For the portion of scenarios where real data for interest rates doesn't exist, the simulation creates random rates: this make sense, it shouldn't be totally predictable. However, If you look at how real interest rates change versus the random ones, you'll notice that real-world interest rates don't exhibit wild changes from month to month. Some times the interest rate will vary over a percentage point between months, and this makes planning expansions, especially for smaller airlines, much more like playing at a casino than running business.
I propose the algorithm for creating the change in interest rate for each month still be random, but be weighted by the month-to-moth change in previous months, with the most recent changes having the heaviest weight viz:
[(feb-jan)*1+(mar-feb)*2+(apr-mar)*3+(may-apr)*4+(random change value)*5]/15 = change in interest rate for June
Essentially look at the delta in monthly rates for the past 4 changes, along with a random change (within a band determined reasonable by the administrators, say +/-100 basis points), and use a weighted average of all of them for the actual change in the interest rate for a given month.
This way the interest rate would not be completely predictable yet nonetheless it would not exhibit the enormous volatility of the current system.
A formula like this could also be used for gas prices, but with different weighting or a wider domain for the random number in order to increase price volatility.