Volatility Forecasting and Value-at-Risk: An Application to Cattle Feeding

Based on mean-squared error criteria, the overall conclusion of the volatility forecasting exercise mirrors that found in the literature: performance of any volatility forecast is both data and horizon specific. However, composite techniques, especially simple composites that combine both conditional time series and implied volatility forecasts, perform well here. Interestingly, correlations, not volatility forecasts, were found to be the dominant factor influencing various Value-at-Risk measures ability to forecast large losses in the cattle feeding margin. Variances and correlations developed using the JP Morgan's Risk Metrics method with a decay factor of 0.97 provided superior Value-at-Risk estimates among other well calibrated specifications. This research is one of the few known empirical applications of composite volatility forecasting and the first known application of Value-at-Risk in the context of agriculture.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Agricultural Economics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Manfredo, Mark Ronald

Contributors dc:contributor

• Leuthold, Raymond M.

• Economics, Finance

Language dc:language

eng

Identifier

(MiAaPQ)AAI9921713

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/83029