Basis Of Risk Research Articles

Interbank borrowing and bank liquidity risk

AbstractTo avoid illiquidity spillovers and basis risk in swaps, interbank lenders are especially cautious about whether interbank borrowers can meet their claims. We examine whether the incentive of interbank lenders to penalize risky borrowers can reduce borrowers' liquidity risk taking. We find that interbank borrowers, especially small and medium banks, manage their liquidity risks more prudently than their counterparts. This phenomenon is especially significant for borrowers with high information asymmetry, low liquidity buffers, and high funding gaps. Our results suggest that interbank exposure reduces the asset, funding, and off‐balance‐sheet liquidity risks of small and medium borrowing banks, and can therefore supplement regulatory liquidity requirements, which target only the largest banks.

The impact of counterparty risk on the basis risk of industry loss warranties and on (collateralized) reinsurance under (non-)linear dependence structures

PurposeThe purpose of this article is to investigate the impact of counterparty risk on the basis risk of industry loss warranties as well as on reinsurance with and without collateral under different dependence structures. The authors additionally compare the solvency and Sharpe ratio for different premium loadings and contract parameters.Design/methodology/approachThe authors propose a model framework extension to account for the counterparty risk of risk transfer arrangements. Copulas are used to also take into account non-linear dependencies between risk factors, and Monte Carlo simulation is employed to derive numerical results and to conduct sensitivity analyses.FindingsThe authors show that the impact of counterparty risk is particularly pronounced for higher degrees of dependencies and tail dependent losses, i.e. in cases of basis risk levels that appear low if counterparty risk is not considered. With respect to counterparty risk management, the authors find that already partial collateralization limits counterparty and basis risk to more acceptable levels.Practical implicationsThe study results are particularly relevant to practitioners, as insurers may not only underestimate the “true” basis risk of index-linked instruments, but also the effect of counterparty risk of reinsurance contracts along with the consequences for solvency and profitability.Originality/valueThe authors extend existing literature by allowing for the (partial) default of industry loss warranties and reinsurance under different dependence structures. Furthermore, the authors include profitability in addition to risk considerations. The interaction effects between counterparty risk and the basis risk of index-based alternative risk transfer instruments are largely unstudied, despite their considerable relevance in practice.

When is a CAT Index Futures Traded and Preferred to Reinsurance? – Tradeoff Between Basis Risk and Adverse Selection –

Insurance risks have traditionally been borne in reinsurance markets. However, in the 1990s, after a series of huge natural disasters, and consequently massive insurance payments, the reinsurance markets reduced their capacity to bear risks. Insurance-linked securities, such as CAT (catastrophe) index futures contracts, were created to provide insurers with a way to transfer insurance risks to capital markets. However, two obstacles are preventing CAT index futures from being traded: the basis risk between insurers’ risks and the futures payoff, and adverse selection between informed insurers and uninformed investors in capital markets. This study investigates the conditions under which CAT index futures, whose payoff is the average of insurers’ losses, can be traded, and insurers choose CAT index futures rather than reinsurance to transfer their risks. The results show a trade-off: CAT index futures can be traded if the number of insurers in the index is large enough, since averaging multiple insurers’ losses can mitigate adverse selection in the index futures’ payoff. However, if the number of insurers in the index is too large, insurers prefer reinsurance to index futures due to the high basis risk in the futures’ payoff.

Feeder Cattle Basis Risk and Determinants

AbstractThe feeder cattle futures contract has been criticized in recent years as not providing an effective hedging instrument. Following historically volatile cattle markets in 2014–2015, feeder cattle basis risk escalated, fueling futures contract concerns. This study assesses these concerns, utilizing weekly auction data from 32 feeder cattle markets from 1992 to 2021. Examined for the first known time, feeder cattle market variation had a statistically and economically significant influence on basis risk. Basis variation differed across regions and marketing periods. Feeder cattle basis risk was historically high in 2014–2015 but declined to levels similar to 2011 by 2018.

