Deaglo shows hedging costs have zero effect on internal rate of returns (IRR) yet reduces volatility
Deaglo helped the client visualize, understand and gain insight into the comparative performance of both the unhedged and hedged share class IRR whilst reducing volatility.
Client type and solutions
Deaglo worked with a Brazilian fund's investors who had requested a hedged share class using IRR simulator and products including Monte Carlo Strategy, fixed spot rates and forward curves.
The benefits of our analysis
Through an IRR hedging analysis running high, low and mid volatility skews Deaglo showed that the IRR (Internal Rate of Return) of the hedged share class would not be impacted.
The Client was able to raise 300% more than anticipated from foreign investors.
Utilizing positive forward points for round trip hedging, meant the cost was vastly reduced
100% of foreign investors invested in hedged share class.
A Brazilian fund's investors requested a hedged share class. There was concern that the USDBRL forward points would negatively impact the IRR of a hedged share class. While it was straightforward to evaluate the IRR using fixed spot rates and forward curves, the five-year investment horizon meant that spot rates could exhibit volatile regimes of appreciation and/or depreciation; additionally, Central Banks frequently modify interest rate curves to manage inflation and other mandates. This has the effect of changing the forward curve between currency pairs and affects hedging costs. While the previous year's (2020) rates were abnormally low due to the COVID pandemic, it was anticipated that rates would return to their normal levels in Q1/2 2021. Understanding the impact to the IRR from these effects could not be determined with a closed-form analysis, so Deaglo employed a Monte Carlo approach to modeling the market, and evaluation of multiple forward rates curve regimes to develop a coded solution to properly evaluate IRR impacts.
The main objective was to analyse and quantify the hedging impact on IRR for the client. So how did we achieve this?
The first task was to generate a simulation across many thousands of potential spot rate paths.
The deployment and harvest tranches were then valued, by amount and date, into and out of BRL according to the simulated spot histories. This generated the data required to model the unhedged share class IRR.
Finally, each of the three forward scenarios was used to calculate the required data for the hedged share class. A layered hedge strategy was selected with quarterly layers over a two-year horizon, giving eight layers, each with its forward rate determined by the simulated spot path and the forward scenario being evaluated. The amount of BRL deployed by the hedged share class and recovered into USD during harvest was dependent on the effective rate generated by the layering.
See figs. 1, 2 & 3 below for visual results from the procedures employed by Deaglo.
Fig. 1 shows the first 50 of those paths, illustrating the variety of outcomes evaluated.
Fig. 2 shows the split violin plot used to display and compare the hedged vs. unhedged share class IRR distributions over all simulated paths.
Fig. 3 shows the comparison between different forward point scenarios:
Scenario 1 - Current forward rates
The result - Unhedged slightly outperforms hedged
Scenario 2 - Starts off with a positive and then negatively- sloped forward
The result - Hedged slightly outperforms unhedged
Scenario 3 - The longest tenors much higher than a linear trend
The result - Both hedged and unhedged approximately equal
BUT the hedged share class has reduced volatility in all scenarios. Further parametric studies across varying volatility and skew were equally enlightening. This sort of analysis and modeling is simply impossible without the use of programming.
By creating a bespoke risk model, using Monte Carlo techniques to model the range of outcomes, Deaglo could help the client visualize, understand and gain insight into the comparative performance of both the unhedged and hedged share class IRR whilst reducing volatility.