Basis Of Risk Research Articles

Pricing of Presale Contracts with Macroeconomic Factors and Stochastic Basis Risk

This study analyzes the stochastic basis risk (presale price minus spot housing price) of presale housing from the viewpoint of forward pricing. We employ the modified Brownian bridge process to deal with the time-varying volatility and price convergence of presale and existing houses. The finding reveals an asymmetric effect of macroeconomic factors on the presale price in different market conditions. Such results might reflect the investors’ behaviors of overreaction and/or over-pessimism. In addition, the influence of macroeconomic variables is greater on the basis price than on the prices of existing houses, especially in a depressed housing market. Our results emphasize the importance of considering basis risk and the influence of macroeconomic factors when pricing presale houses to manage housing risk.

Occurrence of Listeria monocytogenes in Ready-to-Eat Meat and Poultry Product Verification Testing Samples from U.S. Department of Agriculture–Regulated Producing Establishments, 2005 through 2017

Ready-to-eat (RTE) meat and poultry product samples collected between 2005 and 2017 from RTE-producing establishments for the U.S. Department of Agriculture, Food Safety and Inspection Service (FSIS) ALLRTE/RTEPROD_RAND (random) and RTE001/RTEPROD_RISK (risk-based) sampling projects were tested for Listeria monocytogenes (Lm). Data for 45,897 ALLRTE/RTEPROD_RAND samples collected from 3,607 distinct establishments and 112,347 RTE001/RTEPROD_RISK samples collected from 3,283 distinct establishments were analyzed for the presence of Lm. These data were also analyzed based upon the percentages of establishments with positive samples, annual production volume, sanitation control alternatives, geographic location, and season or month of sample collection. Results revealed low occurrence of Lm-positive samples from the random and risk-based sampling projects, with 152 (0.33%) positive samples for ALLRTE/RTEPROD_RAND and 403 (0.36%) positive samples for RTE001/RTEPROD_RISK. The percentage of positive samples significantly decreased over time, from about 0.7% in 2005 and 2006 to about 0.2% in 2017 (P < 0.05). From 2005 to 2017, 3.9% of establishments sampled under the ALLRTE/RTEPROD_RAND sampling project had at least one Lm-positive sample. Similarly, 10.0% of establishments sampled under the RTE001/RTEPROD_RISK sampling project had at least one positive sample. Samples positive for Lm were found in all geographic regions in all months. Thus, in 13 years of RTE product sampling in FSIS-regulated establishments (2005 through 2017), <0.4% of samples were positive for Lm in both risk-based and random sampling projects. The low prevalence of Lm in these products suggests that the combination of FSIS policies and industry practices may be effective for controlling Lm contamination. Information obtained from these sampling projects is relevant to the ongoing prevention of foodborne Lm illnesses from RTE meat and poultry products.

Can Experiential Games and Improved Risk Coverage Raise Demand for Index Insurance? Evidence from Kenya

Uninsured risk impedes agricultural production, but traditional indemnity insurance is not a viable option for smallholder farmers due to market failures. Weather index insurance is often touted as an alternative risk management tool. Demand for weather index insurance, however, has been weak. One possible reason is basis risk, the mismatch between index insurance payouts and individual outcomes. We estimate the impact of two interventions on preferred coverage under a real index insurance product, as revealed through an auction: (a) a reduction in basis risk, and (b) an experiential game that teaches farmers how index insurance works, with an emphasis on basis risk. We show all farmers demonstrate strong sensitivity to basis risk. The experiential game modestly increased knowledge, and experiential game participants indicate higher levels of preferred insurance coverage. Although offering a lower basis risk insurance product and playing an experiential game both increase preferred coverage in isolation, there is no additional impact of doing both. We adapt a theoretical model of misattribution bias to demonstrate how reference‐dependent learning with loss aversion could lead to this unexpected result.

Hedging of Variable Annuities under Basis Risk

Abstract I 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.

