Basis Of Risk Research Articles

Quantifying basis risk associated with supplemental area-based crop insurance

Quantifying basis risk associated with supplemental area-based crop insurance

Basis risk reduction in weather index insurance for rice production in North-West Malaysia

Basis risk reduction in weather index insurance for rice production in North-West Malaysia

The demand for hedging of oil producers: A tale of risk and regret

Rationalizing the relatively low levels of hedging observed in the oil market, compared to those predicted by pure risk minimization, has proven difficult. This article examines whether the objectives of oil producers can explain this discrepancy. From a theoretical perspective, it appears that the observed level of hedging is well explained by risk averse producers who also exhibit regret aversion towards potential losses in the derivatives market. When applying our models to the data, we find that regret effectively rationalizes producers' under-hedging and its persistence. Our results suggest that neither ambiguity surrounding basis risk, nor prospect theory can account for this behavior. Lastly, our findings indicate that relaxing the assumption of market completeness and considering quantity risk also fail to match the observed hedging activity of oil producers.

Preferences for index-based pasture insurance: a choice experiment in Limpopo Province, South Africa

ABSTRACT South African subsistence livestock farmers rely heavily on traditional mitigation and coping mechanisms to deal with the effects of drought. However, these methods have proven ineffective in managing the full impact of drought. Consequently, policymakers are increasingly interested in promoting Index-based Pasture Insurance (IBPI). This paper aims to assess subsistence livestock farmers' preferences for IBPI. A simple random sampling method was used to select 110 subsistence livestock farmers for data collection. A discrete choice experiment—Conditional Logit and Latent Class models and incentivized lottery games were used to elicit preferences for insurance contracts and loss aversion. The findings indicate that subsistence livestock farmers have a favourable attitude toward IBPI contracts that protect against drought-related pasture degradation. The Conditional Logit model shows that farmers prefer transparent contracts that reimburse with feed and vouchers rather than cash. However, they derive negative marginal utility from basis risk and premium. However, the Latent Class model reveals heterogeneous preferences for IBPI among farmers. Farmers are also loss-averse, but loss aversion did not influence their preference for IBPI. Therefore, the primary recommendation for insurance providers is to consider customizing IBPI attributes to increase adoption among subsistence farmers.

Investor Sentiment, Unexpected Inflation, and Bitcoin Basis Risk

ABSTRACTThe introduction of regulated CME futures contracts on Bitcoin in 2017 raised an expectation that cryptocurrencies would become part of mainstream financial markets. This also heightened links between traditional markets and Bitcoin, implying that the cryptocurrency would be subject to systematic spillovers. This paper uses high‐frequency data to examine whether Bitcoin basis risk is linked to investor sentiment from established financial markets. Our findings indicate that extreme investor sentiment, as reflected by the tail risk in various volatility indices, including the VIX, consistently correlates with a negative Bitcoin basis, where Bitcoin futures prices are lower than spot prices. Fluctuations significantly influence this relationship in the trading volume of Bitcoin futures and are more pronounced during periods of substantial unexpected inflation and deflation. These results underline the complex dynamics between market sentiment and cryptocurrency pricing, offering insights with substantial implications for investors and policymakers.

Leveraging browse and grazing forage estimates to optimize index-based livestock insurance

African pastoralists suffer recurrent droughts that cause high livestock mortality and vulnerability to climate change. The index-based livestock insurance (IBLI) program offers protection against drought impacts. However, the current IBLI design relying on the normalized difference vegetation index (NDVI) may pose limitation because it does not consider the mixed composition of rangelands (including herbaceous and woody plants) and the diverse feeding habits of grazers and browsers. To enhance IBLI, we assessed the efficacy of utilizing distinct browse and grazing forage estimates from woody LAI (LAIW) and herbaceous LAI (LAIH), respectively, derived from aggregate leaf area index (LAIA), as an alternative to NDVI for refined IBLI design. Using historical livestock mortality data from northern Kenya as reference ground dataset, our analysis compared two competing models for (1) aggregate forage estimates including sub-models for NDVI, LAI (LAIA); and (2) partitioned biomass model (LAIP) comprising LAIH and LAIW. By integrating forage estimates with ancillary environmental variables, we found that LAIP, with separate forage estimates, outperformed the aggregate models. For total livestock mortality, LAIP yielded the lowest RMSE (5.9 TLUs) and higher R2 (0.83), surpassing NDVI and LAIA models RMSE (9.3 TLUs) and R2 (0.6). A similar pattern was observed for species-specific livestock mortality. The influence of environmental variables across the models varied, depending on level of mortality aggregation or separation. Overall, forage availability was consistently the most influential variable, with species-specific models showing the different forage preferences in various animal types. These results suggest that deriving distinct browse and grazing forage estimates from LAIP has the potential to reduce basis risk by enhancing IBLI index accuracy.