Do Satellite Data Correlate with In Situ Rainfall and Smallholder Crop Yields? Implications for Crop Insurance

Adverse weather is one of the most prevalent sources of risk in agriculture. Its impacts are aggravated by the lack of effective risk management mechanisms. That is why resource-poor farmers tend to respond to weather risks by adopting low-capital investment, low-return, and low-risk agricultural practices. This challenge needs to be addressed with innovative risk management strategies. One of the tools that is gaining traction, especially in the developing countries, is weather-index-based insurance (WII). However, WII uptake is still low because of several constraints, one of which is basis risk. This study attempts to address this problem by evaluating the suitability of TAMSAT, CHIRPS, MODIS, and Sentinel-2 data for WII. We evaluated the first three datasets against in situ rainfall measurements at different spatial and temporal scales over the maize-growing season in a smallholder farming area in South Africa. CHIRPS had higher correlations with in situ measured rainfall data than TAMSAT and MODIS NDVI. CHIRPS performed equally well at 10 km and 25 km spatial scales, and better at monthly than daily and 16-day time steps (maximum R = 0.78, mean R = 0.72). Due to the lack of reliable historical yield data, we conducted yield surveys over three consecutive seasons using an objective crop cut method. We then assessed how well rainfall and NDVI related with maize yield. There was a poor relationship between these variables and maize yield (R2 ≤ 0.14). The study concludes by pointing out that crop yield does not always have a linear relationship with weather and vegetation indices, and that water is not always the main yield-limiting factor in smallholder farming systems. To minimize basis risk, the process of designing WII must include identification of main yield-limiting factors for specific localities. Alternatively, insurers could use crop water requirement methods to design WII.

Basis risk in the pasture, rangeland, and forage insurance program: Evidence from California

AbstractAgricultural producers who are reliant upon rangelands and pasturelands are some of the most vulnerable to weather‐related risk given their dependence on climate‐sensitive resources. Index‐based insurance products, like the Pasture, Rangeland, and Forage Insurance Program, are considered to be an adaptation strategy for mitigating climatic risks. Given that indices are imperfect predictors of losses, residual (basis) risk persists. This study quantifies basis risk and assesses insurance contract quality for nearly 63 million acres of rangelands in California. Basis risk can be summarized by considering false negative probabilities—the probability that an insured producer suffers a loss without receiving an indemnity payment. On California rangelands the false negative probabilities associated with one (both) of insured time intervals failing to indemnify a loss are 31% to 46% (14%–25%). When indemnities were paid, in 36% of the cases the payments are not sufficient to compensate for forage‐related losses; the average shortfall in indemnity‐related compensation ranged from $1.74/acre to $2.73/acre depending upon the location and underlying value of forage.

Basis Risk and Low Demand for Weather Index Insurance

Basis Risk and Low Demand for Weather Index Insurance

Sequenced Crop Evapotranspiration and Water Requirement in Developing a Multitrigger Rainfall Index Insurance and Risk-Contingent Credit

Abstract Weather index insurance (WII) has been a promising innovation that protects smallholder farmers against drought risks and provides resilience against adverse rainfall conditions. However, the uptake of WII has been hampered by high spatial and intraseasonal basis risk. To minimize intraseasonal basis risk, the standard approaches to designing WII based on seasonal cumulative rainfall have been shown to be ineffective in some cases because they do not incorporate different water requirements across each phenological stage of crop growth. One of the challenges in incorporating crop phenology in insurance design is to determine the water requirement in crop growth stages. Borrowing from agronomy, crop science, and agrometeorology, we adopt evapotranspiration methods in determining water requirements for a crop to survive in each stage that can be used as a trigger level for a WII product. Using daily rainfall and evapotranspiration data, we illustrate the use of Monte Carlo risk modeling to price an operational WII and WII-linked credit product. The risk modeling approach that we develop includes incorporation of correlation between rainfall and evapotranspiration indices that can minimize significant intertemporal basis risk in WII.

