Basis Of Risk Research Articles

Corporate Hedging, Contract Rights, and Basis Risk

Corporate Hedging, Contract Rights, and Basis Risk

Designing rainfall index based futures contracts: analysis of basis risk

Designing rainfall index based futures contracts: analysis of basis risk

Designing Rainfall Index Based Futures Contracts: Analysis of Basis Risk

Designing Rainfall Index Based Futures Contracts: Analysis of Basis Risk

Prospects for weather-indexed insurance for blueberry growers

PurposeThe authors investigate whether an index-based weather insurance (WII) product can complement or replace existing traditional crop yield insurance for mitigating farmers' financial risks, with an application to blueberry growers in British Columbia (BC).Design/methodology/approachA hybrid model combining expected utility (EU) and prospect values is developed to analyse farmers' demand for WII.FindingsWhile weather data are used to investigate supply elements, a hybrid model combining EU theory and prospect theory (PT) is developed to analyse farmers' demand for WII. On the supply side, a quality index is constructed and the relationship between the quality index and key weather parameters is quantified using a partial least squares structural model. The authors then model weather parameters via time-series analysis and statistical distributions to provide reasonable estimates for calculating actuarially sound insurance premiums for a rainfall indexed, insurance product. This model indicates that decreases in the proportion of a blueberry grower's total revenue and revenue volatility will decrease the possibility that they participate in WII. At the same time, an increase in the value loss aversion coefficient and WII's basis risk further leads to less demand for WII. In short, a grower may decide not to participate in WII at an actuarially fair premium due to the combined effects of the above factors. Overall, while the supply analysis enables us to demonstrate that WII can potentially help in mitigating farmers' financial risks, it turns out that, on the demand side, blueberry growers are unwilling to pay for such a product without large government subsidies.Originality/valueThe authors argue that the demand for insurance may be affected by the level and the volatility of a berry grower's total revenue. Hence, the authors propose a hybrid expression that assumes a farmer seeks to maximize the total utility function to capture the rational and intuitive parts of a farmer's decision-making process. The EU represents rationality and the prospect value represents the intuitive component. Meanwhile, the authors investigate the possibility of using key weather parameters to construct a berry quality index – one that could be applied to other agricultural areas for studying the relationship between weather conditions and product quality.

Gamma-Gompertz mixture model with cure fraction to analyze data on Anglo-Nubian goats with positive EPG

The exposure of small ruminants to gastrointestinal endoparasites is one of the main factors limiting their production, by compromising the herd’s performance because of delayed growth and mortality, among other factors. But with advancements in the treatments, an increase in the life expectancy this ruminants under-going new treatments is expected. In this paper, we present a study experimental goat herd belonging to the Agrarian Sciences Center of Federal University of Piaui (UFPI) diagnosed with positive EPG. The objective of this study was to use the approach of cure fraction models. The proposed modeling is based on the gamma-Gompertz mixture model with cure fraction model in which the frailty variable assumes the Gamma distribution and the basis risk function is Gompertz. The model proposed was suitable for this type of analysis, because besides enabling quantifying the heterogeneity not observed due to hard-to-measure factors, it was possible to identify evidence of long-term survival for some subgroups of animals while in others the survival curve approached zero with increase of useful life.

An empirical study of the key factors affecting herders' purchasing decision on weather index insurance—A case study from inner Mongolia autonomous region, China

The weather index insurance for mutton sheep (WIMS) is the first weather index insurance product that addresses the increased feeding cost-related risk of grassland animal husbandry herders in China. In 2019, the WIMS was implemented as a pilot project in 11 county regions of Xilin Gol League, Inner Mongolia, China. Since the implementation of this product, the purchasing rate has varied from region to region, as the highest purchasing rate was 84.3%, while the lowest was only 1.8%. This research uses the double hurdle model to identify the factors affecting herders' purchasing behavior. This paper reports a survey study of 535 participants herders in the Xilin Gol League. The result indicates that herders' age, disaster risk evaluation, insurance awareness, pasture area, and neighborhood purchasing decision significantly impact herders' purchasing propensity. Disaster evaluation, insurance awareness, pasture area and location significantly impact the purchasing degree of herders. The study suggests that young herders and herders with large pasture areas should be motivated to purchase insurance. In addition, new methods should be applied for the publicity of this insurance product; Investments should be focused on the automatic weather monitoring station projects in pastoral areas to reduce the basis risk of the insurance.