Spatial natural hedging: a general framework with application to the mortality of U.S. states

It is well known that coupling life and death benefits within an insurance portfolio may be a beneficial longevity risk reduction technique, especially when policies are underwritten in the same geographical region. However, though desirable, the lack of available capacity of life insurance instruments in terms of underlying cohorts or duration of products underwritten within a given region can substantially constrain the use of natural hedging strategies for life insurance companies. That is why the primary objective of this paper is to investigate the implementation and effectiveness of natural hedging strategies when considering the geographical or spatial dimension. Starting from a well-known multi-population mortality model, we evaluate the relevance of natural hedging strategies and their susceptibility to basis risk resulting from age, period, and spatial effects. Our novel theoretical findings provide direct insights into specific and often complex positions necessary for optimal real-world hedging. In a practical numerical application predicated on U.S. mortality data, we demonstrate the situation of a U.S.-based insurance company capable of selling policies across different states. Though often unable to curtail product sales, an insurance company using our analytical tool can effectively, through marketing strategies, stimulate or destimulate sales to approach an optimal hedging position of an overall portfolio.

Typology and Design of Parametric Cat-in-a-Box and Cat-in-a-Grid Triggers for Tropical Cyclone Risk Transfer

The insurance industry has used parametric solutions to transfer catastrophe risks since the 1990s. Instead of relying on a lengthy process to assess a claim, these products pay the insured a pre-agreed amount if the physical characteristics of the event fulfill pre-defined conditions. Cat-in-a-box or cat-in-a-circle triggers, commonly used tools for tropical cyclone risk transfer, provide a payout to the insured if the track of a hurricane crosses the perimeter of a geographic area defined by a polygon or a circle with a certain intensity. Cat-in-a-grid solutions are novel and more sophisticated. They rely on a set of multiple cat-in-a-box triggers arranged on an orthogonal grid. The consideration of multiple geographic domains instead of a single box or circle is helpful to reduce basis risk, i.e., the difference between the parametric loss estimate and the target loss. In the case study for Miami presented here, for instance, a cat-in-a-grid solution showed 18.5% less basis risk than a typical cat-in-a-box alternative. To organize the different types of triggers within a common framework, we classify the existing alternatives based on whether they use a single geographic domain (like a box or a circle) or multiple domains (like a grid). We discuss their advantages and disadvantages and describe the process required to calibrate any one solution with the help of a catastrophe-risk model. We focus, in particular, on the analysis and construction of cat-in-a-grid triggers, the alternative that we believe offers the greatest potential for global standardization and adoption.

Two-Population Mortality Forecasting: An Approach Based on Model Averaging

The analysis of residual life expectancy evolution at retirement age holds great importance for life insurers and pension schemes. Over the last 30 years, numerous models for forecasting mortality have been introduced, and those that allow us to predict the mortality of two or more related populations simultaneously are particularly important. Indeed, these models, in addition to improving the forecasting accuracy overall, enable evaluation of the basis risk in index-based longevity risk transfer deals. This paper implements and compares several model-averaging approaches in a two-population context. These approaches generate predictions for life expectancy and the Gini index by averaging the forecasts obtained using a set of two-population models. In order to evaluate the eventual gain of model-averaging approaches for mortality forecasting, we quantitatively compare their performance to that of the individual two-population models using a large sample of different countries and periods. The results show that, overall, model-averaging approaches are superior both in terms of mean absolute forecasting error and interval forecast accuracy.

Macroeconomic uncertainty and earnings management: evidence from commodity firms

This study examines the relationship between macroeconomic uncertainty and earnings management, using quarterly data of US commodity firms from the period 1990–2019. The findings show that oil and iron firms use both accruals and real activities to decrease earnings in quarters with high basis risk. Earnings management is economically significant. Further investigation provides fine-grained evidence that specific types of uncertainty (economic policy, climate policy, geopolitical) have varying effects on earnings management. The study also provides evidence that earnings management is aimed at giving investors useful information about the firms’ performance during uncertain times. The study contributes to previous research on uncertainty and earnings management. It also informs market participants about the financial reporting quality of commodity firms, and has practical implications for financial reporting regulation in extracting industries.

