Duration and the Measurement of Basis Risk

Good index selection is key to minimising basis risk in weather index insurance design. However, interannual, seasonal, and intra-seasonal hydroclimatic variabilities pose challenges in identifying robust proxies for crop losses. In this study, we systematically investigated 574 hydroclimatic indices for their relationships with yield in Malaysia’s irrigated double planting system, using the Muda rice granary as a case study. The responses of seasonal rice yields to seasonal and monthly averages and to extreme rainfall, temperature, and streamflow statistics from 16 years’ observations were examined by using correlation analysis and linear regression. We found that the minimum temperature during the crop flowering to the maturity phase governed yield in the drier off-season (season 1, March to July, Pearson correlation, r = +0.87; coefficient of determination, R2 = 74%). In contrast, the average streamflow during the crop maturity phase regulated yield in the main planting season (season 2, September to January, r = +0.82, R2 = 67%). During the respective periods, these indices were at their lowest in the seasons. Based on these findings, we recommend temperature- and water-supply-based indices as the foundations for developing insurance contracts for the rice system in northern Peninsular Malaysia.

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A Two-Population Mortality Model to Assess Longevity Basis Risk

Risks ◽

10.3390/risks9020044 ◽

2021 ◽

Vol 9 (2) ◽

pp. 44

Author(s):

Selin Özen ◽

Şule Şahin

Keyword(s):

Risk Reduction ◽

Significant Risk ◽

Reference Population ◽

Mortality Data ◽

Basis Risk ◽

Mortality Model ◽

Proposed Model ◽

Population Mortality ◽

Mortality Jumps ◽

The Impact

Index-based hedging solutions are used to transfer the longevity risk to the capital markets. However, mismatches between the liability of the hedger and the hedging instrument cause longevity basis risk. Therefore, an appropriate two-population model to measure and assess longevity basis risk is required. In this paper, we aim to construct a two-population mortality model to provide an effective hedge against the basis risk. The reference population is modelled by using the Lee–Carter model with the renewal process and exponential jumps, and the dynamics of the book population are specified. The analysis based on the U.K. mortality data indicate that the proposed model for the reference population and the common age effect model for the book population provide a better fit compared to the other models considered in the paper. Different two-population models are used to investigate the impact of sampling risk on the index-based hedge, as well as to analyse the risk reduction regarding hedge effectiveness. The results show that the proposed model provides a significant risk reduction when mortality jumps and sampling risk are taken into account.

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Hedging of Variable Annuities under Basis Risk

Asia-Pacific Journal of Risk and Insurance ◽

10.1515/apjri-2019-0040 ◽

2020 ◽

Vol 14 (2) ◽

Author(s):

Jan Bauer

Keyword(s):

Financial Market ◽

Negative Impact ◽

Mathematical Finance ◽

Insurance Company ◽

Hedging Strategy ◽

Variable Annuities ◽

Basis Risk ◽

Hedging Strategies ◽

Cross Hedging ◽

Geometric Brownian Motions

AbstractI study dynamic hedging for variable annuities under basis risk. Basis risk, which arises from the imperfect correlation between the underlying fund and the proxy asset used for hedging, has a highly negative impact on the hedging performance. In this paper, I model the financial market based on correlated geometric Brownian motions and analyze the risk management for a pool of stylized GMAB contracts. I investigate whether the choice of a suitable hedging strategy can help to reduce the risk for the insurance company. Comparing several cross-hedging strategies, I observe very similar hedging performances. Particularly, I find that well-established but complex strategies from mathematical finance do not outperform simple and naive approaches in the context studied. Diversification, however, could help to reduce the adverse impact of basis risk.

