Posted by Peter Orr on Wed, Jul 28, 2010 @ 06:59 AM
Here’s a quick quiz. If over the last 10 years 1M LIBOR reset weekly averaged 2.814%, and the average of SIFMA / 1M LIBOR was 82.0%, what was the SIFMA average over the same time period (all rates unadjusted for day counts, holidays etc.)?
A. 2.05% B. 2.31% (2.814% * 82.0%) C. 2.62%, or D. None of the above but it seems like a trick’s in here somewhere
The correct answer is in fact A, which is a testament to how strongly the Fed has been stepping on the money accelerator over the last decade. Monetary policy aside, if you answered B (simply multiplying the LIBOR average by the SIFMA/LIBOR ratio average) you would’ve made a very common mistake which falls into the category of the Flaw of Averages. Overreliance on simple averages, partly induced by overreliance on simple spreadsheets, can very easily lead to errors of calculation and ultimately judgment. In this case, the seemingly more intuitive answer B is over 25 basis points wrong!
How does this work? When rates are low, SIFMA/LIBOR has been high and vice versa i.e. the two rates have been negatively correlated. If you don’t capture this fact in your analysis, you’re missing a critical component of how the tax-exempt markets have worked. This ultimately leads to over-hedging, misunderstanding of balance sheet hedging effects, and other regrettable side effects.
Luckily, there are readily accessible public finance analytics that capture these effects very easily.
Posted by Peter Orr on Tue, Jul 20, 2010 @ 07:56 AM
Credit markets are certainly not “normal” (in any sense of the
word) but at least they’re stable enough for issuers to make some decisions. That said, keeping in mind the answers to three deceptively simple yet vitally important questions will always serve CFOs, governing boards, finance committees, and other financial decision makers very well.
Notice that these questions are not framed in terms of some specific risk metric or probability. That’s because they are intended to address decision making in a way that we (as a species) are best suited to understanding. Despite the fact that banking regulation has often focused on extremely remote events like 99.9% annualized confidence intervals i.e. events that happen every 1,000 years, it’s a well researched fact that we human types simply don’t do very well making decisions about such tiny likelihoods. We tend to overemphasize the dramatic remote risks (shark attacks and plane crashes) over the far more dangerous yet mundane occurrences (auto accidents and drowning).
Can we make it through the worst plausible scenario?
The nature of risk management comes first in defining a plausible event or set of events to be concerned about. Without some sense for what that the downside concern is and how it will impact a corporation’s financial position, risk management doesn’t exist. Notice that this is where the entity’s level of risk aversion comes explicitly to the surface.
“Make it through” will mean different things to different entities since incentives and consequences, including political fallout, are obviously not uniform across institutions. For many, this concept is tied to liquidity access – a topic that’s found a great deal of interest over the last 18 months.
“Plausible” is also an important word here. An issuer I know, when answering this question for themselves, looked at the marks on their swaps if the entire yield curve moved to 0%. This is obviously a definable event and it gives one boundary value for their swaps; some people may consider it so implausible however that it should not be the focus in response to this question.
How much might we gain in the best plausible scenario?
This is an important question in that if there’s very little gain expected relative to the “do nothing” scenario, absorbing the risk may not be worth it. This question wraps in it whether you want to evaluate the best scenario in terms of the individual transaction in isolation, or evaluate the overall impact against the backdrop of the entire portfolio (debt and/or investment).
The answer to this question in conjunction with the first helps determine the nature of the strategy’s distribution. A remote but large downside with a modest but likely upside is similar to a “sold option” situation. A fairly uniform upside and downside is a simple long position in some risk, etc.
What is the breakeven?
How far do the factors that affect the performance of the instrument(s) need to move in order for the strategy to break even with the “do nothing scenario”? For instance, do you want exposure to SIFMA based variable rates as a tax-exempt borrower if you believe significant inflation will arrive eventually and you can lock in a rate at 3.75% fixed? How fast to floating rates need to rise for this strategy to break even (download model here)?
Understanding the break even helps us evaluate the likelihood that the transaction will work in your favor in a way that no other calculation really does. It allows us to directly assess a tangible, quantified event and the subjective probability that that event will occur. With that information in hand, evaluation of the best course of action is often much more clear.
