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Pension funds and insurers face difficulties in hedging their longevity risk, which is the uncertainty of how long their clients will live. A possible solution could be using longevity-linked securities to transfer some of this risk to other parties. However, these securities may not match the actual mortality rates of the insurer’s clients, resulting in a potential loss due to basis risk. In this paper, we measure this basis risk through the pricing of a longevity derivative under Solvency II. We also compare this method with other common pricing methods in finance. We explore and evaluate different hedging strategies for insurers, using a multi-population model derived from a two-dimensional Hull and White model that captures the dynamics of mortality over time.

Purpose - The purpose of this paper to develop an empirical methodology for managing spatial basis risk in weather index insurance by studying the fundamental causes for differences in weather risk between distributed locations.Design methodology approach - The paper systematically compares insurance payouts at nearby locations based on differences in geographical characteristics. The geographic characteristics include distance between stations and differences in altitude, latitude, and longitude.Findings - Geographic differences are poor predictors of payouts. The strongest predictor of payout at a given location is payout at nearby location. However, altitude has a persistent effect on heat risk and distance between stations increases payout discrepancies for precipitation risk.Practical implications - Given that payouts in a given area are highly correlated, it may be possible to insure multiple weather stations in a single contract as a \"risk portfolio\" for any one location.Originality value - Spatial basis risk is a fundamental problem of index insurance and yet is still largely unexplored in the literature.

The number of index insurance pilots in developing countries has grown tremendously in recent years, but there has been little progress in our understanding of the quality of those products. Basis risk, or remaining uninsured risk, is a widely recognized but rarely measured feature of index insurance product quality. This article uses eight semi-annual seasons of longitudinal household data to examine the distribution of basis risk associated with an index-based livestock insurance (IBLI) product in northern Kenya. We find that IBLI coverage reduces exposure to covariate risk due to large shocks and mitigates downside risk substantially for many households, even at commercial premium rates. But index insurance is no magic bullet; insured households continue to face considerable basis risk. Examining the components of basis risk, we find that IBLI reduces exposure to covariate risk due to high loss events by an average of 63%. The benefits of reduced covariate risk exposure are relatively small, however, due to high exposure to seemingly mostly random idiosyncratic risk, even in this population often thought to suffer largely from covariate shocks. The result is that IBLI policyholders are left with an average of 69% of their original risk due to high loss events. This research underscores the need for caution when promoting index insurance as a risk mitigation tool, as well as the importance of product quality evaluation.

In the last decade, the index-based weather products (also called weather derivatives) have been gaining attention in the climate resilience discussion. Weather derivatives are designed to help companies hedging against climate variability. These products, that can be market-traded or over-the-counter, compensate individuals based on a pre-defined weather index. Thus, pay-offs of a weather derivative depend on a weather index and not, as with traditional types of insurance, on the actual amount of money lost due to adverse weather. One of the major drawbacks that may prevent weather derivatives to catch on is the impact of the Geographical Basis Risk (GBR), that is the deviation of weather conditions at different locations. In fact, when the reference weather station is not located in the immediate vicinity of the site of interest the hedging effectiveness may be reduced. In this paper, we contribute to the existing literature on GBR by proposing an optimization method that may help in offering a tailored solution, while at the same time keeping a standardized instrument as a reference. Using a historical record of Italian temperatures, strikes for temperatures are the choice variables of a penalty function containing pay-offs of a reference station and all other stations. Further, altitude and latitude of meteorological stations are shown to be relevant predictors to explain GBR. This can be an interesting starting point for the design of weather derivatives, since, from a unique station where the “reference” derivative is priced, all the other stations may be easily settled.

'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.

This article analyzes the demand for a simple rainfall-based weather insurance product among farmers in rural India. We explore the predictions of a standard expected utility theory framework on the nature of demand in terms of price, the basis of the hedge, and risk aversion using data from a randomized control trial. We find that demand behaves as predicted: it falls with price and basis risk and is hump-shaped in risk aversion, with price sensitivity decreasing at higher levels of basis risk. We estimate a negative price elasticity of 0.58 and find that doubling the distance to a reference weather station decreases demand by 18%. These results indicate that improving pricing and quality of insurance products can directly increase demand. In addition, we examine the impact of insurance training relative to other mechanisms designed to increase understanding. The evidence suggests that increased incentives to learn or learning by using are more effective at increasing both understanding and demand. Finally, we contribute to the scarce evidence on the demand for insurance over time. In terms of our main interventions, we find that the effect of premium subsidies persists over time, while the impact of investments in new weather stations diminishes and the effect of increased training in the first season seems to disappear during the second season. Importantly, while having previously purchased insurance does not encourage future uptake, receiving a payout does. This could reflect issues of trust in the product or the insurance company, and constitutes an important topic for future research.

