### 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)

- 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. [[[PAGE]]]

In terms of the relative impact of these sources of risk, the outcome of this analysis is captured in Figure 1. The graph on the left shows the relative contribution of the likely variation each factor to the variation in the investment’s Internal Rate of Return (IRR), and to Net Present Value (NPV) in the graph on the right. The range illustrated reflects a 80% confidence interval for each source of risk and the point the expected value as used in the base case analysis.

The use of this type of analysis allowed management to prioritise, on a data driven basis, the sources of financial risk facing the company. It clearly showed the importance of the ZAR/USD exchange rate - this is the only individual source of risk which could drive the investment into negative NPV territory. This gave the company the ability to focus its risk mitigation efforts in a targeted way.

We also used this risk model to evaluate Company X’s current ability to repay its existing debt related cash flows in any particular year. Note that under advice from management we included in this analysis the use of a currently unused R100m overdraft facility. This was a relatively conservative approach - it looks at each year independently and ignores any flexibility that management has over its cash flow management. Thus it probably over-states the likelihood of default for the company.

Figure 2 indicates the distribution of net cash flows after meeting the debt repayments per year modelled. All the outcomes above -R100m are highlighted in blue. These represent the years where the company is able to meet its debt commitments in that year. The bars in red reflect years where the company is technically in default. The number in the middle of the x-axis reflects the sum of the blue bars. In this case it suggests that the probability that the company would able to meet its current debt commitments (using the R100m facility) as approximately 65% in this case (i.e. given the current debt structure).

This application was revolutionary for management as it provided them with the first data driven analysis of their risk of default. This was a vital tool in terms of them being to able to evaluate the sustainability of their current capital structure as well as consider the impact (in terms of risk) of proposed changes to it.

The final piece of the puzzle was the modelling of the potential benefits of hedging. Our risk model allowed to us to quantify the expected benefits of hedging the exchange rate risk. To do so, we modelled the effects (and costs) of a very simple (and relatively expensive) hedging strategy where Company X completely hedges its expected implicit exposure by using USD/ZAR put options. This insurance comes at a price (R165m in this case). We re-ran the risk model with the hedge (and the costs) in place. [[[PAGE]]]

We assessed the impact of this hedge in two ways. Firstly, its effects on the risk of default are dramatic. The probability of default (with the hedge) given the current capital structure is 12% compared to 35% previously. The hedge has reduced our probability of default by two thirds.

Secondly, we examined the effects of the hedge on the distribution of possible company valuations. The graph on the left hand side of Figure 3 shows the effects of the hedge on the range of possible cash flow outcomes (as measured, in this case, by Earnings Before Interest, Depreciation and Amortization - EBITDA). The shaded area reflects the reduced downside resulting from the hedge. The right hand graph reflects the impact of the reduced downside risk - the lower bound of the 80% confidence interval is approximately R400m higher with the hedge than without it.

Does this mean the company can take on more debt? The lower volatility of the hedged cash flows should give the company the confidence to do so. They indicated an interest in increasing their level of debt by 33%. We modelled the risk of default using the hedged cash flows. The expected default probability goes to 25%. This is much higher than the 12% for the existing levels of debt (with the hedge), but still better than the original 32% (without the hedge).

From an investor’s point of view the expected seven year IRR increases from 35% to 53%. This is directly the result of the use of the hedging strategy: the additional financial risk of the higher level of debt is offset by the reduction in operational cash flow risk due to the hedging strategy.

Our analysis indicated that increasing the level of debt in this company is possible but only if the company hedges its cash flows. Our analysis allowed management to clearly identify their key cash flow risk driver - in this case, their USD/ZAR exposure. As it is possible to hedge this risk, we were able to show the benefits of such a course of action for the company. Through the use of risk measurement and management skills normally found in the Treasury of the company management of Company X now know that implementing a forex hedge can either help them sleep better at night, or it can also allow for higher expected returns due to a higher level of financial leverage. Our approach allowed us to estimate the levels of risk associated with the revised capital structure and it turned out to be less than the existing state of affairs.

We believe that this case study clearly shows the value of risk modelling for management. If management understand the drivers of their company’s cash flows and the sensitivities of these to market volatilities they can optimise their capital structures. It is possible, it seems, to get higher expected returns with lower levels of risk - but only if you know where to hedge.

*1 Note that while Company X does not import or export products; its local input and output prices are set as the ZAR equivalents of the international USD prices and it has an effective ZAR/USD exchange rate exposure*