which could lead to losses for customers and the cloud provider (the provider is typically obligated to compensate its customers). It can protect itself from such losses through insurance, which transfers the risk to the insurer. In the case of poor cloud availability, the amount that the insurer has to pay back to the cloud provider may become so high that it endangers the insurer’s financial solvency. We propose the use of cat bonds as reinsurance tools as well as the Nowak–Romaniuk pricing scheme. The outage frequency was described by the Poisson process and the loss severity was described by a Pareto random variable; we derived a closed formula for the price of a cat bond in a stochastic interest rate environment, using both one-factor and two-factor short-rate models. We demonstrated the applicability of our pricing formula in a real context.

Trading and liquidity in the catastrophe bond market

AbstractWe provide first insights into secondary market trading, liquidity determinants, and the liquidity premium of catastrophe bonds. Based on transaction data from TRACE (Trade Reporting and Compliance Engine), we find that cat bonds are traded less frequently during the hurricane season and more often close to maturity. Trading activity indicates that the market is dominated by brokers without a proprietary inventory. Liquidity is high in periods of high trading activity in the overall market and for bonds with low default risk or close to maturity, which results from lower order processing costs. Finally, using realized bid–ask spreads as a liquidity measure, we find that on average, 21% of the observable yield spread on the cat bond market is attributable to the liquidity premium, with a magnitude of up to 141 bps for high‐risk bonds.

Application of Compound Poisson Process in Pricing Catastrophe Bonds: A Systematic Literature Review

The compound Poisson process (CPP) is often used in catastrophe risk modeling, for example, aggregate loss risk modeling. Hence, CPP can be involved in pricing catastrophe bonds (CAT bonds) because it requires a catastrophe risk modeling method. However, studies of how the application of CPP in pricing CAT bonds is still scarce. Therefore, this study aims to conduct a systematic literature review (SLR) on how CPP is used in pricing CAT bonds. The SLR consists of three stages: the literature selection, bibliometric analysis, and gap analysis. At the literature selection stage, the 30 articles regarding the application of CPP in pricing CAT bonds are obtained. Then, the conceptual and nonconceptual structures of the articles are mapped at the bibliometric analysis stage. Finally, in the gap analysis stage, the application of CPP in pricing CAT bonds from the previous studies is analyzed, and new research opportunities are studied. This research can be a reference for researchers regarding the application of CPP in pricing CAT bonds and can motivate them to design more beneficial ways of pricing CAT bonds with CPP in the future.