Insuring Weather Risks in European Agriculture

SummarySmart insurance designs (e.g. tailoring insurances to the individual farm) and technological progress (e.g. advances in satellite technology) enable improvements in insurance schemes. Spatially and temporally more detailed information as well as better knowledge of the relationship between weather and yields losses can reduce basis risk of insurances. However, the use of new and better data does not automatically lead to better insurance schemes. The SURE‐Farm project contributes to the understanding of weather risks and new insurance mechanisms as possible tools to increase the resilience of European agriculture to weather extremes. Therefore, we aim to show how to integrate newly available data sources for different agricultural outputs, namely grassland, crop and dairy production in different European regions. In the four contributions summarised here, we evaluate currently existing index insurance schemes and provide fundamental insights for future developments of index insurances for the crop and livestock sector in Europe. We first summarise and discuss existing index‐based weather insurances. Second, we investigate the drought risk reduction potential of different drought indicators in two case studies on different crops in Eastern Germany. Third, we examine the relationship between hot and humid weather and milk yield losses for dairy producers in Flanders.

The Impact of Model Uncertainty on Index-Based Longevity Hedging and Measurement of Longevity Basis Risk

We investigate the impact of model uncertainty on hedging longevity risk with index-based derivatives and assessing longevity basis risk, which arises from the mismatch between the hedging instruments and the portfolio being hedged. We apply the bivariate Lee–Carter model, the common factor model, and the M7-M5 model, with separate cohort effects between the two populations, and various time series processes and simulation methods, to build index-based longevity hedges and measure the hedge effectiveness. Based on our modeling and simulations on hypothetical scenarios, the estimated levels of hedge effectiveness are around 50% to 80% for a large pension plan, and the model selection, particularly in dealing with the computed time series, plays a very important role in the estimation. We also experiment with a modified bootstrapping approach to incorporate the uncertainty of model selection into the modeling of longevity basis risk. The hedging results under this approach may approximately be seen as a “weighted” average of those calculated from the different model candidates.

Modeling Housing Price Dynamics and their Impact on the Cost of no-Negative-Equity-Guarantees for Equity Releasing Products

We investigate model risk in pricing no-negative-equity guarantees (NNEGs) with the aim of identifying the housing risks involved in equity-release products. To analyze the regional and local effect in the house price modeling, we evaluate different models using the house price index (HPI) based on the cities of London, Manchester and Coventry and the UK nationwide HPI respectively. The ARMA-GARCH jump model that can capture the characteristics of jump persistence, autocorrelation and volatility clustering are proposed according to the model fittings. To investigate the model risk on the cost of NNEGs, we then derive the risk-neutral valuation framework using the conditional Esscher transform technique (Buhlmann et al. 1996). Our numerical analyses reveal that the housing model risk affects the costs of NNEGs significantly. In addition, the cost of NNEGs is significantly different for different cities due to localized effect. Therefore, the basis risk is large enough to matter when pricing NNEGs.

Practical Applications of Volumetric Risk Hedging Strategies and Basis Risk Premium for Solar Power

<h3>Practical Applications</h3> In <b><i>Volumetric Risk Hedging Strategies and Basis Risk Premium for Solar Power</i></b>, published in the Summer 2020 issue of <b><i>The Journal of Alternative Investments</i>, Takashi Kanamura</b> of <b>Kyoto University</b> helps meet the need for new tools to manage the risks involved with generating renewable energy. The economics of hedging the risks associated with the production of renewable energy are fundamentally different from those associated with traditional energy products. The author makes a unique contribution to the literature by providing a framework for pricing solar energy options. Conventional means of option pricing rely on the notion of a replicating portfolio. Yet, in the case of renewable energy, where prices for the underlying asset do not exist, and the market is incomplete, traditional pricing techniques are not possible. The author instead applies a method of establishing “good-deal bounds” (Cochrane 2000). This approach rules out excessively good prices that can result in arbitrage opportunities or unsustainably high Sharpe ratios. His empirical estimates further illustrate that there is a basis risk when using weather derivatives as a proxy for a solar energy option. <b>TOPICS:</b>ESG investing, emerging, options, performance measurement