Insure or Unsure? Basis Risk Exposure and the Uptake of Weather Index Insurance in Kenya

Insure or Unsure? Basis Risk Exposure and the Uptake of Weather Index Insurance in Kenya

Knowledge, Attitude and Practice among endodontists of Nepal in patient management during COVID-19 arena

Background: Coronavirus disease, 2019 (COVID-19) is an infectious disease caused by the severe acute respiratorysyndrome coronavirus 2 (SARS-CoV-2).COVID-19 is a global pandemic affecting all aspects of life in all countries. Endodontist are at inherent risk because of aerosol generating procedures. The objective of the study was to assess the knowledge, attitude and practice (KAP) among Endodontists of Nepal to combat the COVID 19 pandemic.
 Method:A web-based cross-sectional study was conducted among Endodontist of Nepal from March to June 2021.Convenient sampling method was adopted. The data were analysed using SPSS version 20.Descriptive statistics was performed, percentage and frequency distribution were assessed. Chi-square tests wereused to see the association between the categorical variables. A p-value of <0.05 was considered statistically significant.
 Results: Out of the total 81 Endodontists who responded to our study, 55.6% of them were females and 44.4% males. Around 65%were from Bagmati province.Majority (87.7%) of responders reported that they categorize the cases on the basis risk assessment, 56.8% do not perform routine procedures during the pandemic. Maximum (97.5%) of the responders discuss about COVID-19 with the patients.About 55% of the responders use rubber dam during the endodontic procedures, while 17.3% of the responders do not use them at all.
 Conclusion:Endodontists of Nepal werefound to have good knowledge, attitude and practice score, which is important to overcome the present pandemic scenario of COVID19.Certain pitfalls in attitude and practice level can be decreased by efficient training programs, precise guidelines, and treatment protocols.

Comparison of hydrological and vegetation remote sensing datasets as proxies for rainfed maize yield in Malawi

Weather Index-based Insurances (WIIs) have emerged as a promising risk coping mechanism to compensate for weather-induced damage to rainfed agriculture. Remote sensing may provide cost-effective information capable of discriminating the weather spatial variability thus reducing the spatial basis risk, i.e., the mismatch between the weather-based index triggering the insurance payout and the actual damage experienced by the farmers, which is often one of the causes hindering the wide implementation of WIIs. In this work we assess which indices based on remote sensing datasets are the best proxy indicators for rainfed maize yield in Malawi. We analyse the spatial (district scale) and temporal (monthly) correlations of historical maize yield data and several remote sensing datasets including the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset, the ESA CCI Soil Moisture combined dataset (version 4.2), the Evaporative Stress Index (ESI) from the Atmosphere-Land Exchange Inversion model (ALEXI), the MOD13Q1 Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). With respect to the previous literature, this work exploits a historical crop yield dataset at the sub-national level which allows us to analyse the correlation of the hydro-meteorological and vegetation variables at a higher spatial resolution than what is commonly done (i.e., at the national level using FAO national yield statistics) and ultimately explore the issues related to WII spatial basis risk. Results show that the correlations between crop yield and satellite datasets show high spatial and temporal variability, making it difficult to identify a unique WII index that is at the same time simple and effective for the entire country. Precipitation, particularly the standardized March precipitation anomaly, has the highest correlations with maize yield (with Pearson correlation values higher than 0.55), in Central and South Malawi. Soil moisture and NDVI do not add much value to precipitation in anticipating historical maize yield at the district scale. From a methodological perspective, our work shows that WII indexes are best identified by: i) considering datasets with fine spatial resolution, whenever possible; ii) accounting for the vulnerability of the different crop growing stages to water-stress; iii) distinguishing between water scarce and water abundant events.