This study proposes an optimization-based weather-yield model to reduce the basis risk of weather-based index insurance. This weather-yield model helps us capture the growing season's monthly variation as it involves monthly explanatory weather indices. In addition, it can capture additional extreme weather effects by including extreme cooling or heating weather indices. This study presents an innovative machine learning framework incorporating optimization approaches to ensure the parsimony of weather index models and the accuracy of crop yield predictions, which can be integrated into the conventional policy design and pricing process. The advantages of this modeling approach and the effectiveness of weather index-based insurance based on this approach in reducing basis risk and revenue risk are demonstrated by applying county-level yield data for mid-season rice in the Anhui province, China.

Weather index insurance allows small-scale farmers to secure income fluctuations caused by adverse weather conditions. However, basis risk, which occurs when the insurance-triggering weather index poorly correlates with the actual yield losses, hinders the demand for such insurance. A higher yield aggregation might reduce basis risk by offsetting localised weather impacts or increase it by oversmoothing. Meanwhile, spatially interpolated rainfall data might reduce basis risk by improving index accuracy or reduce it by introducing interpolation errors. The literature evidence is inconclusive and limited, particularly for small-scale tropical agriculture systems. This work thus aimed to assess the potential risk reduction of hypothetical weather index insurance products for rice production in north-western Peninsular Malaysia, considering both yield aggregation and rainfall interpolation effects. Our results indicated that yield averaged at smaller geographical units produced better correlations with indices and a higher risk reduction potential in only one of four sub-areas studied. Furthermore, the value of interpolated rainfall was only evident at a higher yield aggregation level. These findings underscore the complex interplay between yield aggregation, rainfall interpolation, and basis risk, emphasising the need for context-specific approaches to designing effective weather index insurance products.

Transmission congestion can cause a divergence between wholesale power prices at the individual pricing nodes where power is generated and the more-liquid trading hubs where that power is often delivered and sold. This nodal price difference is commonly referred to as the “locational basis” (or just “basis”). Because the basis varies over time, it can—if not hedged—unpredictably affect a wind plant’s revenue and/or value, which increases investor risk and potentially slows deployment. We find wind plants typically face a larger and more-negative basis than do thermal generators, and hence are more-negatively impacted by congestion. Moreover, while most thermal generators can effectively hedge basis risk by purchasing conventional fixed-volume financial transmission rights (FTRs), these fixed-volume FTRs do not effectively hedge basis risk for variable wind generation. More-effective hedging mechanisms may be required to support those generators most-impacted by congestion, and to promote continued investment in variable generation resources in congested markets.

Longevity swaps have been one of the major success stories of pension scheme de-risking in recent years. However, with some few exceptions, all of the transactions to date have been bespoke longevity swaps based upon the mortality experience of a portfolio of named lives. In order for this market to start to meet its true potential, solutions will ultimately be needed that provide protection for all types of members, are cost effective for large and smaller schemes, are tradable, and enable access to the wider capital markets. Index-based solutions have the potential to meet this need; however, concerns remain with these solutions. In particular, the basis risk emerging from the potential mismatch between the underlying forces of mortality for the index reference portfolio and the pension fund/annuity book being hedged is the principal issue that has, to date, prevented many schemes progressing their consideration of index-based solutions. Two-population stochastic mortality models offer an alternative to overcome this obstacle as they allow market participants to compare and project the mortality experience for the reference and target populations and thus assess the amount of demographic basis risk involved in an index-based longevity hedge. In this paper, we systematically assess the suitability of several multi-population stochastic mortality models for assessing basis risks and provide guidelines on how to use these models in practical situations paying particular attention to the data requirements for the appropriate calibration and forecasting of such models.