For analytics that help answer each of these questions using rigorous, comprehensive decision frameworks see
here.
Posted by Peter Orr on Fri, Jul 16, 2010 @ 12:29 PM
I’ve always believed there are actually three certainties in life (in contrast to the far less archetypal two): death, taxes, and finance people’ love of spreadsheets. Spreadsheets are excellent for doing certain types of work given their flexibility. Though frankly, these “electronic chalkboards” as their inventors called them are simply not the right medium for others. For instance, heavy duty simulation based number crunching and optimization shouldn’t be done on a chalkboard, electronic or otherwise. The memory management and numerics simply aren’t suitably industrial strength for big jobs like that. As a data store the spreadsheet also has drawbacks. Sure it’s flexible and easy to add new bits, but that same flexibility is a problem when it comes to compatibility and consistency, virtues in and of themselves.
Here’s a top 10 list explaining how a database (sometimes) simply eats a spreadsheet’s lunch:
10) Database is a single, accessible location for complete, accurate information
9) Database offers on-demand distribution of data to professionals in all regional offices in your business
8) Impress your friends by confidently exclaiming, “We’ve implemented a best-practice abstraction of our data from our data format”
7) Easy to connect to a database from a spreadsheet to grab what you need
6) Much harder for an employee to email themselves their fancy, custom database before quitting and going to a competitor
5) Analytic tools can be built to apply to entire database, increasing accuracy and boosting productivity – BIG time
4) Put your database in the cloud and join the millions of people who talk about cloud computing but only have a very foggy idea of what it is!
3) With a database you have a specific manager providing crystal clear responsibility for data integrity, completeness, and security
2) Sure Excel now has 16,000 columns and a million rows (on Sheet1), but just try and use them all! For gobs and gobs of data, a database is the solution, hands down.
And the number 1 reason a database kicks a spreadsheet’s butt is
1) Capturing data in a spreadsheet to run some #s is soooo 90s. Nowadays you’ve got to apply your fancy numerical recipes to an entire database and auto-email results to your pre-defined audiences – 100x more efficient and infinitely cooler!
Posted by Peter Orr on Wed, Jul 14, 2010 @ 08:50 AM
I know evolutionary biology comparisons to business are a bit
tired at this point, but having just checked off an item on my personal bucket list (diving in Galapagos!) I can’t resist reciting a few quotes from my friend Chuck Darwin. After seeing some of his famous finches firsthand one day and dealing with a balance sheet market risk analysis the next, I can’t help myself:
“It is not the strongest of the species that survives, nor the most intelligent that survives. It is the one most adaptable to change.”
“In the long history of humankind (and animal kind, too) those who learned to collaborate and improvise most effectively have prevailed”
“A man who dares to waste one hour of time has not discovered the value of life.”
- Charles Darwin
Have a full and evolving day!
Posted by Peter Orr on Mon, Jun 07, 2010 @ 11:41 AM
"Programming today is a race between software engineers stirring to build bigger and better idiot-proof programs, and the universe trying to produce bigger and better idiots. So far, the universe is winning." - Unknown
Many public finance businesses are grappling with the "build vs buy" question as it relates to their analytic tools. I've commented here on the challenges of building good
financial software and frankly, most firms that aren't specifically in software development are poorly equipped to do so. And this is a tangentially related and important question.
Of course, build vs buy is not a new question generally but it may be new to some in financial services. The most succinct variant of the common wisdom on this is in this infoworld article . Here's the bottom line from the article:
"Decades of trial, error, and egghead analysis have yielded a consensus conclusion: Buy when you need to automate commodity business processes; build when you're dealing with the core processes that differentiate your company."
There's an interesting dynamic about the technology "backbone" behind a public finance business (providing accurate/current debt profiling, refund screens, historic prices and reset histories from EMMA, bond and option valuation functionality, etc). Although these may be commodity business processes that every banking/advisory/investment firm must do in one form or another, that data backbone serves as the foundation for proprietary analytic tools and reports that can differentiate one from the rest.
At IA, we do both automation and innovation so that the workflow public finance analytics need to support are fully addressed. This allows us to be far more efficient and effective in designing each.
