Risk Management for Grain Processors and “Copulas”

The importance of calibrating hedging strategies for processors has escalated primarily due to the sharply increased volatility of futures, product, and by‐product prices. The purpose of this paper is to analyze price risk‐management strategies for wheat flour milling using copula distributions. While the application is for flour milling, it has similarities with other processing industries which confront one or more ingredients, one or more outputs, and futures for one of the commodities and/or products. The paper develops utility maximizing models encompassing expected return and risk. Alternative scenarios are evaluated. First, the models were used to derive optimal hedge ratios, as well as various measures of risk and return under alternative scenarios, and hedge durations. The results indicated hedge ratios are typically less than 1. The hedge ratios for the Mean‐value‐at‐risk (M‐VaR)‐Copula model increased with greater durations. Second, the VaR for the M‐VaR‐Copula was in most cases less than the noncopula specifications. Thus, noncopula models may over state risk as represented by VaR.     

Managing basis risk with multiscale index insurance

Agricultural index insurance indemnifies a farmer against losses based on an index that is correlated with, but not identical to, her or his individual outcomes. In practice, the level of correlation may be modest, exposing insured farmers to residual, basis risk. In this article, we study the impact of basis risk on the demand for index insurance under risk and compound risk aversion. We simulate the impact of basis risk on the demand for index insurance by Malian cotton farmers using data from field experiments that reveal the distributions of risk and compound risk aversion. The analysis shows that compound risk aversion depresses demand for a conventional index insurance contract some 13 percentage points below what would be predicted based on risk aversion alone. We then analyze an innovative multiscale index insurance contract that reduces basis risk relative to conventional, single‐scale index insurance contract. Simulations indicate that demand for this multiscale contract would be some 40% higher than the demand for an equivalently priced conventional contract in the population of Malian cotton farmers. Finally, we report and discuss the actual uptake of a multiscale contract introduced in Mali.     

Crop-Yield Distributions Revisited

This article revisits the issue of crop-yield distributions using improved model specifications, estimation, and testing procedures that address the concerns raised in recent literature, which could have invalidated previous findings of yield nonnormality. It concludes that some aggregate and farm-level yield distributions are nonnormal, kurtotic, and right or left skewed, depending on the circumstances. The advantages of utilizing nonnormal versus normal probability distribution function models, and the consequences of incorrectly assuming crop-yield normality are explored.     

Risk Preferences of Dairy and Hog Producer in Quebec

The objective of this study is to investigate the nature of risk preferences of Quebec dairy and hog producers. The direct elicitation of utility method is employed to determine producers'degree of risk aversion. The Delphi process is used to obtain more refined and realistic responses. The results reveal that the risk preferences of the randomly selected Quebec farmers are highly diverse. The percentage of risk-taking farmers ranges from 8% to 23% depending upon the level of investment and the nature of the enterprise. On average, the majority of farmers in both groups are found to be risk averse. Based on the differences between the means as well as distributions, although not significant in all cases, hog producers are found to be consistently more risk averse than dairy producers. Moreover, the gap between the two groups widens as the level of investment increases. The implications of this result are that the stability of farm income due to supply management in dairy sector may facilitate investments of a given risk (for example, adoption of a new technology) more so than it would in the hog sector.     

Transport infrastructure equities in mixed-asset portfolios: estimating risk with a Garch-Copula CVaR model

Transport infrastructure is an important subsector within infrastructure, but knowledge of its equities in terms of risk-return characteristics and contribution to portfolio performance is still limited. This study assesses the subsector individually and in a multi-asset, index-based portfolio. In doing so, we apply a t-Copula-based Conditional Value-at-Risk model to simulate risk and returns. Our findings reveal that the subsector has a relatively low dependency on other equities, performs like other alternative asset classes such as general real estate, and does not grant significant risk diversification benefits for mainstream institutional investors such as pension funds. Investors aiming for higher target returns may however assign substantial weights to transport infrastructure, supporting our conjecture that it does not share the same asset class characteristics as general infrastructure. By contrasting Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) scores for both the mean-variance framework and the t-Copula simulation, we further document the limitations of traditional VaR approaches. Hence, this study’s results support the use of risk assessment tools that incorporate non-normal distributions to represent multivariate dependence structures.     

THE EFFECT OF FERTILISER ON RISK: A HETEROSCEDASTIC PRODUCTION FUNCTION WITH MEASURABLE STOCHASTIC INPUTS

The sources of production risk are many and diverse in nature. Estimating risk as a black box, without explicit recognition of its sources, can lead to inferior estimates of optimal inputs under risk aversion. In this paper, a method is presented for estimating production functions with measurable stochastic inputs and for generating the parameters of the probability distributions of yield for various environments and input levels. Based on this method, it appears that moderate risk aversion can account for a 6.7 per cent to 16.7 per cent reduction in nitrogen use (relative to the risk-neutral solution) for selected rice producing areas of the Philippines. Estimating optimal inputs without environment specific information about the sources of risk leads to large errors. This underscores the value of collecting information about the sources of risk and of exercising caution when such information is not available.     

