P&L attribution for energy portfolios with non-linear exposures
Carlos Blanco and Alessandro Mauro explain how non-linear P&L attribution tools can improve a company’s business intelligence capabilities, be an effective way of benchmarking mark-to-model values, and identify key sources of risk and return on energy portfolios
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Carlos Blanco is managing director at Ascend Analytics and faculty at the Oxford Princeton Programme, while Alessandro Mauro is risk officer at MKS and faculty at the Master in Commodity Trading, University of Geneva.
In this article, we introduce a framework to perform profit and loss (P&L) attribution for portfolios that contain non-linear market exposures. In a linear portfolio, the P&L is fully described by changes in volumes and market prices. The effect of the
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