Other good "build vs buy" articles here:
techrepublic
MIT Free Software: Build or Buy Dilemma
Posted by Peter Orr on Mon, May 31, 2010 @ 10:07 AM
In the 2nd post on the topic, Dan Cooreman of the Sunday Business section indicated that the problems Gretchen Morgenson spoke to in her article,The Swaps That Swallowed Your Town, were in fact related to the variable rate instruments. He seemed to agree with exactly my point but ultimately, my final words (below) to the Times editors on the topic have gone unanswered.
___________________________
Thank you for the response, Mr. Cooreman, and the quote from the NYS Division of Budget. You have hit on, and seemingly agreed with, exactly my point - if what Mrs. Morgenson was talking about was how "the collapse of the auction rate and bond insurance market, in conjunction with rising credit concerns for a number of liquidity providers (commercial banks) caused the interest rates on certain variable rate bonds to increase to unprecedented levels" why wasn't the headline, "Auction Rate Securities and Bad Variable Rate Bonds Gobble a Gotham Near You"? Did someone bury the lead? If it's the auctions and variable rate securities that are swallowing your nearest town, where is the "ensnared in the derivatives mess" coming from in the article? Why is it all about how interest rate swaps have caused a problem??? The fact is, in the vast majority of cases, they didn't.
The critical background Ms. Morgenson seems to misunderstand is that interest rateswaps are only really useful for hedging interest rate risk. They are not designed to hedge the fragility of the auction rate market, the melting down of MBIA, Dexia's balance sheet exposure, or "credit concerns for a number of liquidity providers." Blaming the swaps for these problems (the headline is about swaps after all) is akin to having a fearsome headache after a late night and blaming the hat you're wearing in the morning for the pain. Here's the equivalent headline, "Knit Cap Causes Enormous Hangover." The cap is there to keep your head warm, not fix your headache, and it certainly didn't create your hangover in the first place.
The reporter has conflated every possible economic and financial event that could impact an issuer's specific auction or variable bond rate, and then blamed interest rate swaps for not hedging them all. This is again, dead wrong. These interest rate swaps were never designed to remove all the risks inherent in an auction or variable rate borrowing program, and to imply otherwise is again, fundamentally incorrect. And anyone in public finance who's worked on these structures knows it.
The story in its entirety is misleading at best but this sentence in particular, "The contracts, however, assumed that economic and financial circumstances would be relatively stable and that interest rates used in the deals would stay in a narrow range." is patently untrue, and there's now way to avoid it. New York Times readers deserve a clarification. And the talented public finance officials who structured these transactions on behalf of taxpayers, prudently and with the best information available at the time, are owed even more. If the New York Times is going to Monday morning quarterback the credit crisis, at least get the facts right.
On this Memorial Day, my dad the Navy officer and Korean War vet may not have fully approved of my sneaking in some work-related activity. That said I'm pretty sure my
dad the writer and English Lit major wouldn't get too bent. Thinking of you...
Posted by Peter Orr on Tue, May 18, 2010 @ 02:01 PM
This is the second round of commentary (of three) with the NYT editors justifying their mistake in the article,
The Swaps That Swallowed Your Town. My response was simple, though I was forced to use a three number example.
__________________________________________
Dear Editor(s),
I'll try and keep this brief.
Given Ms. Morgenson's response, it is clear she is sorely confused about what has caused financial strain for states/municipalities. I'll prove this simply, although I will use a few numbers - 3 to be exact. Let's take the last year to represent our time period of market stress (though you could pick any reasonable representative period, the answer will be substantively identical). The first number is 0.20%, which is the average of 67% of 1M LIBOR over the last year. I use 67% because it is the most common rate municipalities have used in swaps to hedge variable rate bonds. The second number is 0.34%. This is the average of the SIFMA index, the index against which all tax-exempt variable rate bonds are priced. The difference between these two numbers is the spread that Ms. Morgenson shockingly claims is outside the "narrow range." It is this crushing differential that she, and worse, the NYTimes has told its entire readership is going to imminently "swallow" all the swap-exposed towns near you in a madly corrupt, swap induced, financial maelstrom. And how big is this staggering, non-narrow differential??? 0.14%. Please look carefully at where the decimal point is on that number; it is no mistake.