Indifference pricing of pure endowments via BSDEs under partial information

In this paper, we investigate the pricing problem of a pure endowment contract when the insurance company has a limited information on the mortality intensity of the policyholder. The payoff of this kind of policies depends on the residual life time of the insured as well as the trend of a portfolio traded in the financial market, where investments in a riskless asset, a risky asset and a longevity bond are allowed. We propose a modeling framework that takes into account mutual dependence between the financial and the insurance markets via an observable stochastic process, which affects the risky asset and the mortality index dynamics. Since the market is incomplete due to the presence of basis risk, in alternative to arbitrage pricing we use expected utility maximization under exponential preferences as evaluation approach, which leads to the so-called indifference price. Under partial information this methodology requires filtering techniques that can reduce the original control problem to an equivalent problem in complete information. Using stochastic dynamics techniques, we characterize the indifference price of the insurance derivative in terms of the solutions of two backward stochastic differential equations. Finally, we discuss two special cases where we get a more explicit representation of the indifference price process.

Rainfall-Related Weather Indices for Three Main Crops in China

Rainfall-related hazards—deficit rain and excessive rain—inevitably stress crop production, and weather index insurance is one possible financial tool to mitigate such agro-metrological losses. In this study, we investigated where two rainfall-related weather indices—anomaly-based index (AI) and humidity-based index (HI)—could be best used for three main crops (rice, wheat, and maize) in China’s main agricultural zones. A county is defined as an “insurable county” if the correlation between a weather index and yield loss was significant. Among maize-cropping counties, both weather indices identified more insurable counties for deficit rain than for excessive rain (AI: 172 vs 63; HI: 182 vs 68); moreover, AI identified lower basis risk for deficit rain in most agricultural zones while HI for excessive rain. For rice, the number of AI-insurable counties was higher than the number of HI-insurable counties for deficit rain (274 vs 164), but lower for excessive rain (199 vs 272); basis risks calculated by two weather indices showed obvious difference only in Zone I. Finally, more wheat-insurable counties (AI: 196 vs 71; HI: 73 vs 59) and smaller basis risk indicate that both weather indices performed better for excessive rain in wheat-planting counties. In addition, most insurable counties showed independent yield loss, but did not necessarily result in effective risk pooling. This study is a primary evaluation of rainfall-related weather indices for the three main crops in China, which will be significantly helpful to the agricultural insurance market and governments’ policy making.

Optimal Dynamic Longevity Hedge with Basis Risk

This paper proposes an optimal dynamic strategy for hedging longevity risk in a discrete-time setting. Our proposed hedging strategy relies on standardized mortality-linked securities and minimizes the variance of the hedging error as induced by the population basis risk. While the formulation of our proposed hedging strategy is quite general, we use a stylized pension plan, together with a specified yearly rolling” trading strategy involving q-forwards and a specified stochastic mortality model, to illustrate our proposed strategy. Under these specifications, we show that the resulting hedging problem can be formulated as a stochastic optimal control framework and that a semi-analytic solution can be derived through the Bellman equation. Extensive Monte Carlo studies are conducted to highlight the effectiveness of our proposed hedging strategy. We also consider a scheme to approximate the semi-analytic solution in order to reduce the computational time significantly while still retaining its hedging effectiveness. We benchmark our strategy against the delta” hedging strategy as well as its robustness to q-forwards’ maturity, reference age, and stochastic mortality models. The proposed strategy has many appealing features, including its discrete-time setting which is consistent with market practice and hence conducive to practical implementation, and its generality in that the underlying hedging principle can be applied to other standardized mortality-linked securities and other stochastic models.

How Might Climate Change Influence farmers' Demand for Index-Based Insurance?

The low observed uptake of non-subsidised index-based insurance policies in developing countries has been puzzling researchers for about a decade. This paper analyses the role of drought frequency in farmers' demand for index-based insurance in developing countries. While it is typically assumed that an increase in exposure to risk would result in higher demand for index insurance, this paper finds the opposite: an increase in drought frequency may result in lower demand for index insurance under fairly standard conditions. In an expected utility model, we show that the demand for insurance is an inverted U function of drought frequency. We further show that downside basis risk decreases insurance demand under frequent drought conditions. It implies that insurance against similar but more frequent events cannot meet large demand from farmers. To check the empirical relevance of these effects, we conduct an insurance field experiment in Burkina Faso with 205 farmers. We analyse insurance demand for different drought frequencies, different levels of basis risks and different loading factors through incentivised lottery choices. This analysis confirms that for higher drought frequencies, insurance demand is lower. Insurance demand also decreases with basis risk and the loading factor.