Exploring the weather-yield nexus with artificial neural networks

CONTEXTWeather is a pivotal factor for crop production as it is highly volatile and can hardly be controlled by farm management practices. Since there is a tendency towards increased weather extremes in the future, understanding weather-related yield factors becomes increasingly important not only for yield prediction, but also for the design of insurance products. Although insurance products mitigate financial losses for farmers, they suffer from considerable basis risk, i.e., a discrepancy between losses and the indemnity payment. OBJECTIVEThe objective of this paper was to explore the potential of machine learning for estimating the relationship between crop yield and weather conditions at the farm level and to use it as a tool for reducing basis risk in index insurance applications. METHODSAn artificial neural network was set up and calibrated to a rich set of farm-level yield data in Germany, covering the period from 2003 to 2018. A nonlinear regression model, which uses rainfall, temperature, and soil moisture as explanatory variables for yield deviations, served as a benchmark. RESULTS AND CONCLUSIONSThe empirical application revealed that compared with traditional estimation approaches, the gain in forecasting precision by using machine learning techniques was substantial. Moreover, the use of regionalized models and disaggregated high-resolution weather data improved the performance of artificial neural networks. A considerable part of yield variability at the farm level, however, could not be captured by statistical methods which solely use “big weather data”. SIGNIFICANCEOur findings have important implications for the design of weather-index based insurance because they document that a rather high level of basis risk remains if insurance products are based on an estimation of the weather-yield relationship.

Analysis of fixed volume swaps for hedging financial risk at large-scale wind projects

Large scale wind power projects are increasingly selling power directly into wholesale electricity markets without the benefits of stable (fixed price) off-take agreements. As a result, many wind power producers seek financial hedging contracts to mitigate exposure to price risk. One particular hedging contract - the “fixed volume price swap” - has gained widespread use, but it poses several liabilities for wind power producers that reduce its effectiveness. In this paper, we examine problems associated with fixed volume swaps and explore possibilities for improving their performance. Using a hypothetical wind power project in the Southwest Power Pool (SPP) market as a case study, we first look at how “shape risk” (an imbalance between actual wind power production and hourly production targets specified by contract terms) negatively impacts contract performance and whether this could be remedied through improved contract design. Using a multi-objective optimization algorithm, we find examples of alternative contract parameters (hourly wind power production targets) that are more effective at increasing revenues during low performing months and do so at a lower cost than conventional fixed volume swaps. Then we examine how “basis risk” (a discrepancy in market prices between the “node” where the wind project injects power into the grid, and the regional hub price) can negatively impact contract performance. Overall, our results suggest that wind power producers would be better served hedging substantially lower volumes of wind power production, and in certain months should not be hedging at all. Another key finding is that contract performance improves with modest reductions in basis risk. This indicates that eliminating transmission congestion issues across the grid may not be necessary to improve contract performance.

Nexus Between Interest Rate Risk and Economic Value of Equity of Banks

This analytical study looks to provide recommendations to the banking sector on different policies and regulations by examining certain aspects of the Basel III accord, which was designed to manage specific operational, capital and market risks of banks. A review of extant literature reveals that only a few papers have been written on simulation-based approaches, using basis and re-pricing risks. We look to connect this as a source while attempting to define and measure the impact of interest rate risk (IRR) on the economic value of equity (EVE) of banks. We propose to use the driver—driven method, wherein interest rate shocks are derived through prime lending rate (PLR) for the period of 2016–2019 in the context of India. Monte Carlo Simulation and OLS regression was performed to predict the IRR; Granger causality was used to examine the cause and effect relationship; the impulse response function (IRF) was used for sensitivity analysis; and the vector error correction model (VECM) technique was used for co-integrating relationships. Notably, the EVE movement caused due to shocks in interest rates had to be traced as it envisages probable EVE losses. Importantly, our study is among the first few to show the relationship between IRR and EVE of banks, especially after the deregulation of Indian banking sector.

Do high-resolution satellite indices at field level reduce basis risk of satellite-based weather index insurance?