This would be funny if it weren't exposing such flagrant misinformation and flat out bad reporting. And if 0.14% isn't in the "narrow range," I would ask Ms. Morgenson what is, exactly? As a point of reference, the 2 year average differential is 0.32% so this wild differential has only gotten narrower over the last year. The simple undeniable fact as it relates to this "narrow range" issue is that the current period actually shows one of the narrowest spreads we've seen historically, because the actual level of interest rates is so close to zero. Again, this is simple, unalterable, basic fact that anyone can check. I urge you to run this by your "municipal experts" in the story, or anyone else who knows something about public finance - I assure you they will agree with me. Your readers deserve better and this egregious mistake should be corrected.
Again, the actual reasons states and municipalities are under stress from their debt programs are exactly those that I described in my first letter: failed auction rate securities and variable rate bond programs which have lost the support of the banks. Whether those programs were hedged with interest rate swaps is an entirely separate issue, though admittedly can cause additional stress if the state/municipality chooses to terminate the swap.
I'm sure Ms. Morgenson is a good writer; as an NYT reader I have enjoyed some of her articles in the past. Unfortunately in this case, she is in over her head, knows only enough about the subject matter to be factually wrong, and has embarrassed the New York Times. I believe an editor's job is in part to acknowledge and correct when the paper doesn't have its facts straight. The premise of this entire article is clearly mistaken (I hope in good faith and not just to sell papers), and your readers deserve to know it. There's enough falsehood in our public discourse without news organizations throwing their own rubbish onto the heap.
Happy to discuss this or the real challenges municipalities face with whomever cares about accurate reporting. At minimum I look forward to a correction of this error.
I was a bit edgy that day so the tone wasn't quite as constructive as I would've liked. Nonetheless, here was the response from Dan Cooreman of the NYT Sunday biz section:
Mr. Orr:
Thank you for the additional information in your email message of last evening. But it seems that you and Gretchen Morgenson are discussing two different things.
Here is a summary of her explanation:
The spread that was referred to in the column did not refer to the difference between 67% of LIBOR and the average of the SIFMA index. When the column said the contracts assumed that the rates in the deals would stay in a narrow range, it was referring to the problems associated with spikes in interest rates on variable rate debt. When the spread between this rate and that received by the issuer from the swap counterparty blew out, it created significant problems for tax-exempt debt issuers. A crucial reason for this, as you and the column both pointed out, was the seizing up of the auction rate securities market.
As outlined in the Annual Performance Report from the New York State Division of the Budget: "In 2008-09, the crisis in the credit markets negatively affected the performance of the swap portfolio. The global credit crisis has highlighted that the use of these financial instruments can expose municipal debt issuers to large unanticipated costs. In particular, the increased costs associated with credit risk, basis risk and early termination payment risk have had a significant impact on the performance of synthetic fixed rate swaps. During the past year, the collapse of the auction rate and bond insurance market, in conjunction with rising credit concerns for a number of liquidity providers (commercial banks) caused the interest rates on certain variable rate bonds to increase to unprecedented levels. For example, interest rates on auction rate bonds in the Tobacco bond program rose to 14.2 percent from 4 percent over a one month period. The dislocation in the credit markets negatively affected more than half of the state's variable rate portfolio ($5.2 billion)."
This is the aspect of the deals that the column was referring to, not the difference between 67% of LIBOR and the SIFMA index.
Dan Cooreman
Sunday Business section
Oh! So the article was referring "to the problems associated with spikes in interest rates on variable rate debt"?!?!? Wait, what was the title of her article again? Somehow I don't remember seeing anything about interest rates on variable rate debt. Stay tuned for the third and final chapter.
Posted by Peter Orr on Sat, May 08, 2010 @ 09:46 AM
One of the (few) benefits of leaving a perfectly functional Street job is that I'm now free to have a free dialogue with the free press using my now free(er) speech. To that end, I decided to engage the NYT a few weeks back regarding Gretchen Morgenson'sThe Swaps That Swallowed Your Town article. This is the first of 3 posts recounting the transcript as surprisingly, they did engage.