Apple farmer’s willingness to pay for RWBCIS: Determinants and empirical evidences from Jammu and Kashmir, India

Climate-induced extreme events are increasingly affecting farmers’ livelihoods through weather-related losses such as flash floods, hailstorms, and rising temperatures. In response to these challenges, the Central Government has introduced the voluntary Restructured Weather Based Crop Insurance Scheme (RWBCIS) as an effective risk management strategy. Under this scheme, farmers are required to pay a premium of 5 percent of the total compensation to avail the benefits. Apple, a major commercial crop in the north-western Himalayas, contributes 70 percent of India’s total apple production, with a significant share attributed to the Jammu and Kashmir Union Territory. This study aims to explore whether apple growers are willing to pay for RWBCIS and to identify the drivers influencing their decision to purchase such insurance products. Data were collected from 900 apple growers in the purposively selected districts of Baramulla and Budgam in Jammu and Kashmir. The findings indicate that while all farmers were willing to adopt the weather-based crop insurance scheme, the majority were unwilling to pay the current premium. The estimated mean willingness to pay (WTP) was Rs. 37.22 per tree without covariates and Rs. 17 with covariates, compared to the prevailing Rs. 53 in the neighboring state of Himachal Pradesh. Factors contributing to the low WTP included a lack of knowledge about the scheme’s modalities, basis risk, and distrust in both government and private insurance companies. The government should consider innovative methods to provide existing subsidies to achieve the targeted coverage of 25 percent of farmers under the scheme.

Application of Index Insurance in Iran's Agriculture: case of wheat growers.

Drought-induced risk endangers farmers in arid and semi-arid regions. Insurance is recognized as an appropriate policy alternative to support farmers facing with financial losses associated with production reduction. In this context, present study developed an ex ante index-based insurance program to deal with drought-induced risk of production losses. We applied this model to wheat growers in Iran. After the calibration of the contract parameters, an insurance scheme was optimized and tested. We showed that optimal insurance contracts generate low gain of certain equivalent income, high compensation, and a high basis risk. The best contract was not proportional to the complexity of the proposed index. The insurance program studied is recommended as a proper alternative for currently applying yield insurance.

Investor Sentiment, Unexpected Inflation and Bitcoin Basis Risk

Investor Sentiment, Unexpected Inflation and Bitcoin Basis Risk

Pricing and hedging of longevity basis risk through securitisation

AbstractPension 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.

Basis risk and the demand for catastrophic rainfall insurance

Abstract Rainfall is an important source of covariate shock in developing countries. Insurance against a rainfall index has, therefore, held much promise as a formal insurance product to protect the livelihoods of poor farmers. But how good is rainfall as a measure of covariate shocks? The imperfect association between them has been flagged as a reason for low demand for index insurance. Using district crop yields and rainfall data for India, we find that tail dependence characterizes the association between aggregate crop yields and rainfall. Could this property be exploited to design catastrophic loss insurance programs that have low basis risk? Using simulations of the estimated copulas, we show that the value of index-based insurance relative to actuarial cost is higher for insurance against extreme losses (of the index) than for insurance against all losses. We conclude that index insurance could find greater acceptability if it only insured extreme losses.

Ambiguity attitudes and demand for weather index insurance with and without a credit bundle: experimental evidence from Kenya

We investigate the impact of ambiguity attitudes on the willingness-to-pay (WTP) for index insurance among female smallholders in Kenya. We gauge incentive-compatible measures of ambiguity aversion and insensitivity in the domain of gains and losses, as well as loss aversion. Next, we setup a framed experiment to measure WTP for insurance with basis risk. For a random subsample we introduce an alternative ‘rebate’ insurance, comparable to an insurance purchased through a loan – repaid in good years and deducted from payout in bad ones – that is expectedly more palatable for the loss averse. We find that ambiguity aversion significantly increases WTP for the standalone insurance, while loss aversion reduces it as expected. The former result is seemingly at odds with previous evidence from the field, but is consistent with a setting in which insurance ambiguity engenders relatively less disutility compared to the vagaries of weather. We show that this apparent divergence is not caused by differences in the method used to estimate ambiguity aversion compared to existing field studies. Rather, we exploit exogenous variation in the familiarity with insurance within our sample to show that it is explained away by the role of experience with the novel technology—a previously underestimated mediator. Ambiguity aversion hinders adoption at early stages but increases when the insurance is better understood. The rebate scenario, instead, all but cancels the effect of loss aversion on WTP, but the increased contractual ambiguity results in significantly lower bids by the ambiguity averse. In the lab, the WTP for rebate-type insurance-credit bundles is not different from that of the actuarially equivalent standalone insurance, implying that evidence from the field on greater uptake for the former may be attributable to liquidity constraints and time discounting effects, rather than to behavioural traits.