PurposeSatellite-based weather index insurance has recently been considered in order to reduce the high basis risk of station-based weather index insurance. However, the use of satellite data with a relatively low spatial resolution has not yet made it possible to determine the satellite indices free of disturbing landscape elements such as mountains, forests and lakes.Design/methodology/approachIn this context, the Normalized Difference Vegetation Index (NDVI) was used based on both Moderate Resolution Imaging Spectroradiometer (MODIS) (250 × 250 m) and high-resolution Landsat 5/8 (30 × 30 m) images to investigate the effect of a higher spatial resolution of satellite-based weather index contracts for hedging winter wheat yields. For three farms in north-east Germany, insurance contracts both at field and farm level were designed.FindingsThe results indicate that with an increasing spatial resolution of satellite data, the basis risk of satellite-based weather index insurance contracts can be reduced. However, the results also show that the design of NDVI-based insurance contracts at farm level also reduces the basis risk compared to field level. The study shows that higher-resolution satellite data are advantageous, whereas satellite indices at field level do not reduce the basis risk.Originality/valueTo the best of the author’s knowledge, the effect of increasing spatial resolution of satellite images for satellite-based weather index insurance is investigated for the first time at the field level compared to the farm level.

The potential of machine learning for weather index insurance

Abstract. Weather index insurance is an innovative tool in risk transfer for disasters induced by natural hazards. This paper proposes a methodology that uses machine learning algorithms for the identification of extreme flood and drought events aimed at reducing the basis risk connected to this kind of insurance mechanism. The model types selected for this study were the neural network and the support vector machine, vastly adopted for classification problems, which were built exploring thousands of possible configurations based on the combination of different model parameters. The models were developed and tested in the Dominican Republic context, based on data from multiple sources covering a time period between 2000 and 2019. Using rainfall and soil moisture data, the machine learning algorithms provided a strong improvement when compared to logistic regression models, used as a baseline for both hazards. Furthermore, increasing the amount of information provided during the training of the models proved to be beneficial to the performances, increasing their classification accuracy and confirming the ability of these algorithms to exploit big data and their potential for application within index insurance products.

Hedging basis risk in power portfolio

Hedging basis risk in power portfolio

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 rollingǥ 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 an extended 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 hedge effectiveness. We benchmark our strategy against the “delta” hedging strategy as well as its robustness to q-forwards maturity, reference age, interest rate, 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.

Risk transfer beyond reinsurance: the added value of CAT bonds

Reinsurance and CAT bonds are two alternative risk management instruments used by insurance companies. Insurers should be indifferent between the two instruments in a perfect capital market. However, the theoretical literature suggests that insured risk characteristics and market imperfections may influence the effectiveness and efficiency of reinsurance relative to CAT bonds. CAT bonds may add value to insurers’ risk management strategies and may therefore substitute for reinsurance. Our study is the first to empirically analyse if and under what circumstances CAT bonds can substitute for traditional reinsurance. Our analysis of a comprehensive data set comprising U.S. P&C insurers’ financial statements and CAT bond use shows that insurance companies’ choice of risk management instruments is not arbitrary. We find that the added value of CAT bonds mainly stems from non-indemnity bonds and reveal that (non-indemnity) CAT bonds are valuable under high reinsurer default risk, low basis risk and in high-risk layers.

Profit margin hedging in the New Zealand dairy farming industry

This paper explores the effectiveness of Whole Milk Powder futures to protect the net profit margins of New Zealand dairy farms. The proposed strategy for farms suggests selling futures contracts when the current futures price (adjusted for basis risk and commissions) is above the break-even price. We use historical data from 2011 to 2017 and simulate a profit margin hedging strategy with a target price that covers total cash expenses of a representative farm. We find that the representative farm’s payout for this strategy is statistically higher than the continuous hedging and no hedging strategies after accounting for brokerage fees and basis risk. Additionally, we apply the strategy using actual farm-level data and demonstrate that the strategy helps to reduce discretionary cash variance by 35%, semi-variance (downside risk) by 76% and decreases chances of financial distress by 16%. Moreover, we document that the strategy increases mean discretionary cash for farms by 39% and that farms with a high level of leverage experience the biggest improvement. We estimate that if the strategy was adopted by all New Zealand dairy farms over the five year period, it could have generated an additional income of NZD 0.53 billion yearly, on average.