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

Weather Climate and Society ◽

10.1175/wcas-d-16-0020.1 ◽

2016 ◽

Vol 8 (4) ◽

pp. 409-419 ◽

Cited By ~ 24

Author(s):

Tobias Dalhaus ◽

Robert Finger

Keyword(s):

Crop Production ◽

Market Failure ◽

Weather Station ◽

Yield Losses ◽

Grid Data ◽

Weather Index ◽

Basis Risk ◽

Promising Alternative ◽

Station Data ◽

Adverse Weather

Abstract 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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A note on currency hedging with basis risk: methodology and application

International Journal of Banking Accounting and Finance ◽

10.1504/ijbaaf.2008.020244 ◽

2008 ◽

Vol 1 (1) ◽

pp. 105 ◽

Cited By ~ 2

Author(s):

Moawia Alghalith

Keyword(s):

Currency Hedging ◽

Basis Risk ◽

Risk Methodology

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Index Insurance Quality and Basis Risk: Evidence from Northern Kenya

American Journal of Agricultural Economics ◽

10.1093/ajae/aaw046 ◽

2016 ◽

Vol 98 (5) ◽

pp. 1450-1469 ◽

Cited By ~ 42

Author(s):

Nathaniel D. Jensen ◽

Christopher B. Barrett ◽

Andrew G. Mude

Keyword(s):

Index Insurance ◽

Basis Risk ◽

Northern Kenya

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Overcoming basis risk in agricultural index insurance using crop simulation modeling and satellite crop phenology

10.5194/egusphere-egu21-9534 ◽

2021 ◽

Author(s):

Mehdi H. Afshar ◽

Timothy Foster ◽

Thomas P. Higginbottom ◽

Ben Parkes ◽

Koen Hufkens ◽

...

Keyword(s):

Smallholder Farmers ◽

Simulation Models ◽

Extreme Weather ◽

Index Insurance ◽

Yield Estimation ◽

Crop Phenology ◽

Yield Data ◽

Basis Risk ◽

Crop Simulation ◽

Crop Simulation Models

Extreme weather causes substantial damage to livelihoods of smallholder farmers globally and are projected to become more frequent in the coming decades as a result of climate change. Index insurance can theoretically help farmers to adapt and mitigate the risks posed by extreme weather events, providing a financial safety net in the event of crop damage or harvest failure. However, uptake of index insurance in practice has lagged far behind expectations. A key reason is that many existing index insurance products suffer from high levels of basis risk, where insurance payouts correlate poorly with actual crop losses due to deficiencies in the underlying index relationship, contract structure or data used to trigger insurance payouts to farmers.&#160;In this study, we analyse to what extent the use of crop simulation models and crop phenology monitoring from satellite remote sensing can reduce basis risk in index insurance. Our approach uses a calibrated biophysical process-based crop model (APSIM) to generate a large synthetic crop yield training dataset in order to overcome lack of detailed in-situ observational yield datasets &#8211; a common limitation and source of uncertainty in traditional index insurance product design. We use this synthetic yield dataset to train a simple statistical model of crop yields as a function of meteorological and crop growth conditions that can be quantified using open-access earth observation imagery, radiative transfer models, and gridded weather products. Our approach thus provides a scalable tool for yield estimation in smallholder environments, which leverages multiple complementary sources of data that to date have largely been used in isolation in the design and implementation of index insuranceWe apply our yield estimation framework to a case study of rice production in Odisha state in eastern India, an area where agriculture is exposed to significant production risks from monsoonal rainfall variability. Our results demonstrate that yield estimation accuracy improves when using meteorological and crop growth data in combination as predictors, and when accounting for the timing of critical crop development stages using satellite phenological monitoring. Validating against observed yield data from crop cutting experiments, our framework is able to explain around 54% of the variance in rice yields at the village cluster (Gram Panchayat) level that is the key spatial unit for area-yield index insurance products covering millions of smallholder farmers in India. Crucially, our modelling approach significantly outperforms vegetation index-based models that were trained directly on the observed yield data, highlighting the added value obtained from use of crop simulation models in combination with other data sources commonly used in index design.

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