Measuring Price Risk on UK Arable Farms

Price risk is estimated for a representative UK arable farm using value‐at‐risk (VaR). To determine the distribution of commodity returns, two multivariate generalised autoregressive conditional heteroscedasticity (GARCH) models, with t‐distributed and normally distributed errors, and a RiskMetrics^TM model are estimated. Returns show excess kurtosis and that the GARCH model with t‐distributed errors fits best. Estimates of VaR differ between models: both GARCH models perform well but the RiskMetrics^TM model underestimates expected losses. UK arable farms face substantial price risk.     

Efficient Estimation of Agricultural Time Series Models with Nonnormal Dependent Variables

This article proposes using an expanded form of the Johnson S_U family as a way to approximate nonnormal distributions in regression models. The distribution is one of the few that allows modeling heteroskedasticity and autocorrelation. The technique is evaluated with Monte Carlo simulation and illustrated through an empirical model of the West Texas cotton basis. Given nonnormality, this technique can substantially reduce the variance of slope parameter estimates relative to least squares procedures.     

Non-parametric estimation of decision makers' risk aversion

A new non‐parametric method to estimate a decision maker's coefficient of absolute risk aversion from observed economic behaviour is explained. The method uses the expected value‐variance (E‐V) framework and quadratic programming. An empirical illustration is given using Norwegian farm‐level data.     

欧盟碳交易市场收益率厚尾特征与极端风险度量研究

选取欧盟碳金融市场收益率数据作为研究对象,基于极值理论通过对数据建立区组极大值模型(BMM)和超阈值模型(POT)来拟合收益率数据的尾部。结果表明:正态分布不能很好地描述欧盟碳金融市场收益率数据特征,而基于极值理论地的BMM和POT模型可以较好地拟合数据尾部;基于正态性假定下计算的风险度量指标,在高置信水平下会低估尾部数据的风险水平;基于极值理论的模型可以更好地拟合碳金融市场收益率数据,由此得到的风险度量指标可以更好地帮助风险管理者们监控和应对风险。     

Trends in Approval Times for Genetically Engineered Crops in the United States and the European Union

Genetically engineered (GE) crops are subject to regulatory oversight to ensure their safety for humans and the environment. Their approval in the European Union (EU) starts with an application in a given Member State followed by a scientific risk assessment, and ends with a political decision‐making step (risk management). In the United States (US) approval begins with a scientific (field trial) step and ends with a ‘bureaucratic’ decision‐making step. We investigate trends for the time taken for these steps and the overall time taken for approving GE crops in the US and the EU. Our results show that from 1996–2015 the overall time trend for approval in the EU decreased and then flattened off, with an overall mean completion‐time of 1,763 days. In the US in 1998 there was a break in the trend of the overall approval time. Initially, from 1988 until 1997 the trend decreased with a mean approval time of 1,321 days; from 1998–2015, the trend almost stagnated with a mean approval time of 2,467 days.     

More reasons why farmers have so little interest in futures markets

The use by farmers of futures contracts and other hedging instruments has been observed to be low in many situations, and this has sometimes seemed to be considered surprising or even mysterious. We propose that it is, in fact, readily understandable and consistent with rational decision making. Standard models of the decision about optimal hedging show that it is negatively related to basis risk, to quantity risk, and to transaction costs. Farmers who have less uncertainty about prices and those with a diversified portfolio of investments have lower optimal levels of hedging. If a farmer has optimistic price expectations relative to the futures market, the incentive to hedge can be greatly reduced. And finally, farmers who have low levels of risk aversion have little to gain from hedging in terms of risk reduction, in that the certainty‐equivalent payoff at their optimal hedge may be little different than the certainty equivalent under zero hedging. These reasons are additional to the argument of Simmons (2002) who showed that, if capital markets are efficient, farmers can manage their risk exposure through adjusting their leverage, obviating the need for hedging instruments.     

Implications of biotech traits with segregation costs and market segments: the case of Roundup Ready(R) Wheat

Roundup Ready^® Wheat (RRW) is one of the first geneticallymodified (GM) traits for the wheat sector. We develop a spatialpartial equilibrium model of the higher-protein hard wheat marketand assess the changes in the distribution of welfare associatedwith release and adoption of RRW. It incorporates segments forGM aversion in each market and segregation costs for each segment.In the most likely scenario, producer and consumer welfare increasesby $301 and $252 million, respectively. Producers of hard redspring wheat in the US and Canada gain. There are welfare lossesto hard red winter wheat growers in the US and to EU consumerswho have to import at a higher cost.     

Incorporating risk in a positive mathematical programming framework: a dual approach

In this study we develop a new methodological proposal to incorporate risk into a farm‐level positive mathematical programming (PMP) model. We estimate simultaneously the farm nonlinear cost function and a farmer‐specific coefficient of absolute risk aversion as well as the resource shadow prices. The model is applied to a sample of representative arable crop farms from the Emilia‐Romagna region in Italy. The estimation results confirm the calibration ability of the model and reveal the values of the individual risk aversion coefficients. We use the model to simulate different scenarios of crop price volatility, in order to explore the potential risk management role of an agri‐environmental scheme.     