HEDGING UNDER PRICE, OUTPUT AND BASIS RISKS: EMPIRICAL ANALYSIS

This paper is the first to consider hedging under price, output and basis risks using a general framework. In doing so, it provides significant empirical results pertaining to the US.

Avoiding the floating rate cliff-edge: How asset management general counsels can tackle the LIBOR cessation challenge

LIBOR’s (London Inter-bank Offered Rate) impending cessation poses several significant challenges for the asset management industry and the buy-side generally. This paper aims to assist asset management general counsels and law departments in developing and implementing LIBOR cessation action plans so that they can effectively meet the needs of their firms and colleagues in operations, risk, front office, marketing, compliance and beyond in tackling LIBOR’s expected sunset. Readers can expect to obtain familiarity with the key legal hurdles to overcome by law departments, including fallback analysis and assessment of basis risk inherent in contracts across asset classes (eg cash products, derivatives, repo etc). Specific focus is given to the practical consequences for asset managers of LIBOR transition, with an eye to arming general counsels with the information they need to ‘make the case’ for early and thorough preparation.

WELFARE GAINS FROM MACRO-HEDGING

Macro-hedging is one of the most important issues in hedging, but there are very few studies on the welfare impact of macro-hedging. To bridge a gap in the literature of macro-hedging, this paper introduces a method that generalizes and extends existing models of macro-hedging in several significant ways. We first assume the existence of basis risk in a small country to hedge in futures markets instead of forward contracts and relax the full-hedging assumption. We use the quantity being hedged in futures contracts as a decision variable. We also relax the restrictive assumption regarding the form of the spot price. We then derive the formula to estimate the welfare gain which can be easily implemented in any empirical case. In contrast to quasi-simulation being used in some existing approaches, our proposed method can be used for any real data, including future data, but existing methods in the literature cannot. Our approach is for investors for their investment decision-making when they use macro-hedging as their trading strategy.

Portfolio optimization in the catastrophe space

AbstractIn today's global catastrophe space, the role of insurance‐linked securities has evolved from that of a threatened reinsurance substitute to now being a viable complementary reinsurance product, underpinning the convergence of the two markets. This study constructs a two‐agent sequential optimization framework to mimic the economics of the reinsurance/insurance markets and shows how NPV‐maximizing reinsurers and hedging cost‐minimizing insurers can optimally allocate default‐risky catastrophe reinsurance and default‐free catastrophe bonds at the interface of these two markets. We analyze parametric impacts considering interest rate risk, financial leverage, basis risk, differential markup, catastrophe arrival intensity, and severity, as well as other relevant characteristics.

Volumetric Risk Hedging Strategies and Basis Risk Premium for Solar Power

This article studies volumetric risk hedging strategies for solar power with a twofold proposal of temperature-based and solar power generation–based models for solar power derivatives. It discusses the basis risk arising from a solar power volumetric risk hedge with temperature. Using the two models, the author designs and prices call options written on the accumulated non-cooling-degree days and the accumulated low solar power generation days for hedging cool summer risk, respectively, under the good-deal bounds. Using empirical studies of Czech solar power generation and Prague temperature, the author estimates the models’ parameters and numerically obtain basis risk premiums, that is, solar power generation–based call option prices minus temperature-based call option prices. Results show that premiums increase with temperature up to 25°C and decrease with temperature when it is higher than 25°C. This is consistent with the relationship between solar power generation and temperature. Above 25°C, the relationship is close to linear because of the offsetting effects of increased solar radiation and decreased solar panel efficiency. TOPICS: Portfolio management/multi-asset allocation, ESG investing, factor-based models Key Findings • We examine hedging strategies for solar power’s volumetric risk in cool summers with two call options priced under good-deal bounds using temperature-based and solar power generation-based models and discuss the basis risk from the temperature risk hedge. • Empirical studies using Czech solar power generation and Prague temperature show that the basis risk premiums increase with temperature up to 25°C and decrease with temperature over 25°C. • The latter is consistent with the relationship between solar power generation and temperature because above 25°C, the relationship is close to linear from the offsetting effects of increased solar radiation and decreased solar panel efficiency.

