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'Catastrophe Risk Financing in Developing Countries' provides a detailed analysis of the imperfections and inefficiencies that impede the emergence of competitive catastrophe risk markets in developing countries. The book demonstrates how donors and international financial institutions can assist governments in middle- and low-income countries in promoting effective and affordable catastrophe risk financing solutions. The authors present guiding principles on how and when governments, with assistance from donors and international financial institutions, should intervene in catastrophe insurance markets. They also identify key activities to be undertaken by donors and institutions that would allow middle- and low-income countries to develop competitive and cost-effective catastrophe risk financing strategies at both the macro (government) and micro (household) levels. These principles and activities are expected to inform good practices and ensure desirable results in catastrophe insurance projects. 'Catastrophe Risk Financing in Developing Countries' offers valuable advice and guidelines to policy makers and insurance practitioners involved in the development of catastrophe insurance programs in developing countries.

The peaks over threshold (POT) model for catastrophe (CAT) reinsurance pricing has been widely used, but has mainly focused on univariate CAT reinsurance pricing. We provide further justification and support for the model by considering the addition of more than one type of CAT risk in the context of extreme value theory. We further extend the applicability of the CAT reinsurance premium model by considering house damage and deaths as CAT risk. Using the proposed model, we present a simulation framework for pricing double risk CAT reinsurance, based on excess-of-loss reinsurance contract. Furthermore, we fit the POT model to the earthquake loss data in Indonesia. Finally, we provide the price of the double risk CAT reinsurance premium under the standard deviation premium principle. The framework results obtained show that the pricing formulas in this study are appropriate for the double risk claim and may be used as a basis for the pricing of double risk CAT excess-of-loss reinsurance contracts.

Catastrophe reinsurance is an important way to prevent and resolve catastrophe risks. As a consequence, the pricing of catastrophe reinsurance becomes a core problem in catastrophic risk management field. Due to the severity of catastrophe loss, the Peak Over Threshold (POT) model in extreme value theory (EVT) is extensively applied to capture the tail characteristics of catastrophic loss distribution. However, there is little research available on the pricing formula of catastrophe excess of loss (Cat XL) reinsurance when the catastrophe loss is modeled by POT. In the context of POT model, we distinguish three different relations between retention and threshold, and then prove the explicit pricing formula respectively under the standard deviation premium principle. Furthermore, we fit POT model to the earthquake loss data in China during 1990–2016. Finally, we give the prices of earthquake reinsurance for different retention cases. The computational results illustrate that the pricing formulas obtained in this paper are valid and can provide basis for the pricing of Cat XL reinsurance contracts.

We propose an integrated approach straddling the actuarial science and the mathematical finance approaches to pricing a default-risky catastrophe reinsurance contract. We first apply an incomplete-market version of the no-arbitrage martingale pricing paradigm to price the reinsurance contract as a martingale by a measure change, then we apply risk loading to price in—as in the traditional actuarial practice—market imperfections, the underwriting cycle, and other idiosyncratic factors identified in the practice and empirical literatures. This integrated approach is theoretically appealing for its merit of factoring risk premiums into the probability measure, and yet practical for being applicable to price a contract not traded on financial markets. We numerically study the catastrophe pricing effects and find that the reinsurance contract is more valuable when the catastrophe is more severe and the reinsurer’s default risk is lower because of a stronger balance sheet. We also find that the price is more sensitive to the severity of catastrophes than to the arrival frequency; implying (re)insurers should focus more on hedging the severity than the arrival frequency in their risk management programs.

The private health sector was officially recognized in the Republic of Congo over 20 years ago June 6, 1988, establishing the conditions for the independent practice of medicine and the medical-related and pharmaceutical professions. The Congolese government recently expressed its commitment to working with the private health sector in order to strengthen the health system, improve the health of the population and preserve the basic human right to a healthy life through the National Health Care Policy, which it adopted in 2003, the 2007-2011 National Health Development Plan and the 2010 Health Care Services Development Program. Throughout these various documents there is an acknowledgement that the lack of coordination with the private health sector is a weakness of the health system. Nevertheless, the scarcity of information about the private sector in policy and planning documents suggests that the government's engagement with the private health sector is limited. There is no official government policy on the private health sector, or strategies or working plans to encourage cooperation between the public and private sectors. The objective of this assessment was to better determine the role, position, and importance of the private sector within the health system, in order to identify the limitations to its development as well as ways it can be integrated into the efforts to meet the objectives of the Plan national de developpement sanitaire (PNDS) [National Health Development Plan]. The World Bank Group contracted with the Results for Development Institute (R4D, United States) and Health Research for Action (HERA, Belgium) as well as with a team of local consultants, to conduct a 'study of the private health sector in the Republic of Congo.' This study was conducted in close collaboration with the Ministry of Health and Population (MSP), which arranged and oversaw a steering committee consisting of actors from the public and private sectors to facilitate and guide the study. The goal of the study and the workshops was a concrete plan of action for the health sector that could be used by the Congolese government, the private sector in the Republic of Congo, and international development partners. Certain aspects of the action plan should be included in the work programs of the Programme de developpement des services de sante (PDSS) [Health System Development Project] for the years 2011-2013.