Longevity hedge effectiveness using socioeconomic indices

This paper evaluates socioeconomic basis risk in longevity hedging. Using data for a full population stratified into socioeconomic groups, we explore the benefits and costs of two alternative hedging strategies, with and without basis risk, in the capital market. The benefit of the longevity hedge is represented by the risk reduction in the variability of a life annuity, whereas the cost is the notional amount of hedging contracts times the actuarial risk premium. We find that hedging is more cost-effective for the annuity provider when basis risk is eliminated. Moreover, it allows for a higher degree of hedge effectiveness at a cost that is equivalent to a hedge where basis risk is present. Finally, the yearly expenses related to hedging longevity risk require, at most, an extra added rate of return of no more than 0.16%.

Farmers’ Willingness to Purchase Weather Index Crop Insurance: Evidence from Battambang, Cambodia

The weather index crop insurance (WICI) scheme was introduced under a pilot project for rice in Cambodia in 2021. The adoption rate was low and the loss ratio was higher than 200%. The increase in farmers’ participation would help reduce the loss ratio, which can sustain the WICI scheme. This study, therefore, examines Cambodian rice farmers’ willingness to purchase WICI in Cambodia. The hypothesis is that the low adoption rate is due to a lack of awareness, lack of understanding of WICI, lack of trust in weather stations, and the problem of basis risk. This study would like to test the influence of those factors on the willingness to purchase in Cambodia. Battambang Province was chosen as the study area as it is the largest area for rice production and has the largest take-up rate of farmers buying WICI. Detailed interviews of 232 farmers were conducted in the districts of Bavel and Moung Ruessei. The probit regression model was used to identify factors that significantly impact farmers’ willingness to purchase WICI. The results indicate that land size, level of trust in weather stations, level of farmers’ understanding of WICI, and joining the WICI awareness program have positive effects on the probability of farmers’ willingness to buy WICI, whereas the number of household laborers and expectation of floods have negative influences. The probability of willingness to purchase by farmers who attended the awareness program on WICI was 38% higher than those who did not. The size of farmland, level of trust in weather stations, and level of understanding of WICI increase in one unit affecting the probability of willingness to purchase WICI by 4%, 16%, and 25%, respectively. On the other hand, the increase in the number of household laborers in the rice field by one person and the increase in the probability of expected flood increase by 0.1 drag back the probability of farmers’ willingness to purchase by 16% and 5%, respectively. The results suggest the government to raise the insurance awareness and understanding of WICI. Development of weather station infrastructure, as well as maintenance of weather stations, is needed to guarantee the accuracy of data generated from the weather station.

Disentangling Trend Risk and Basis Risk with Functional Time Series

In recent multi-population stochastic mortality models, one critical scientific issue is the vague distinction between trend risk and population basis risk. In particular, the cross- and auto-correlations between the innovations of the latent factors representing the common trend and the population-specific trends are often assumed to be non-existent, although they are possibly statistically significant. While it is theoretically possible to capture such correlations by treating the latent factors as a vector time series, the resulting model would contain a large number of parameters, which may in turn lead to robustness problems. In this paper, we address these issues by the use of the product–ratio model. Contrary to the prevalent assumption of non-existent correlations, the latent factors under the product–ratio model are approximately uncorrelated. This permits us to disentangle trend risk and population basis risk, thereby sparing us from the need to use a heavily parameterized vector time-series process. Compared to the augmented common factor model, our approach demonstrates improved robustness in terms of correlation structures and hedging performance, offering a new perspective on treating cross- and auto-correlations between latent factors in mortality modeling.

A pricing model for agricultural insurance based on big data and machine learning

<p>Agricultural insurance is a crucial element of policies that promote and protect agriculture. It protects agriculture from risk and distributes agricultural hazards. The rural economy’s stabilization has been a significant stabilizer function. But as agriculture insurance has quickly advanced, a number of issues have unavoidably come to light. Agricultural insurance still offers a wide range of products and services available today. Big data will play a significant supporting role in the pressing need to innovate and improve goods and services. Other information supporting agricultural insurance includes agricultural data connected to it. The two previously most often utilized agricultural index insurances are regional yield insurance and weather index insurance. They struggle with risk pricing mostly due to a lack of appropriate empirical data, complicated dependence linkages between various hazards, and the prevalence of basis risk. A comprehensive study and review of pertinent research findings are carried out by modelling regional yield risk, building weather indicators and their distribution fitting, modelling agricultural dependence risk, and measuring and reducing basis risk. This article highlights the flaws in the current pricing models as well as the problems that need to be addressed in future studies. The need to further develop agricultural index insurance’s risk modelling techniques and increase the objectivity and precision of the pricing outcomes cannot be overstated in terms of their practical importance.</p>