It’s 2015. Watson vanquished humans in Jeopardy 4 years ago and is now rapidly moving towards replacing as many oncologists as possible. Google is just one company running driverless cars and trucks around everywhere. Facebook is trying to monetize every eye twitch you make looking at a web page. Let’s check in on innovation in public finance:
- Rarely if ever calculate and manage relevant risk metrics. Check
- Analyses of all stripes performed in spreadsheets or 20+ year old bond software - despite massive limitations. Check
- Unexamined rules of thumb for when to refund bonds. Check
- Applying 25+ year old, black-box option models that are simply inappropriate for munis and few understand. Check
The last one might be considered an “innovation” given its somewhat more recent rise but with innovation like that…well, it seems like we could do better.
How about we start with improving the good ol’ yield calculation for issuers?!? We’ve used recent fixed income research, spawned from the obvious shortcomings of models during the financial crisis, to create a better yield mousetrap. We can now incorporate an issuer’s actual refunding criteria in cash flow calculations to create a lifetime cost of financing including 1st and 2nd generation refundings. For that reason, this is called the Refunding Adjusted Yield (RAY).
How’d we do it? Well, it’s just 4 simple steps:
- Implement a real world market model that realistically generates the issuer’s tax-exempt, taxable, and SLGS (escrow) curves in a single consistent model. Ideally make it fully transparent and testable. Examples are here and here. (We chose the latter as it has the benefit of capturing how yields actually change across levels.) You can test it yourself by playing Curve Quiz (free!) on iphone/ipad here or on your Android device here.
- Using issuer’s actual refunding criteria, determine when hypothetical refundings occur (make nice refunding probability graph).
- Given the timing of refundings in 2) and the future yield curves in 1) adjust the original debt service cash flows based on the new refundings. Do the same for 2nd generation refundings as applicable. Don't forget to enforce the rules: no tax-exempt refunding of advance-refunding bonds, just like the real world.
- Average the net cash flows from 3) and calculate a yield back to the purchase price of the bond or issue. This is the Refunding Adjusted Yield (a RAY of shining light on true lifetime project cost and muni bond structuring!)
What does RAY look like for an actual deal structure? For a 20 year level debt issue (amort and pricing at bottom of article) we have these statistics:
Issue Par $100,000,000.00
Issue Price $113,496,745.45
Arbitrage Yield 2.910%
True Interest Cost (TIC) 3.382%
RAY with 5% PV Savings Criterion 3.207%
For those not fully initiated into the mysteries of public finance yield calculations, the arbitrage yield is generally a yield to worst (from the investor’s perspective) calculation and as such is often to the call date for premium callables. The TIC on the other hand is to maturity. Armed with this information, it is intuitive that the RAY would sit somewhere between the arbitrage yield and TIC. In this case, obviously, the closer RAY is to the arbitrage yield the more likely the bonds are to be refunded and called. In this case the RAY calculation incorporated a refunding if the callable bonds hit a 5% PV savings target. Different refunding criteria lead to different RAYs. More on that to come...
This framework has a ton of really nice side benefits too, which we’ll look at over the next few weeks:
- How RAY Changes with Refunding Criteria/Policy
- The effect on RAY of 2nd Generation Refundings
- New stats like Avg Time to Refunding, Refunding Adjusted Avg Life, and % of Escrow Supported Cash flows
The information content in these numbers is staggeringly greater than simple arb yield or TIC. Ding dong...the TIC is dead
If you’d like to know more or would like to calculate a RAY for a new pricing or to compare structures, drop us a line - firstname.lastname@example.org or call 646.202.9446.