Tailored to the extremes: Quantile regression for index‐based insurance contract design

Conradt, Sarah; Finger, Robert; Bokusheva, Raushan

doi:10.1111/agec.12180

Cited by 59 publications

(73 citation statements)

References 67 publications

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“…This flexible index definition was found to be superior to a fixed index definition (Conradt et al 2015b). However, the definition of vegetation phases based on GDD faces several challenges, for example, that assumptions must be made on the occurrence of vegetation periods based on GDD across various crops and varieties used, and that precise knowledge of sowing dates might be required (Conradt et al 2015b). …”

Section: Literature Reviewmentioning

confidence: 91%

“…Only a few studies have incorporated growing seasons explicitly (e.g., Kapphan et al 2012;Leblois et al 2014b;Conradt et al 2015b;Kumar et al 2016). While Kapphan et al (2012) extracted information on vegetation phases from a crop model, Leblois et al (2014a) and Conradt et al (2015b) used growing degree days to specify vegetation periods. Kumar et al (2016) identified heat-sensitive growth stages from experimental data based on observed phenological phases.…”

Section: Literature Reviewmentioning

confidence: 99%

“…1 As an exception, Conradt et al (2015b) used flexible weather index definitions based on vegetation periods defined using growing degree days (GDD), allowing index measurement windows to be specially suited to the vegetation phase and to vary from year to year. This flexible index definition was found to be superior to a fixed index definition (Conradt et al 2015b).…”

Section: Literature Reviewmentioning

confidence: 99%

“…(3) and solve for r R i . We follow Conradt et al (2015a) and estimate the parameters of Eq. (3) using quantile regression (QR).…”

Section: Conceptual Background a Index Designmentioning

confidence: 99%

“…In our analysis we will build upon various contributions to reduce design risk that have been suggested (e.g., Pelka and Musshoff 2013;Conradt et al 2015a). In this study, we further aim to present novel approaches to reduce both spatial and temporal basis risk.…”

Section: Introductionmentioning

confidence: 99%

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Can Gridded Precipitation Data and Phenological Observations Reduce Basis Risk of Weather Index–Based Insurance?

Dalhaus

Finger

2016

Weather, Climate, and Society

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Adverse weather events occurring at sensitive stages of plant growth can cause substantial yield losses in crop production. Agricultural insurance schemes can help farmers to protect their income against downside risks. While traditional indemnity-based insurance schemes need governmental support to overcome market failure caused by asymmetric information problems, weather index–based insurance (WII) products represent a promising alternative. In WII the payout depends on a weather index serving as a proxy for yield losses. However, the nonperfect correlation of yield losses and the underlying index, the so-called basis risk, constitutes a key challenge for these products. This study aims to contribute to the reduction of basis risk and thus to the addition of risk-reducing properties of WII. More specifically, the study tests whether grid data for precipitation (vs weather station data) and phenological observations (vs fixed time windows for index determination) that are provided by public institutions can reduce spatial and temporal basis risk and thus improve the performance of WII. An empirical example of wheat production in Germany is used. No differences were found between using gridded and weather station precipitation, whereas the use of phenological observations significantly increases expected utility. However, even if grid data do not yet reduce basis risk, they enable overcoming several disadvantages of using station data and are thus useful for WII applications. Based on the study’s findings and the availability of these data in other countries, a massive potential for improving WII can be concluded.

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Section: Literature Reviewmentioning

confidence: 91%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

“…(3) and solve for r R i . We follow Conradt et al (2015a) and estimate the parameters of Eq. (3) using quantile regression (QR).…”

Section: Conceptual Background a Index Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Can Gridded Precipitation Data and Phenological Observations Reduce Basis Risk of Weather Index–Based Insurance?

Dalhaus

Finger

2016

Weather, Climate, and Society

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Farm‐level evaluation of area‐ and agroclimatic‐based index insurance

Tsiboe,

Tack,

2023

J of Agr & App Econ Assoc

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We utilize over 190,000 historical farm‐level dryland row‐crop yield observations (corn, sorghum, soybeans, and winter wheat) spanning over 7000 Kansas farms from 1973 to 2018 coupled with agroclimatic variables to assess the performance of a broad range of weather‐ and area‐based insurance products. Results showed substantial levels of basis risk across agroclimatic‐based indices, limited ability to reduce income variability under fair pricing, and underperformance relative to area‐based yield products. Growth‐stage specific heat indices for corn and soybeans may offer an effective risk management tool. Implications in the context of current agricultural policy initiatives and climate change adaptation are discussed.

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Dealing with trend uncertainty in empirical estimates of European rainfall climate for insurance risk management

Jewson

Dallafior

Comola

2021

Meteorological Applications

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The insurance industry uses mathematical models to estimate the risks due to future natural catastrophes. For climate-related risks, historical climate data are a key ingredient used in making the models. Historical data for temperature and sea level often show clear and readily quantified climate change driven trends, and these trends would typically be accounted for when building risk models by adjusting earlier values to render the earlier data relevant to the future climate. For other climate variables, such as rainfall in many parts of the world, the questions of whether there are climate change driven trends in the historical data, and how to quantify them if there are, are less simple to answer. We investigate these questions in the context of European rainfall with a specific focus on how to deal with the uncertainty around trend estimates. We compare 10 empirical methodologies that one might use to model and predict trends, including traditional statistical testing and alternatives to statistical testing based on standard methods from model selection and model averaging. We emphasize prediction and risk assessment, rather than detection of trends, as our goal. Viewed in terms of this goal, the methods we consider each have qualitative and quantitative advantages and disadvantages. Understanding these advantages and disadvantages can help risk modellers make a choice as to which method to use, and based on the results we present, we believe that in many common situations model averaging methods, as opposed to statistical testing or model selection, are the most appropriate.

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Tailored to the extremes: Quantile regression for index‐based insurance contract design

Cited by 59 publications

References 67 publications

Can Gridded Precipitation Data and Phenological Observations Reduce Basis Risk of Weather Index–Based Insurance?

Can Gridded Precipitation Data and Phenological Observations Reduce Basis Risk of Weather Index–Based Insurance?

Farm‐level evaluation of area‐ and agroclimatic‐based index insurance

Dealing with trend uncertainty in empirical estimates of European rainfall climate for insurance risk management

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