Modules 1601 and 1602
Risk Analysis and Risk Management in Agriculture:
Theory and Applications
Prof. Dr. Martin Odening, Farm Management Group
Prof. Dr. Matthias Ritter, Quantitative Agricultural Economics
Humboldt-Universität zu Berlin, Faculty of Agriculture and Horticulture, Department of Agricultural Economics
Prof. Dr. Oliver Mußhoff, Farm Management
Georg-August-University Göttingen, Faculty of Agricultural Sciences, Department for Agricultural Economics and Rural Development,
Prof. Dr. Robert Finger, Agricultural Economics and Policy
ETH Zürich, Department of Management, Technology and Economics
In addition, several guest lectures will contribute to specific topics of the course.
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.
- Classification of risk sources
- Components of a risk management system
- Distributions: expected value, variance, tests
- Stochastic processes: diffusion processes, jump processes, GARCH, characteristics, tests
- Stochastic simulation: implementation, variance reduction procedures
- Value at Risk (VaR), Expected Tail Loss (ETL), Extreme Value Theory (EVT)
- Stochastic dependence and copulas
- Expected utility theory, expected value variance analysis, stochastic dominance
- Risk programming
- Optimal input use under risk
- State contingent approach
- Term structure
- Convenience yield
- Hedging with futures
- Theoretical and empirical analysis of crop insurance
- Ito's Lemma
- Stochastic dynamic programming (Hamilton-Jacobi-Bellman-equation)
- Binominal trees, Genetic algorithms
- Classification, valuation, applications
- Empirical validation of real options models
- Risk neutral valuation, no-arbitrage-pricing
- Pricing in incomplete markets
- Modeling weather related risks
- Pricing of weather derivatives
Lectures (40%), seminars (20%), PC-demonstrations (20%), hands-on-exercises (20%)
Grading: Presentation (40%), assignments (40%), participation (20%)
Credit points: 6
Requirements: Statistics, microeconomics (master level), good MS-EXCEL skills
- 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 and STATGRAPHICS
Organization and time
The course is split up into two one-week-modules. They are held at Humboldt Universität zu Berlin and Georg-August-Universität Göttingen.
For further information contact Martin Odening (email@example.com, 030209346842).