Economic analysis of the introduction of agricultural revenue insurance contracts in Spain using statistical copulas

Ahmed, Osama; Serra, Teresa

doi:10.1111/agec.12141

Cited by 22 publications

(15 citation statements)

References 31 publications

(51 reference statements)

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“…The findings in Chapter 6 showed that premium rates of a revenue insurance contract for the Dutch ware potato sector were much higher than the rates calculated in other studies (e.g. Ahmed and Serra, 2015). The premium rates could be much lower if a coverage rate lower than 75% had been used.…”

Section: Identification Effectiveness and Pricing Of Price Volatilitmentioning

confidence: 66%

“…For instance, using a t copula, Ahmed and Serra (2015) found premium rates of 1.4% for apples and 5.2% for oranges in Spain, for a coverage rate of 75%. Also using a t copula and a coverage rate of 75%, found an average premium rate of 0.3% for corn and 0.1% for soybean.…”

Section: Premium Rates Loss Costs and Loss Probabilities Per Categorymentioning

confidence: 99%

“…Although expected prices could have been generated using time-series models in Chapter 6 (e.g. as in Ahmed and Serra, 2015), the availability of futures price data for the Dutch ware potato sector and the superiority of futures prices over spot prices in terms of quality ) makes futures prices a better measure of expected prices. Besides, futures markets are argued to provide objective reference prices for the rating of agricultural insurance schemes .…”

Section: Expected Pricesmentioning

confidence: 99%

“…Similarly, Ghosh et al (2011) applied copulas to county-level corn yields and futures prices. To the best of our knowledge, the study by Ahmed and Serra (2015) is the only study that has applied copulas to rate an agricultural revenue insurance contract within the EU. Their case studies focused on the Spanish orange and apple markets.…”

Section: Introductionmentioning

confidence: 99%

“…An m-dimensional copula given by ( 1 ( 1 ), 2 ( 1 ), … , ( )) is defined as any multivariate distribution function in the unit hypercube [0; 1] with uniform [0; 1] marginal distributions (Zhu et al, 2008;Ahmed and Serra, 2015). The marginal distributions of 1 , 2 , … , are given by 1 ( 1 ), 2 ( 2 ), … , ( ) respectively, where can refer to any variables for which the dependence structure is of interest (i.e.…”

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confidence: 99%

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The transmission and management of price volatility in food supply chains

Assefa¹

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The 2006-2011 period has been marked by increased volatility in food an agricultural commodity prices at a global level. In the EU, the continuous liberalization of agricultural markets under the Common Agricultural Policy has led to the exposure of EU agricultural to increasing market price volatility. This thesis has investigated the transmission and management of price volatility in EU food supply chains. The transmission of price volatility in various food supply chains is first investigated through a literature review followed by an empirical analysis of price volatility transmission in the case of the German fresh pork supply chain. The effect of market power was also taken into account in the latter empirical analysis. Next, the management of price volatility was investigated through interviews conducted with actors of selected EU food supply chains. This was followed by the analysis of the effectiveness of selected price volatility management strategies. Lastly, in light of the policy support for agricultural insurance within the Common agricultural policy, premium rates of an agricultural revenue insurance contract were calculated for the Dutch ware potato sector.One of the gaps identified in the reviewed literature is the lack of attention given to the effects of contextual factors on price volatility transmissions in food supply chains. Contextual factors include market power in the chain and pricing strategies (e.g. contracts) by chain actors. Results of the price volatility transmission analysis conducted in this thesis in the case of the German pork chain show that retail market power limited both the transmission of price levels and price volatility. This thesis shows that price volatility is perceived as risky by all actors in the food supply chain. Deviations of prices by more than 10 to 15 % from expected levels were perceived as price volatility by a majority of the chain actors. Results further show that price volatility management strategies in EU food chains are diverse and well beyond traditional instruments such as futures and forward contracts. Contrary to expectations, price fixing contracts were not found to be desirable by interviewed chain actors. This thesis also found that the effectiveness of contracts in reducing price volatility depended on how the contract price was set.Results of this thesis further show that premium rates of a revenue insurance contract for the Dutch ware potato sector across categories of farms. The average premium rates calculated were 32.1%, 22.2%, 33.1% and 24.0% on guaranteed revenue per hectare for the high expected yield, low expected yield, high yield variance to expected yield ratio and low yield variance to expected yield ratio categories of farm, respectively. The difference in premium rates across categories of farms implies that charging the same average premium rate to all Dutch ware potato farms can lead to adverse selection.

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Section: Identification Effectiveness and Pricing Of Price Volatilitmentioning

confidence: 66%

Section: Premium Rates Loss Costs and Loss Probabilities Per Categorymentioning

confidence: 99%

Section: Expected Pricesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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The transmission and management of price volatility in food supply chains

Assefa¹

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Insurance risk assessment in the face of climate change: Integrating data science and statistics

Lyubchich

Newlands

Ghahari

et al. 2019

WIREs Computational Stats

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Local extreme weather events cause more insurance losses overall than large natural disasters. The evidence is provided by long‐term observations of weather and insurance records that are also a foundation for the majority of insurance products covering weather related damages. The insurers around the world are concerned, however, that the past records used to assess and price the risks underestimate the risk and incurred losses in recent years. The growing insurance risks are largely attributed to climate change that brings increasingly more alterations and permanent impact on all aspects of human life and welfare. From floods to hail to excessive wind, adverse atmospheric events are a poignant reminder of how vulnerable our society is across a broad range of threats posed by environmental extremes. Indeed, as climate change effects become more pronounced, we face a new era of risk with increasing weather related damages and losses. This in turn, coupled with challenges of massive climatic data, requires developing innovative analytic approaches that transcend traditional disciplinary boundaries of statistical, actuarial and environmental sciences. Nevertheless, the multidisciplinary nature of climate risk assessment and its impact on insurance is often overlooked and neglected. We highlight the most recent developments and interdisciplinary perspectives on diverse statistical and machine learning methodology for modeling and assessing climate risk in agricultural and home insurances, with a particular focus on noncatastrophic events. This article is categorized under: Applications of Computational Statistics > Computational Climate Change and Numerical Weather Forecasting Statistical and Graphical Methods of Data Analysis > Multivariate Analysis Data: Types and Structure > Massive Data

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