Charities and non-profit organizations face distinct challenges when processing donations and payments across multiple countries. In this sector, the impact of currency exchange losses is not simply about the effect on an organization’s financial performance, there’s also the potential disruption to projects to consider when budgets are at risk. Zanders developed a ‘Budget at Risk’ model to help a non-profit client with worldwide operations to better forecast the potential impact of currency fluctuations on their operating budget. In this article, we explain the key features of this model and how it's helping our client to forecast the budget impact of currency fluctuations with confidence.

The client in question is a global non-profit financed primarily through individual contributions from donors all over the world. While monthly inflows and outflows are in 16 currencies, the organization’s global reserves are quantified in EUR. Consequently, their annual operating budget is highly impacted by foreign exchange rate changes. To manage this proactively demands an accurate forecasting and assessment of: 

• The offsetting effect of the inflows and outflows.  

• The diversification effect coming from the level of correlation between the currencies.  

With the business lacking in-house expertise to quantify these risk factors, they sought Zanders’ help to develop and implement a model that would allow them to regularly monitor and assess the potential budget impact of potential FX movements.

Developing the BaR method

Having already advised the organization on several advisory and risk management projects over the past decade, Zanders was well versed in the organization’s operations and the unique nature of the FX risk it faces. The objective behind developing Budget at Risk (BaR) was to create a model that could quantify the potential risk to the organization’s operating budget posed by fluctuations in foreign exchange rates.  

The BaR model uses the Monte Carlo method to simulate FX rates over a 12-month period. Simulations are based on the monthly returns on the FX rates, modelled by drawings from a multivariate normal distribution. This enables the quantification of the maximum expected negative FX impact on the company’s budget over the year period at a certain defined level of confidence (e.g., 95%). The model outcomes are presented as a EUR amount to enable direct comparison with the level of FX risk in the company’s global reserves (which provides the company’s ‘risk absorbing capacity’). When the BaR outcome falls outside the defined bandwidth of the FX risk reserve, it alerts the company to consider selective FX hedging decisions to bring the BaR back within the desired FX risk reserve level. 

The nature of the model 

The purpose of the BaR model isn’t to specify the maximum or guaranteed amount that will be lost. Instead, it provides an indication of the amount that could be lost in relation to the budgeted cash flows within a given period, at the specified confidence interval. To achieve this, the sensitivity of the model is calibrated by: 

• Modifying the confidence levels. This changes the sensitivity of the model to extreme scenarios. For example, the figure below illustrates the BaR for a 95% level of confidence and provides the 5% worst-case scenario. If a 99% confidence level was applied, it would provide the 1% worst (most extreme) case scenario.  

• Selecting different lengths of sample data. This allows the calculation of the correlation and volatility of currency pairs. The period length of the sample data helps to assess the sensitivity to current events that may affect the FX market. For example, a sample period of 6 months is much more sensitive to current events than a sample of 5 years.  

Figure 1 – BaR for a 95% level of confidence 

Adjusting these parameters makes it possible to calculate the decomposition of the BaR per currency for a specified confidence level and length of data sample. The visual outcome makes the currency that’s generating most risk quick and easy to identify. Finally, the diversification effect on the BaR is calculated to quantify the offsetting effect of inflows and outflows and the correlation between the currencies. 

Table 1 – Example BaR output per confidence level and length of data sample 

Pushing parameters 

The challenge with the simulation and the results generated is that many parameters influence the outcomes – such as changes in cash flows, volatility, or correlation. To provide as much clarity as possible on the underlying assumptions, the impact of each parameter on the results must be considered. Zanders achieves this firstly by decomposing the impact by: 

• Changing FX data to trigger a difference in the market volatility and correlation. 

• Altering the cash flows between the two assessment periods. 

Then, we look at each individual currency to better understand its impact on the total result. Finally, additional background checks are performed to ensure the accuracy of the results. 

This multi-layered modelling technique provides base cases that generate realistic predictions of the impact of specific rate changes on the business’ operating budget for the year ahead. Armed with this knowledge, we then work with the non-profit client to develop suitable hedging strategies to protect their funding. 

Leveraging Zanders’ expertise 

FX scenario modelling is a complex process requiring expertise in currency movements and risk – a combination of niche skills that are uncommon in the finance teams of most non-profit businesses. But for these organizations, where there can be significant currency exposure, taking a proactive, data-driven approach to managing FX risk is critical. Zanders brings extensive experience in supporting NGO, charity and non-profit clients with modelling currency risk in a multiple currency exposure environment and quantifying potential hedge cost reduction by shifting from currency hedge to portfolio hedge.  

