Many companies sell goods and services abroad or procure raw materials from there. Such companies are used to having cash flows in foreign currencies that create uncertainties due to exchange rate fluctuations – especially with regard to planning data.
Crises such as the war in Ukraine and, for example, the Turkish lira crisis, can significantly affect global companies without active foreign currency management and severely reduce their sales margins. For this reason, Treasury hedging against foreign currency risk is absolutely crucial for global companies and their survival. In doing so, it is important to mitigate the possible negative effects of unfavorable exchange rate developments as much as possible without losing out on potential opportunities or even incurring needless transaction costs due to over-hedging.
Questions that arise in this context are: "How can the risk in future cash flows associated with volatility in exchange rates be measured? How can it be mitigated (more effectively)?
How can the risk in future cash flows associated with uncertainties in exchange rate developments be measured?
One possible answer to this question is the cash flow-at-risk approach. Cash flow-at-risk provides a statistical assessment of the current level of foreign currency risk. It differs from value-at-risk in the reference value. While in the case of value-at-risk the value is the company's or security's value, for cash flow-at-risk it is the cash flow. Given that the cash flow is considered over the entire forecast period, aggregating monthly cash flow data into an overall cash flow is a considerable challenge. In order to estimate the cash flow-at-risk, it is necessary to simulate possible future cash flow realizations from which the cash flow distribution is derived. On this basis, the distribution of the deviations of all cash flow realizations from the expected total cash flow can be calculated. At a confidence level of 95%, the cash flow-at-risk can now be expressed as the 5% quantile of this. The value of a potential cash flow realization consists of two components: On the one hand, (monthly) cash flow planning data in a foreign currency, on the other hand, exchange rate data for the specific forecast period. The reliability of the planning data is particularly important for a reliable cash flow-at-risk estimate. It is a well-known fact that exchange rate data for future periods are uncertain and require an adequate forecasting procedure.
Besides the Monte Carlo simulation and the parametric variance-covariance approach, historical simulation is also possible, which is based on historical data and therefore does not require a distribution assumption. For this procedure, the required input data are only the current exchange rates and historical exchange rate yields, which are used to simulate different exchange rate scenarios and to calculate the possible cash flow realizations. For simulating exchange rates, a weighted random draw is made from historical exchange rate returns applied to the current exchange rate. From the cash flow-at-risk, it can be deduced, at a confidence level of 95%, the (negative) deviation from the expected cash flow within the forecast period is not expected to exceed EUR x with a probability of 95%.
How can an optimized hedging strategy be identified?
Within a company, there are often conflicting interests. While from a profit center point of view the reduction of the future expected cash flow is unwelcome, it mostly follows directly from risk mitigation and is the determining factor for the risk management. Trying to optimize the hedging strategy is therefore always a trade-off between risk mitigation and cash flow maximization. While minimizing the cash flow-at-risk as the parameter for the exchange rate risk, the aim is to simultaneously maximize the future expected cash flow. Therefore, an efficient use of hedging transactions is crucial.
For a dynamic optimization we use an adaptive mathematical optimization algorithm. Its approach is based on generating a large number of possible hedging solutions and continuously improving them, by targeting promising regions of the solution space and eliminating unfavorable solutions. The steering and eliminating of solutions are thereby controlled mainly by two criteria: the dominance and the distance criterion. Both criteria are measured against the objective function values (cash flow-at-risk and future expected cash flow) of the solutions.
According to the dominance criterion, a solution is non-dominated if no other solution exists that provides both a lower cash flow-at-risk and a higher expected cash flow. Such a non-dominated solution is desirable for our optimization algorithm and is continued in the algorithm process.
The distance criterion builds on a metric for determining the distance between solutions in the solution space. The objective here is to achieve the widest possible spread of solutions with respect to the objective function values and to eliminate densely spaced solutions so as to enable a global search in the solution space. The result of such optimization is a multitude of hedging solutions for future periods of varying risk levels, one of which can be selected depending on the risk appetite and by taking into account the different interests.
How is this technically implemented?
Transferring the model of the historical simulation and the optimization algorithm into an IT tool is done via programming languages such as for example R or Python. After importing the required input data, such as historical exchange rates, forward rates for possible forward exchange transactions or planning data on future cash flows in foreign currency, there are two stages. In the first stage, the historical simulation of the exchange rate scenarios is executed, based on which the individual hedging solutions are evaluated in terms of risk and expected cash flow. In the next stage, the hedging strategy is optimized through selective generating and optimizing of new hedging solutions. Business intelligence tools, such as for instance SAP Analytics Cloud (SAC) or Microsoft Power BI, can be used as a front end to analyze and visualize the hedging strategies. The user has the possibility to import future cash flow plan data and a certain hedging strategy and to analyze them with the help of our pre-implemented visualizations and reports.
How can the cash flow-at-risk model be validated?
After the technical implementation of the model, validation by means of various tests is at least as important. This includes, among other things, the backtesting of the tool, in which the recommended hedging strategies are applied to historical data in order to allow an ex-post assessment of the results achieved. Hereby, the cash flows actually realized with the recommended hedging strategies are compared to the cash flows actually realized without any hedging transactions. Questions to be addressed are: "How do the exchange rates we forecast behave in relation to those that actually occurred? Does the strategy comply with the forecast cash flow-at-risk value? Does the recommended hedging strategy actually contribute to more security and predictability with regard to future cash flows in foreign currencies?"
Ill-considered over-hedging is just as big a problem in the context of foreign currency management as ignoring foreign currency risks altogether and foregoing any hedging transactions. Therefore, a well-considered and dynamic hedging strategy is required to balance the trade-off between risk mitigation and cash flow maximization, considering possible transaction costs of the hedging transactions. Therefore, what the cash flow-at-risk tool offers is a stable forecast of expected future exchange rate developments and based on this, an optimized, dynamic hedging strategy. The primary focus here is always on increasing planning certainty for future cash flows without incurring superfluous transaction costs through over-hedging.
Source: KPMG Corporate Treasury News, Edition 124, August 2022
Börries Többens, Partner, Finance and Treasury Management, Corporate Treasury Advisory, KPMG AG
Julian Fisahn, Manager, Finance and Treasury Management, Corporate Treasury Advisory, KPMG AG
Partner, Financial Services, Finance and Treasury Management
KPMG AG Wirtschaftsprüfungsgesellschaft