Unravelling the pastoralist paradox – preferences for land tenure security and flexibility in Kenya

AbstractIn this paper, we use a discrete choice experiment conducted among pastoralists in four different semi-arid counties in Kenya characterized by different land tenure regimes to analyze how pastoralists make tradeoffs between tenure security and grazing flexibility – the so-called pastoralist paradox. Results show that there is one group of respondents who are desperate for change and seem to prefer either group or private title deeds to their current situation. A second, smaller group has strong preferences for the status quo, which could be driven by their relatively short migration distances. Concerning index-based livestock insurance, the basis risk suffered by insured pastoralists due to underprediction is high, but willingness to pay (WTP) for livestock insurance should still be high enough to ensure maximum uptake, leaving current low uptakes hard to explain. The worry about climate change is high but does not translate into increased WTP for more secure tenure or formal livestock insurance.

An Information-Theoretic Approach to Reconciling Historical Climate Observations and Impacts on Agriculture

Abstract Decision-makers in climate risk management often face problems of how to reconcile diverse and conflicting sources of information about weather and its impact on human activity, such as when they are determining a quantitative threshold for when to act on satellite data. For this class of problems, it is important to quantitatively assess how severe a year was relative to other years, accounting for both the level of uncertainty among weather indicators and those indicators’ relationship to humanitarian consequences. We frame this assessment as the task of constructing a probability distribution for the relative severity of each year, incorporating both observational data—such as satellite measurements—and prior information on human impact—such as farmers’ reports—the latter of which may be incompletely measured or partially ordered. We present a simple, extensible statistical method to fit a probability distribution of relative severity to any ordinal data, using the principle of maximum entropy. We demonstrate the utility of the method through application to a weather index insurance project in Malawi, in which the model allows us to quantify the likelihood that satellites would correctly identify damaging drought events as reported by farmers, while accounting for uncertainty both within a set of commonly used satellite indicators and between those indicators and farmers’ ranking of the worst drought years. This approach has immediate utility in the design of weather-index insurance schemes and forecast-based action programs, such as assessing their degree of basis risk or determining the probable needs for postseason food assistance. Significance Statement We present a novel statistical method for synthesizing many indicators of drought into a probability distribution of how bad an agricultural season was likely to have been. This is important because climate risk analysts face problems of how to reconcile diverse and conflicting sources of information about drought—such as determining a quantitative threshold for when to act on satellite data, having only limited, ordinal information on past droughts to validate it. Our new method allows us to construct a probability distribution for the relative severity of a year, incorporating both kinds of data. This allows us to quantify the likelihood that satellites would have missed major humanitarian droughts due to, for example, mistimed observations or unobserved heterogeneity in impacts.

Hydrological Drought-Indexed Insurance for Irrigated Agriculture in a Highly Regulated System

Water scarcity is an increasingly recurring problem for irrigated agriculture in Mediterranean regions. It is, therefore, necessary to establish technical and financial measures to enable irrigators to deal with this problem. This study presents a new index-based drought insurance scheme in an irrigation district in the Jucar river basin in Spain, a highly regulated water system. Three insurance scheme options were evaluated and, the values of the fair risk premiums, the maximum compensation, and the deductible franchise were established. These insurance schemes were designed in agreement with the preexisting drought system operating rules to reduce moral hazard and adverse selection. Risk-reducing and effective evaluation methods were used to determine the insurance coverage’s viability for irrigators: standard deviation gross margin, minimum gross margin, and RMSL. The proposed insurances were also evaluated using synthetic hydrological time series generated with a stochastic ARMA model through a basin-wide water resource simulation model developed in the DSS Shell AQUATOOL. Financial indicators, such as the basis risk and claim ratio were applied to analyze the economic feasibility for insurance companies. The results show that a suitable and efficient option is an early-bird contract combined with a trigger of emergency or alert state in a multi-year contract. This type of specialized insurance helps to fill the existing gap in traditional insurance schemes for irrigated crops and offered additional coverage to farmers under drought and water scarcity conditions.

On the relationships among durum wheat yields and weather conditions: evidence from Apulia region, Southern Italy

The weather index-based insurances may help farmers to cope with climate risks overcoming the most common issues of traditional insurances. However, the weather index-based insurances present the limit of the basis risk: a significant yield loss may occur although the weather index does not trigger the indemnification, or a compensation may be granted even if there has not been a yield loss. Our investigation, conducted on Apulia region (Southern Italy), aimed at deepening the knowledge on the linkages between durum wheat yields and weather events, i.e., the working principles of weather index-based insurances, occurring in susceptible phenological phases. We found several connections among weather and yields and highlight the need to collect more refined data to catch further relationships. We conclude opening a reflection on how the stakeholders may make use of publicly available data to design effective weather crop insurances.

