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93 Years of Top Tax Rates: What it means for Public Finance



The income tax has made more liars out of the American people than golf has.   - Will Rogers

The wages of sin are death, but by the time taxes are taken out, it's just sort of a tired feeling. - Paula Poundstone


To put it mildly, tax talk is hot these days and of course with it comes uncertainty as to the value of the income exemption on municipal bonds. The administration has floated the so-called "Buffet Rule" which would enforce a minimum effective tax rate for wealthy individuals along with the draft Debt Reduction Act of 2011 that would involve automatic spending cuts, including ones on tax preferences on interest. Needless to say, many muni market participants are not fans of the additional uncertainty of the value of tax-exemption.

But really how new is this?  The chart below shows historical top Federal marginal tax rates from the inception of the income tax in 1913 to today.

Top Tax Rates 1913-2011

Source: National Taxpayer's Union

A few interesting things to note.  First the highest federal marginal tax rate was often well north of 50% from 1932 through 1986. For most of that period this rate kicked in for those earning more than $200k to $400k, though during the late 30s and early 40s it was reserveded for those Rockefellers of the day taking down more than $5 million. Next, note the creation of tax-exempt VRDBs coincided fairly closely with a big drop in the top Federal Marginal Tax Rate (FMTR) with the Tax Reform Act of 1986.  

How has this changed the value of tax-exempt bonds? This is at best difficult to say as the municipal market has changed so dramatically (along with other financial markets) during the period after the Tax Reform Act of 1986.  Since then, the top Federal Marginal Tax Rate (FMTR) in relative terms simply hasn't changed much.

Once you remove the effect of rate levels themselves we find that the very approximate relationship between tax rates and tax-exempt / taxable ratios in the variable rate market are

Tax-Exempt / Taxable Ratio = (1 - TopFMTR) + 2%  

Depending on the data set you use and how you control for rates you can get that 2% to move around quite a bit but we find this a reasonable rule of thumb when rates are in a normal range. Of course they haven't been anywhere near normal for many years since Fed Funds has been effectively zero.  

Today the true tax risk could go in either direction. The threat of higher marginal tax rates on high earners would bode well for the value of tax-exemption, but proposals pushing a lower FMTR with fewer loopholes would likely hurt.

Tax risk is certainly real, now how much SIFMA/LIBOR swap do you need to hedge it?   

Good News for Muni Issuers: Rate and Ratio Correlations Still Work!



"Those who trust to chance must abide by the results of chance."

            Calvin Coolidge

One of the early lessons of modern finance that I was taught, like many others, in portfolio management class is that correlation is risk in a large diversified portfolio. This lesson for many has morphed into myth in light of the financial crisis. Based upon fairly low correlation calculations in normal market environments, investors thought they held diversified positions in say domestic equities, real estate, or even credit default swaps.  However in mathematical terms correlations are time inhomogeneous – that is, they aren’t stable over time.  And worse, recent experience showed us that for many of the aforementioned markets in times of stress, when you need diversification most, correlations move to 1; now your subtly engineered, diversified portfolio is one big boulder heading south. 

What does this have to do with municipal issuers? Municipal and
tax-exempt borrowers often focus their energy predominantly on cash flow and cost of capital when looking at debt management. That’s not to say mark to market changes are ignored, particularly in the case of liability based hedges which can lead to collateral calls and liquidity pressure, but this is usually secondary to understanding the impact on debt service and budgets.

LIBOR and ratio scatter

From a cash flow perspective, the two primary types of cash flow risk factors in which munis dabble are interest rate (say LIBOR) and tax-exempt/taxable ratio (SIFMA/LIBOR) risks. Looking at these two factors, through up and down market cycles including the crisis, we find that the correlation is negative and has stayed negative. Ratio risk performs poorly when rates are low but very well when rates are higher.  Market microstructure reasons abound to explain the phenomenon which should give financial managers comfort.  

Interesting thing about tax-exempt variable rate demand bonds (VRDBs) is they contain both risks within the structure (no, LIBOR swaps do not add tax risk – it exists in the VRDBs whether hedged or not!).  With many issuers struggling against tight budgets and looking for ways to lower debt service, at least in the near term, how do you determine the optimal amount of each factor? That depends upon your expected view of rates, tax-exempt/taxable ratios, the volatility of each, and the correlation between the two. A good tax-exempt risk structuring tool can get you there.  