Are we risking too much? Perspectives on risk in farm modeling

Risk and uncertainty have been extensively studied by agricultural economists. In this paper we question (a) the predominant use of static frameworks to formally analyse risk; (b) the predominant focus on risk aversion as the motivation for considering risk and (c) the notion that explicitly probabilistic models are likely to be helpful to farmers in their decision making. We pose the question: for a risk‐averse farmer, what is the extra value of a recommendation derived from a model that represents risk aversion, compared to a model based on risk neutrality? The conclusion reached is that for the types of the decision problems most commonly modelled by agricultural economists, the extra value of representing risk aversion is commonly very little.     

Stochastic efficiency analysis with risk aversion bounds: a correction

A recent paper by Hardaker et al. (The Australian Journal of Agricultural and Resource Economics, 48, 2004a, 253) and book by Hardaker et al. (Coping with Risk in Agriculture, 2004b) describe a procedure for determining an efficient set from among a set of random alternatives. This procedure, called stochastic efficiency with respect to a function (SERF), is claimed to make the same assumption concerning the risk aversion measures as does stochastic dominance with respect to a function (SDRF). This is claim is incorrect. SERF imposes an additional requirement on the risk aversion measures of the decision makers. Both procedures assume a lower and an upper bound on risk aversion, but SERF also assumes that all risk aversion measures are of the same functional form as these lower and upper bound functions. This additional strong requirement on risk preferences implies that the efficient set identified under SERF is usually smaller than that identified using SDRF.     

基于风险价值法的蔬菜市场风险度量与评估——以北京蔬菜批发市场为例

本文以北京市蔬菜批发市场为例,以油菜、大白菜、黄瓜、苦瓜、番茄、柿子椒、云架豆、荷兰豆、胡萝卜、土豆10种蔬菜的价格变化为研究对象,利用风险价值法(VaR法)度量了蔬菜市场的风险大小,并评估了蔬菜市场的风险发生程度。分析结果表明:蔬菜市场是一个风险发生频繁且发生程度较高的一类农产品市场;正态分布并不是拟合蔬菜市场风险的最优分析模型,不同品种的蔬菜,其市场风险发生程度有所不同,其中,叶菜类蔬菜的市场风险程度较高;蔬菜价格上涨风险与下跌风险同等重要,并且两者的发生频率基本相当,在关注价格上涨风险的同时更要重视价格下跌风险。     

Moral Hazard and Risk Management in Agri‐environmental Policy

This paper develops the key finding of Ozanne, Hogan and Colman (2001) that risk aversion among farmers ameliorates the moral hazard problem in relation to agrienvironmental policy compliance. It is shown that risk averse farmers who face uncertainty in their production income are more likely to comply with such a policy as a means of risk management. In addition, it is shown that a principal who has control over both the level of monitoring and the size of penalty, if detected, can reduce non‐compliance by adjustments to these instruments which increase the variance of farmers' income but leave the expected penalty unchanged. It is concluded that risk management by both principals and agents has the potential to diminish the moral hazard problem, especially given proposed developments in agri‐environmental policy in the European Union.     

Evaluating the Potential of Index Insurance Schemes to Reduce Crop Yield Risk in an Arid Region

We evaluate yield risk reduction through weather index, area yield index and farm yield insurance contracts for wheat farms in Kazakhstan by employing data from 1980 to 2002. We use the usual mean variance (MV) approach and also a second‐degree stochastic dominance (SSD) criterion. While MV is not necessarily consistent with the expected utility (EU) theory, SSD results only in a minimum but EU‐consistent benefit from insuring. Differences in the estimation results for both approaches underline the advantage of applying both criteria to analyse the risk‐reducing potential of crop insurance. Bootstrapping results show that none of the analysed insurance schemes provides statistically significant risk reduction for every single farm. In addition, weather‐based index insurance is found to provide less risk reduction than area yield insurance based on the rayon (county) yield. Moreover, rayon yield index insurance can reduce yield risk more effectively for Kazakhstan's wheat producers than farm yield insurance with a low strike yield.     

RISK ATTITUDES AMONGST AUSTRALIAN FARMERS

A procedure for the measurement of risk attitudes is developed and applied. The data for the analysis were obtained from a survey in which 201 farmers throughout Australia were asked to provide points of indifference between sure amounts of income and risky prospects. Although the conclusions from this pilot study are of a tentative nature, it is suggested that risk aversion is the most prevalent risk attitude in the agricultural sector. However, the average degree of risk aversion is relatively small and, in an expected utility context, farmers gave a wide variety of responses. The latter result highlights the need to consider the size distribution of risk attitudes in economic modelling. Influences of socioeconomic and other variables on risk attitudes are examined. The results, when considered jointly with other studies, emphasise the desirability of further research into the determination of risk attitudes.