Basis risk management and randomly scaled uncertainty

This paper proposes a method for quantifying the basis risk present in index-based insurance. It applies when the inherent uncertainty is represented by a randomly scaled variable. This turns out to be a reasonable assumption in a number of practical situations. Several properties of such a variable are first briefly studied. Their order in the s-convex sense is discussed and the associated extreme distributions are obtained to generate the worst situations. In each scenario, the basis risk consequences are then assessed using a penalty function that takes into account the risk tolerances of the protection buyer. Basis risk limits for a fixed budget can also be set. The proposed approach is illustrated by a few simple examples.

Demand for Weather Index Insurance among Smallholder Farmers under Prospect Theory

Index insurance offers an innovative risk management solution for uninsured agricultural weather risk. We investigate the theoretical relationship between prospect theory risk preferences and characteristics of index insurance. We pair these theoretical findings with data from a lab-in-the-field experiment in Kenya. Empirically, we find that insurance demand is decreasing in loss aversion, and the negative marginal effect of loss aversion on insurance demand increases with basis risk and the insurance premium. Our theoretical and empirical results combined illustrate the importance of considering both risk and loss aversion, as well as basis risk, in understanding index insurance decisions.

CHALLENGES FACING THE ADOPTION AND IMPLEMENTATION OF WEATHER INDEXED INSURANCE BY INSURANCE FIRMS

Purpose: The study assessed the challenges facing the adoption and implementation of weather indexed insurance by insurance firms
 Methodology: This was accomplished through the use of a desktop literature review. The use of Google Scholar was utilized in order to locate seminal references and journal articles that were pertinent to the study. In order to meet the inclusion criteria, the papers had to be no more than ten years old.
 Findings: Using a certain weather parameter, a specific weather station, and a predetermined length of time, Weather Index Insurance is calculated. To avoid crop losses, the insurance might be constructed to cover against index realizations that are predicted to be too high or low. Pilots and feasibility studies have indicated that while the weather index product appears to be a boon, there are numerous hurdles or downsides to weather index products. Because of this, we set out to find out what obstacles insurance companies may face in implementing weather-indexed insurance. Index insurance's biggest flaw is its exposure to basis risk. In addition, weather-related risks exhibit characteristics that frequently contravene classical insurance rules. Drought and other slow-developing meteorological hazards, such as floods, can have a systemic effect since they are regionally connected. As a result, actuarial ratemaking becomes more challenging because of the increased volatility of meteorological variables and non-stationary loss distributions. Third, a lack of yield and weather data makes it difficult to estimate loss distributions accurately. Some farmers may not comprehend weather index insurance and hence not buy it, and a significant number of farmers may also require a subsidy if they want to get weather insurance.
 Unique contribution of theory, practice and policy: Crop insurance companies can benefit from approaches like time diversification, local test procedures, and the addition of expert knowledge to observational data, as well as mechanisms like supporting insurance market development as a means of facilitating adaptation. In addition, better training on weather index insurance and regular communication are required.

Enhanced Weather-Based Index Insurance Design for Hedging Crop Yield Risk.

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.

Mediating and mapping climate risk: micro-insurance and earth observation

ABSTRACT The recent trajectory of micro-insurance in Africa lies at the intersection of two trends that have been percolating unevenly over the past decades: (1) the incorporation of micro-insurance into the practices of FinTech; and (2) the reframing of micro-insurance as a technology relevant to the management of ‘climate risk.’ I focus on digital experiments in the construction of platforms which access data from remote sensing ‘earth observation’ technologies to formulate unique indices of climate risk. These platforms mediate the antinomies of presence/absence, place/distance; they operate ‘remotely’ from the risks they constitute and map while claiming an intimate and ‘hyper-local’ index of those risks. These mediations manage the distance between ‘the ground’ and the ‘remote’ sites from which knowledge of particular locations is produced, constituting new forms of territory and volume. I argue that these platforms entail unique uncertainties of their own: the ongoing problems of basis risk; costs imposed on vulnerable populations often unable to afford premiums; and issues of climate injustice in which responsibility is placed on poor populations to manage climate risks they had little role in creating in the first place.

Index Design: Hedging and Manipulation

This paper studies optimal index design to facilitate both hedging and alleviate illegal manipulation in a competitive equilibrium paradigm, modified to deal with manipulation. Specifically, a large trader is trading both derivatives and assets that effectively hides her trades behind the competitive market clearing mechanism. Unlike the strategic game paradigm, a volume-weighted average pricing (VWAP) index introduces basis risk and encourages manipulation because of the additional randomness in volume weight and the greater price impact enjoyed by the large trader. In contrast, an equal-weighted average pricing (EWAP) index preserves market completeness and discourages manipulation.

