Using Cash Flow Risk Modelling to Add Value: a case study
by Professor Evan Gilbert, Associate Professor (Finance), Graduate School of Business, University of Cape Town
Using a Monte Carlo cash flow risk model for a bulk chemicals producer in South Africa we identified the company’s implicit USD / ZAR exposure as driving 45% of its cash flow volatility. Our analysis suggested that if this exposure is hedged, the company’s total debt can be increased by 33% with a risk of default on its new debt commitments that is lower than is currently the case. The reduction in the downside Net Present Value offered by the hedge was 2.5 times the investment required for the (relatively expensive) hedging strategy modelled. Our approach demonstrates how traditional treasury specific risk management techniques can be directly applied to operational risk measurement and mitigation with value creating results.
Company X is a bulk chemicals manufacturer based in South Africa. It sources its inputs from a local refinery to produce two commodity bulk chemicals for sale in the local economy. Sixty per cent of its capital is currently funded by debt. This makes the question of stable cash flows extremely important for management. It was considering increasing its debt levels further, but was uncertain about its ability to support this additional debt. Through the use of cash flow risk modelling we were able to demonstrate how this might be possible through the targeted use of hedging.
We developed a seven- year risk model of the cash flows of the company based on a combination of the financial projections provided to us by Company X management and external consultants. Following discussions with management, we identified the following key risk drivers:
- International prices for its two main products (in USD)
- USD/ZAR exchange rates1
- Volumes of production of its two products (A and B)
- The gross margins (GM) on its two products (A and B)
We obtained estimates for the appropriate distributions for each of these risk drivers from historical data and tested the continued validity of the distributions with Company X management. Using Monte Carlo analysis we simulated the range of possible cash flow outcomes for the company over the seven year period. This allowed us to calibrate the effects of each of the sources of risk identified above as well as their combined effects on the company’s ability to meet its current (and possible future) debt commitments.