Most of the press on municipal swaps, as already critiqued here, is now as much concerned with drawing an emotional response from the reader usually with the mild sacrifice of fact or perspective. On this particular article, Peter Shapiro has already done the yeoman's duty of correcting the numerous inaccuracies on the LinkedIn Municipal Bond Forum; the exchange there is worth a read. I had a more narrow objective with the NYT; get a correction published of this sentence, "The contracts, however, assumed that economic and financial circumstances would be relatively stable and that interest rates used in the deals would stay in a narrow range." My first salvo of my triple attempt at a correction, and the senior editor's response is below.
Dear Sir/Madam:
The article "The Swaps That Swallowed Your Town," through a healthy dose of misinformation coupled with basic misunderstanding, does a great disservice to the very talented municipal and state finance officials that manage complex capital programs. No different than the homeowner who must decide to use either a fixed or adjustable rate mortgage, these officials must make tough decisions about interest rates. Most of the time they employ traditional fixed rate bonds. However, history has indicated that over substantial periods variable rates often offer lower cost than fixed rate. As a result, states and municipalities frequently allocate a certain percentage of their borrowing to variable rate bonds in order to save the taxpayer on expected interest costs. One way to manage some of the risks inherent in these variable rate bonds is through the use of interest rate swaps.
The article is not inconsistent with the above (though I believe frankly sensationalizes at the expense of clarity). The writer runs amok, and unfortunately this is the thrust of most of the article, when she states, "The contracts, however, assumed that economic and financial circumstances would be relatively stable and that interest rates used in the deals would stay in a narrow range." This is factually INACCURATE...Interest rate swaps hedging variable rate bonds are designed to work and have worked in any and all interest rate environments. To report otherwise is simply false. If the Times cares about correctly reporting information it will retract this statement immediately and visibly.
Further, what happened during the meltdown that really affected municipalities had two primary components:
1) an overreliance on risk-laden bond insurers to raise money in a very thin auction rate securities market and
2) banks retreating from providing support for variable rate bond programs.
But both of these problems had nothing to do with interest rate swaps. The Lehman collapse, if anything, showed municipalities that the swaps market functions incredibly well even with the implosion of a major player. Billions of notional were assigned in an orderly fashion to other dealers in a way that actually wound up making most affected states/municipalities better off.
Thank you for your time and I hope to see the correction quickly and visibly noted. As someone who has worked in the industry for 15+ years, this type of misinformation is both unnecessary and damaging. I hope the Times can admit when it has exaggerated a story to the point of inaccuracy. I understand it's the exaggeration that may sell more papers, but I expect the Times to hold itself to higher standards.
Sincerely,
Peter C. Orr
The reply surprisingly went into basis differential between swap and bond rates and even LIBOR index issues. Of course, this was not directly mentioned anywhere in the article.
Dear Mr. Orr,
We considered your request for a correction and have decided that the article does not contain any correctable errors. The writer, Gretchen Morgenson, noted:
"The only way the swaps would work as they were supposed to was if the interest rate the municipality paid out to its bondholders was close to the interest rate it received from the bank that entered into the swap (which was a percentage of 30-day LIBOR). This has not been the case and that is why these swaps are causing trouble for municipalities--they are paying out more than they are receiving.
That is the 'narrow range' we referred to in the column. It is not narrow anymore and that is what is causing problems for issuers."
Don Hecker
Manager/Staff Editor
But what instrument made this basis blow out??? Stay tuned for pt 2.
Posted by Peter Orr on Wed, Apr 28, 2010 @ 10:09 AM
Two people were examining the output of the new computer in their department. After an hour or so of analyzing the data, one of them remarked: "Do you realize it would take 400 men at least 250 years to make a mistake this big?" Unknown
I'm a big fan of Riccardo Rebonato. From the book on interest rate models, a required text in my grad school, to the papers he's done on interest rates measures in the "real-world", he's an extremely clear thinker on otherwise murky stuff. I can't recommend more highly his recent book, Plight of the Fortune Tellers. If you or your clients are in the business of making tough financial decisions, it's a must read and enjoyable to boot. Enough gushing (I need payment to go any further ...)