Index insurance and basis risk: A reconsideration

Index insurance has been seen as an important tool for managing the risks faced by farmers in developing countries but uptake has been disappointingly low. Basis risk is often cited as one of the main reasons for the lack of uptake. We investigate the role of basis risk in greater depth by considering contracts that allow farmers to choose the level of the index that triggers an indemnity in addition to coverage. We show that risk averse farmers will select trigger levels to protect against catastrophic outcomes, even if downside basis risk increases. A numerical simulation model bears out our theoretical results.

Earthquake parametric insurance with Bayesian spatial quantile regression

With its transparent and fast claims payment, parametric insurance has been widely used to insure nature-related risks such as earthquakes, floods and hurricanes. In 2014, earthquake parametric insurance was introduced to provide coverage for earthquake losses occurred in Yunnan Province of China. However, as a main limitation of parametric insurance, basis risk is inevitable. In this paper, a Bayesian spatial quantile regression model is proposed to reduce the basis risk of earthquake parametric insurance. The effect of earthquake hazard, risk exposure, and vulnerability on economic loss are analyzed and considered in the quantile regression model. Since risk exposure and vulnerability at the epicenter cannot be observed, they will be treated as latent variables in the quantile regression model. Bayesian approaches are applied, and spatial correlation is considered to construct the prior distributions for the latent variables. Earthquake losses in Yunnan Province from 1992 to 2019 are collected and analyzed by the proposed model and methods. The payment mechanism and the corresponding premiums of 16 regions in Yunnan Province are then calculated. The results show that the loss ratio is more reasonable than the current earthquake insurance, and the basis risk is then reduced.

A Primer on Hedging with Stock Index Futures

In this article, the authors discuss the various approaches and issues associated with hedging with stock index futures. The most common approach used in practice is based on minimizing the variance of the hedge within the mean-variance framework to obtain the optimal hedge ratio. In determining the optimal hedge ratio, consideration must be given to the basis risk to which a fund is exposed when using stock index futures. An optimal hedge ratio based on variance minimization is the slope coefficient estimated from an ordinary least squares (OLS) regression of the returns of the portfolio to be hedged on the returns of the stock index futures contract. The estimated slope coefficient is referred to as beta. The optimal hedge ratio can be further refined by adjusting for the beta estimated from an OLS regression of the return on the underlying stock index on the return on the stock index futures. A criticism of the OLS model is twofold. The first is that there are statistical issues in estimating beta using the basic OLS regression model. Several models that employ advanced econometric techniques have been proposed for estimating hedge ratios. The second criticism is that the OLS model assumes a constant hedge ratio, despite the theoretical and empirical evidence showing the hedge ratio should be time varying. Evidence suggests that employing advanced econometric models to estimate the slope coefficient offers little improvement in hedging effectiveness—and even if there is some improvement, the modeling cost may not justify the extra effort. As for the second criticism, the well-known autoregressive conditional heteroscedasticity (ARCH) and the generalized ARCH (GARCH) have been used to allow for time-varying hedge ratios. Although some studies have reported that ARCH and GARCH can improve hedge effectiveness, the effort may not be warranted due to the additional modeling as well as the time-varying hedge ratios involving rebalancing the portfolio periodically, which can add significantly to the cost of hedging.

A Proposed Satellite-Based Crop Insurance System for Smallholder Maize Farming

Crop farming in Sub-Saharan Africa is constantly confronted by extreme weather events. Researchers have been striving to develop different tools that can be used to reduce the impacts of adverse weather on agriculture. Index-based crop insurance (IBCI) has emerged to be one of the tools that could potentially hedge farmers against weather-related risks. However, IBCI is still constrained by poor product design and basis risk. This study complements the efforts to improve IBCI design by evaluating the performances of the Tropical Applications of Meteorology using SATellite data and ground-based observations (TAMSAT) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) in estimating rainfall at different spatial scales over the maize-growing season in a smallholder farming area in South Africa. Results show that CHIRPS outperforms TAMSAT and produces better results at 20-day and monthly time steps. The study then uses CHIRPS and a crop water requirements (CWR) model to derive IBCI thresholds and an IBCI payout model. Results of CWR modeling show that this proposed IBCI system can cover the development, mid-season, and late-season stages of maize growth in the study area. The study then uses this information to calculate the weight, trigger, exit, and tick for each of these growth stages. Although this approach is premised on the prevailing conditions in the study area, it can be applied in other areas with different growing conditions to improve IBCI design.