For more information, visit our NGOs & Charities page here, or contact the authors of this case study, Pierre Wernert and Jaap Stolp.

In a world of persistent market and economic volatility, the Corporate Treasury function is increasingly taking on a more strategic role in navigating the uncertainties and driving corporate success.

Even in the most mature organizations, the involvement of the Treasury center in FX risk management often begins with collecting forecasted exposures from subsidiaries. However, to fundamentally enhance the performance of the FX risk management process, it is crucial to understand the nature of these FX exposures and their impacts on the upstream business processes where they originate.

Enabling this requires the optimization of the end-to-end FX hedging lifecycle, from subsidiary financial planning and analysis (FP&A) that identifies the exposure to Treasury hedging. Improvements in the exposure identification process and FX impact analytics necessitate the use of intelligent systems and closer cooperation between Treasury and business functions.

Traditional models

While the primary goal of local business units is to enhance the performance of their respective operations, fluctuating FX rates will always directly impact the overall financial results and, in many cases, obscure the true business performance of the entity. A common strategy to separate business performance from FX impacts is to use constant budgeting and planning rates for management reporting, where the FX impact is nullified. These budgeting and planning rates typically reflect the most likely hedged rates achieved by Treasury, considering the hedging policies and forecasted hedging horizons. However, this strategy can lead to unexpected shocks in financial reporting and obscure the impacts of FX exposure forecasting and hedging performance.

When these shocks occur, conclusions about their causes, such as over or under-hedging or unrealistic planning rates, can only be drawn through retrospective analysis of the results. Unfortunately, this analysis often comes too late to address the underlying issues.

The most common Treasury tools used to measure the accuracy of business forecasting are Forecast vs. Forecast and Actual vs. Forecast accuracy reporting. These tools help identify recurring trouble areas that may need improvement. However, while these metrics indicate where forecasting accuracy can be improved, they do not easily translate into a quantification of the predicted or actual financial impact required for business planning purposes.

End-to-End FX risk management in a Treasury 4.x environment

Finance transformation projects, paired with system centralization and standardization, may offer an opportunity to create better integration between Treasury and its business partners, bridging the information gap and providing better insight and early analysis of future FX results. Treasury systems data related to hedging performance, together with improved up-to-date exposure forecasting, can paint a clearer picture of the up-to-date performance against the plan.

While some principles may remain the same, such as using planning and budgeting rates to isolate the business performance for analysis, the expected FX impacts at a business level can equally be analyzed and accounted for as part of the regular FP&A processes, answering questions such as:

• What is the expected impact of over- or under-hedging on the P&L?
• What is the expected impact from late hedging of exposures?
• What is the expected impact from misaligned budgeting and planning rates compared to the achieved hedging rates?

The Zanders Whitepaper, "Treasury 4.x – The Age of Productivity, Performance, and Steering," outlines the enablers for Treasury to fulfill its strategic potential, identifying Productivity, Performance, and Steering as key areas of focus.

In the area of Performance, the benefits of enhanced insights and up-to-date metrics for forecasting the P&L impacts of FX are clear. Early identification of expected FX impacts in the FP&A processes provides both time and opportunity to respond to risks sooner. Improved insights into the causes of FX impacts offer direction on where issues should be addressed. The outcome should be enhanced predictability of the overall financial results.

In addition to increased Performance, there are additional benefits in clearer accountability for the results. In the three questions above, the first two address timely forecasting accuracy, while the third pertains to the Treasury team's ability to achieve the rates set by the organization. With transparent accountability for the FX impact, Treasury gains an additional tool to steer the organization toward improved budgeting processes and create KPIs to ensure effective strategy implementation. This provides a valuable addition to the commonly used forecast vs. forecast exposure analysis, as the FX impacts resulting from that performance can be easily identified.

Conclusion

Although FP&A processes are crucial for clear strategic decision-making around business operations and financial planning, the FX impact—potentially a significant driver of financial results—is not commonly monitored with the same extent and detail as business operations metrics.

Improving the FX analytics of these processes can largely bridge the information gap between business performance and financial performance. This also allows Treasury to be utilized as a more engaged business partner to the rest of the operations in the prediction and explanation of FX impact, while providing strategic direction on how these impacts can be minimized, both globally and at local operations levels.

Implementing such an end-to-end process may be intimidating, but data and technology improvements embraced in the context of finance transformation projects may open the door to exploring these ideas. With cooperation between Treasury and the business, a true end-to-end FX risk management process may be within reach.