One extremely important concept woven throughout Plight is the difference between the traditional "probability as frequency" concept and the more general Bayesian or "subjective" probability. Probability as a pure frequentist concept is a special case of Bayesian/subjective probabilities that would be appropriate when looking at the likelihood of a head after a coin flip. Outside of a belief the coin is fair, no prior knowledge is necessary to reliably assess the likelihood of such an event. Contrast that with say, the probability that the Jets win the SuperBowl in 2011, or the Republicans retake the House in November, or even that gold goes over $1,500 an ounce by year end. These are all events to which we could also assign a probability, though analyzing purely historical data in a frequentist sort of way will yield few helpful results. We are much more inclined to include and use other relevant information such as the Jets strong defense going into the next season, the anti-incumbent mood of the electorate, and the growth of global money supplies.
What does this have to do with the use of raw historical data in financial decisions support analytics? A lot. Certain financial questions are better answered using frequentist concepts. Others are far more judgment-based relying on more subjective criteria and professional experience. But how do you know which situations are which? Though no hard and fast rules exist, there are basically four criteria:
Data frequency - The more relevant data you have, the more inclined towards a frequentist approach.
Time horizon - the longer the horizon of analysis, the more likely a subjective analysis will be more relevant.
Rarity of event - the more rare the event, the more the analysis calls for a Bayesian/subjective approach.
Time homogeneity of data - Were there no regime changes or other tectonic shifts in the underlying phenomena from which data was gathered? If so, analysis will tend more towards frequentist methods.
So for long time horizons, a scarcity of data, significant changes through time in the realm in which the data lives, and highly improbable events, we land squarely in the realm of subjective probabilities. Though historical/frequentist data isn't ever completely irrelevant, in these circumstances professional judgment of the situation at hand trumps pure number crunching. Unfortunately, from rating agencies to regulators to a large swath of finance professionals, this is not well understood. Things are just much more clean and simple if we allow ourselves to believe that 100 data points and a fancy model will yield 99.97% confidence precision. This is a particularly dangerous type of belief in finance, as acutely borne out over the last 18 months.
The good news is that whether frequentist or subjective, widely available probability-based models should always be used to capture risk metrics, evaluate best and worst outcomes, assess breakevens, and ultimately to avoid the ever pervasive flaw of averages.
Posted by Peter Orr on Sat, Apr 03, 2010 @ 08:22 AM
"The first and most important thing to understand about Monte Carlo is that it is a numerical technique, not a model."
Ricardo Rebonato, Plight of the Fortune Tellers
If you ever hear people talking authoritatively about their powerful "Monte Carlo model," be very suspicious of the message and the messenger. The Monte Carlo numerical method (in contrast to the lovely place on the French Riviera) is no more a "model" than addition is a "model" for ascertaining that two plus two equals four. It is simply a way to perform certain calculations. For any lingering Pythagoreans out there, Monte Carlo is specifically a very efficient way to calculate integrals in high dimensional spaces. In finance, Markov chain Monte Carlo is used for generating estimated distributions for things like interest rates, equity prices, investment returns, and exchange rates. People who think the Monte Carlo technique is a "model" are confused. My hope is this quick post clears that up and convinces you the distinction is important.
The simple fact of the matter is that once we face a situation that involves more than about three risk factors, Monte Carlo methods are the best we've got for calculating statistics of interest. Modern homo sapiens, with our flat screen TVs, computers, multi-tasking cell phones, ipads, and big brains have simply not invented anything better than Monte Carlo to evaluate these types of problems. And the more complicated the analysis, the more factors to analyze, and the better Monte Carlo does relative to other approaches. Without getting bogged down in only mildly relevant detail, this is a direct result of Monte Carlo's uniquely wonderful properties in the face of the curse of dimensionality.
So what? Why should you care? If you're like me, you hear people periodically either dismissing outright the utility of "Monte Carlo models," or alternatively gushing about how amazingly well their "Monte Carlo model" predicts the future. When you hear this now you can rest comfortably in your understanding of the much more moderate truth: neither the naysayers nor the chest thumpers are in a position to properly use Monte Carlo to help make better financial decisions. And properly used, Monte Carlo can absolutely help inform difficult financial decisions. To that end, I leave you with a quote from Mr. Black Swan himself.
"The dividend of the computer revolution to us did not come in the flooding of self-perpetuating email messages and access to chat rooms; it was in the sudden availability of fast processors capable of generating a million sample paths per minute."
Nassim Taleb,
Fooled by Randomness