Module 1602
Risk Analysis and Risk Management in Agriculture:
Theory and Applications

Instructors

Prof. Dr. Oliver Mußhoff, Farm Management
Georg-August-University Göttingen, Faculty of Agricultural Sciences, Department for Agricultural Economics and Rural Development, oliver.musshoff@uni-goettingen.de

Prof. Dr. Robert Finger, Agricultural Economics and Policy
ETH Zürich, Department of Management, Technology and Economics

Module Description

Many decisions in agriculture and related industries have to be made under uncertainty. Risk, however, adds a considerable degree of complexity to the decision making process. This course presents the theoretical concepts of rational choice under uncertainty and develops the building blocks for risk management in agribusiness. Once the necessary statistical tools are reviewed, various indicators of the firm's risk exposition are discussed, including Value at Risk (VaR) and Expected Tail Loss (ETL). Moreover, the foundations of stochastic optimization are presented. The theoretical part enables the participants to understand the most important risk management tools, as insurance, futures, financial options, real options or weather derivatives. Much emphasis is put on the numerical aspects of risk management. For example, participants will be practically advised to use stochastic simulation and genetic algorithms.

Course Outline

1. Introduction
   • Classification of risk sources
   • Components of a risk management system
2. Statistical Tools
   • Distributions: expected value, variance, tests
   • Stochastic processes: diffusion processes, jump processes, GARCH, characteristics, tests
   • Stochastic simulation: implementation, variance reduction procedures
3. Risk Measurement
   • Value at Risk (VaR), Expected Tail Loss (ETL), Extreme Value Theory (EVT)
   • Stochastic dependence and copulas
4. Decision Theory
   • Expected utility theory, expected value variance analysis, stochastic dominance
   • Risk programming
5. Production Risk
   • Optimal input use under risk
   • State contingent approach
6. Price Risk
   • Term structure
   • Convenience yield
   • Hedging with futures
7. Insurance
   • Theoretical and empirical analysis of crop insurance
8. Stochastic Dynamic Optimization
   • Ito's Lemma
   • Stochastic dynamic programming (Hamilton-Jacobi-Bellman-equation)
   • Binominal trees, Genetic algorithms
9. Real Options
   • Classification, valuation, applications
   • Empirical validation of real options models
10. Contingent Claim Analysis
    • Risk neutral valuation, no-arbitrage-pricing
    • Black-Scholes-model
    • Pricing in incomplete markets
11. Weather Derivatives
    • Modeling weather related risks
    • Pricing of weather derivatives

Teaching methods:

Lectures (40%), seminars (20%), PC-demonstrations (20%), hands-on-exercises (20%)

Grading: Presentation (40%), assignments (40%), participation (20%)

Credit points: 3

Requirements: Statistics, microeconomics (master level), good MS-EXCEL skills

References

• Chavas, J.P. (2004): Risk Analysis in Theory and Practice. Elsevier.
• Dixit, A., Pindyck, R.S. (1994): Investment under Uncertainty. Princeton University Press, Princeton.
• Dowd, K. (2002): Measuring Market Risk. John Wiley & Sons, LTD.
• Hardaker, J. B., Huirne, R. B. M., Anderson, J.R., Lien, G. (2004): Coping with Risk in Agriculture. Second Edition. CAB International. Wallingford.
• Hull, J.C. (2000): Options, Futures, & other Derivatives. Prentice-Hall, Ic.
• Mußhoff, O., Hirschauer, N. (2003): Bewertung komplexer Optionen - Umsetzung numerischer Verfahren mittels MS-EXCEL und Anwendungsmöglichkeiten der Optionspreistheorie auf Sachinvestitionen. PD-Verlag, Heidenau.
• Neftci, N.S. (1996): An Introduction to the Mathematics of Financial Derivatives. Academic Press, San Diego.
• Robinson, L.J., Barry, P.J. (1987): The Competitive Firm's Response to Risk. Macmillan, London.

Software: MS-EXCEL, R (www.r-project.org), IBM SPSS Statistics, Palisade The DecisionTools Suite

Language: English