This report addresses the large flood exposures of Central Europe and proposes efficient financial and risk transfer mechanisms to mitigate fiscal losses from natural catastrophes. In particular, the Visegrad countries (V-4) of Central Europe, namely, Poland, the Czech Republic, Hungary, and the Slovak Republic, have such tremendous potential flood damages that reliance on budgetary appropriations or even European Union (EU) funds in such circumstances becomes ineffective and does not provide needed cash funds for the quick response and recovery needed to minimize economic disruptions. The report is primarily addressed to the governments of the region, which should build into their fiscal planning the necessary contingent funding mechanisms, based on their exposures. The report is addressed to finance ministries and also to the insurance and securities regulators and the private insurance and capital markets, which may all play a role in the proposed mechanisms. An arrangement using a multi-country pool with a hazard-triggered insurance payout mechanism complemented by contingent financing is proposed, to better manage these risks and avoid major fiscal volatility and disruption.

Pandemic risks, such as Covid-19, are difficult to insure as they are characterized by multiple factor risks and losses and involve different types of businesses and people simultaneously. The scarcity of time series and statistical data prevents insurers from developing correct pricing. We propose a model of catastrophe risk with Non-Damage Business Interruption (NDBI) policies to manage the pandemic risk due to the spread of Covid-19. The model employs a Monte Carlo simulation of different lockdown scenarios: the frequency and severity distributions of losses of Italian SMEs. The main results show the importance of a Covid-19 lockdown exposure NDBI policy, which benefits not only SMEs but also the insurer.

Extreme weather losses are increasing worldwide, driven by a combination of growth in socioeconomic exposure and hazards influenced by climate change. This trend is acutely felt in the insurance and reinsurance sectors, where severe convective storms, floods, wildfires, and tropical cyclones are generating unprecedented and compounding financial burdens. A central challenge is that traditional catastrophe models rely heavily on historical event statistics that may no longer represent current or emerging climate regimes. At the same time, global climate model simulations providing insight into long-term projections are currently available at spatial and temporal scales that are too coarse for risk pricing, solvency assessment, and local adaptation planning. This perspective argues for a more formal integration of high-resolution downscaling, ensemble modeling, and catastrophe risk analysis to bridge this gap. High-resolution downscaling approaches can resolve local-scale peril physical processes—such as those related to wildfire, extreme rainfall, tropical cyclones, and hail—that are largely unresolved by global climate models. Large ensembles provide a probabilistic understanding of hazard sensitivity to internal variability and different forcing pathways, information that aligns naturally with the insurance industry’s need to quantify tail risks. Moreover, closer collaboration between atmospheric scientists, climate modelers, and industry practitioners is essential to ensure that model assumptions, uncertainties, and data requirements are mutually understood. Severe convective storms, the fastest-growing peril in U.S. insurance markets, are used as an illustrative example of where scientific advances and industry needs are rapidly converging. Finally, research priorities and policy implications are outlined to help enhance climate-resilient risk management and close the global protection gap.

Insurers draw on sophisticated models for the probability distributions over losses associated with catastrophic events that are required to price insurance policies. But prevailing pricing methods don’t factor in the ambiguity around model-based projections that derive from the relative paucity of data about extreme events. I argue however that most current theories of decision making under ambiguity only partially support a solution to the challenge that insurance decision makers face and propose an alternative approach that allows for decision making that is responsive to both the evidential situation of the insurance decision maker and their attitude to ambiguity.

Romania is highly exposed to seismic risk, with significant implications for residential earthquake insurance and risk-transfer mechanisms, due to the Vrancea intermediate-depth seismic source and a vulnerable building stock. This paper presents a harmonised seismic exposure model for the Romanian residential sector, developed to support probabilistic seismic risk assessment and catastrophe modelling for (re)insurance applications. The model integrates official data from the 2021 Population and Housing Census with the nationally adopted RTC-10 structural typology, height classification, seismic code level, and standardised reconstruction cost indicators. The results indicate that nearly 70% of residential dwellings were constructed before 1990 under pre-code or low- to moderate-code seismic design provisions. Although individual houses dominate the dwelling stock, multi-family apartment buildings concentrate approximately 40% of the total residential replacement cost, particularly in urban areas. The total replacement cost of the residential building stock is estimated at approximately EUR 709 billion, exceeding values derived from global exposure models. Comparison with existing insurance coverage highlights a substantial protection gap between potential seismic losses and insured values. The proposed exposure model provides a transparent, nationally calibrated basis for seismic loss estimation, portfolio accumulation analysis, and evidence-based risk management in both engineering and (re)insurance contexts.