5 Risks to Capture Using Monte Carlo to Analyze Tax-Exempt VRDBs


"Those who trust to chance must abide by the results of chance."     - Calvin Coolidge

"The problem is not their estimates, it's the range of potential error in those estimates." - Alan Greenspan

Despite all behavioral finance has revealed about over-confidence and specifically, the overuse of historic averages in finance, one pervasive legacy of the ubiquitous spreadsheet is a stubborn reliance on static numeric assumptions for market risk factors - “Let’s just use 3% for SIFMA to run that debt service schedule and do the analysis.” It’s such a common modus operandi that it happens often without a second thought. The reality is that keeping variability in the analysis of market variables (where it belongs) is more conceptually challenging, but infinitely better at providing meaningful information to issuers; the right tools can offer critical intuition.

This post describes four risks a skilled modeler with well-designed Monte Carlo tools can explore when looking at tax-exempt variable rate debt (VRDBs).

1. Interest rates

Perhaps less so today, but the primary risk factor people evaluate
in VRDBs is what might happen to the economy and its impact on the demand for short-term money. Of course, what happens atSimulated rates the front of the yield curve is almost entirely driven by those dwellers of the Temple at the Fed. When evaluating tax-exempt VRDBs it makes the most modeling sense (for reasons explained in far more detail in this paper) to first capture some primary benchmark of overall interest rates.  Usually, we start with LIBOR. 

Note that for each period in the analysis, we’re creating an entire distribution of rates from the simulations as shown in the left LIBOR graph above. We use a straightforward but powerful interest rate model described in detail here.


2.  Tax-exempt / Taxable Ratio (SIFMA / LIBOR)

If US tax law changes in a way that diminishes the benefit of tax-exempt income to investors, issuers will immediately face higher costs of borrowing. For this reason, the risk that tax-exempt yields trade closer to taxable ones is actually a component of the variable rate bonds themselves.  NOTA BENE: this has nothing to do with whether or not there’s a swap hedging the interest rate risk in the bonds (the press has a hard time with this one).    

The essential feature to capture of this basis risk is its inverse relationship with rates. When rates are high, ratios are low and vice versa. This has been a persistent feature of these markets through boom and bust and ignoring it, frankly can lead to expensive mistakes detailed here.

This means the Monte Carlo model needs to have a correlated, multi-factor component. Many are familiar with the most famous distribution (Gaussian or Normal) in one dimension: in this case we need 2 per the image below.  

2 factor distribution

3. Credit support costs

Liquidity support behind VRDBs is no longer a slam dunk. It’s now a much more significant component of cost. In fact, with SIFMA where it is currently, support costs today are generally greater than the interest rate itself!  That said these costs are fixed only through the next renewal date.  What assumption does an issuer make after that?  

A well-designed Monte Carlo simulator allows the user to model the potential changes in credit support costs and add them to the interest rate and basis risks described above.  

4. Trading spreads

Much of the joy in public finance lies in the 50,000+ issuer community and its attendant variety. VRDBs are issued by different credits in different states with different tax regimes backed by different banks.  As much as SIFMA offers the benchmark against which most variable rate programs are compared, there will undoubtedly be noise around that benchmark, and sometimes that noise is worth trying to understand.

A flexible Monte Carlo rig should provide the user with the ability to model trading spreads, both in expected level and volatility.

5. Converting the VRDBs to Fixed

One plausible “worst case” (one of the three key questions every CFO must ask) might occur if there’s no l the providers with reasonable prices and terms go hiding. In that case, the multi-modal features of the bonds may kick in and the issuer faces a fixed rate remarketing.  But what might the market look like then?   

With a powerful, generalized simulation framework, the analyst can simulate a long term rate factor, either as part of a complete yield curve simulation or as a separate factor correlated to short term rates.  The payments on the bonds then “flip” from the floating index to a (likely higher) fixed rate at the expiration of the letter/line of credit. It’s important to note that the fixed rates are simulated as well, so it’s not just a single fixed rate assumption on the remarketing date, but an entire range of possibilities. This is consistent with the uncertainty associated with that future unknown rate environment. 

There you have it. If you need help setting up a model to capture these risks, let us know. 

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*Various products, solutions, methodologies, processes and techniques presented and/or described on this website are proprietary to Intuitive Analytics LLC, and are multiple patents pending.