Understanding farmers’ valuation of agricultural insurance: Evidence from Vietnam

Agricultural index insurance offers smallholder farmers the potential to manage pervasive weather and environmental risks, and smooth income over time. Yet despite the potential benefits, take-up of index insurance has been largely disappointing. We employ a novel approach to investigate the reasons for low demand and we confirm that farmers systematically undervalue agricultural index insurance. We find that access to private transfers acts as a substitute for insurance demand, while membership of a farmer’s union helps explain why farmers value index insurance. Finally, we find that individual riskiness is positively related to willingness to pay for agricultural index insurance and interpret this as further evidence that, within the context of basis risk and lack of trust in insurers, farmers valuation of agricultural index insurance can be better understood through behavioural theories such as ambiguity aversion or prospect theory.

Pricing catastrophe swaps with default risk and stochastic interest rates

Catastrophe (CAT) swaps are bilateral contracts through which CAT losses can be transferred between two counterparties. They do not require collateral upon initiation, making them default-risky, have an average maturity of 3 years and may use index triggers, which suggest the valuation model must incorporate interest rate risk and basis risk. This study improves upon the literature and develops a dynamic structural framework to value CAT swaps and to analyze the impacts of interest rate risk, counterparty default risk, basis risk, trigger type, and other critical parameters. We estimate CAT swap spreads by using Monte Carlo simulation, with the main results indicating that the influence of stochastic interest rates on one-year CAT swap spreads is indeterminate, but is significant on 3-year and 5-year contracts. Counterparty default risk lowers CAT swap spreads, and all estimated default risk premiums are positive and economically significant. CAT loss uncertainty impacts contracts with different triggers in different ways, and basis risk becomes more severe for one-year contracts and where the correlation between the loss index and the buyer's actual loss is low.

The Design of Weather Index Insurance Using Principal Component Regression and Partial Least Squares Regression: The Case of Forage Crops

Weather index insurance is a relatively new alternative to traditional agricultural insurance such as individual yield-based crop insurance. It is still mostly at the experimental stage, rather than in widespread use like traditional crop insurance. A major challenge for weather insurance is basis risk, where the loss estimated by the index differs from the actual loss, and this is generally believed to be the main limitation in the use of weather index insurance for crops. Variable basis risk is an important type of basis risk that occurs when there are incorrect variables or missing variables for the design of the weather index. In agriculture, there is a relatively small sample size of yields, and therefore, as the number of considered weather variables increases, the problems of limited degrees of freedom for predictive models must be overcome. The objective of this article is to demonstrate two possible approaches that could be used to construct a multivariable weather index to reduce variable basis risk. Forage insurance is used as an example, and a main focus of the research is on reducing the dimensionality of the predictive model and resolving the problem of multicollinearity among weather variables. The research uses daily weather information and county-level forage yield data from Ontario, Canada. Two multivariable indices are developed based on principal component regression (PCR) and partial least squares regression (PLSR) methods, and they are evaluated against a single-variable benchmark index based on cumulative precipitation (SVCP) using several basis risk metrics. The results show that both the PCR and PLSR models are superior compared to the single-variable weather index based on cumulative rainfall (SVCR) index and can be used to achieve the objective of reducing the dimensionality of the weather variable matrix and addressing the issue of multicollinearity. Though the PLSR indices perform better than the PCR and SVCR indices in terms of average value of basis risk (),(rom) the PCR method produces a smaller percentage of mismatch, suggesting that the PCR method may be superior in correctly detecting when the insurance payment should be triggered. The methods demonstrated in this article will assist in the development of weather index–based crop insurance.