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Hong Kong dollars, Indian cents, British pounds and Eskimo pence!

What’s that lever over there marked “rates” do?

Although the FOMC’s discussion of slowing or ending its Large Scale Asset Purchase (LSAP) program have induced a collective fear of a rapid pace of tightening and rate increases, leading to a steeper yield curve, it is important in moments like this to remember exactly how monetary policy works. At the present time, the Fed primarily relying on two tools to handle monetary policy: rate policy and quantitative policy. It uses these two tools to achieve its dual mandate of price stability (currently defined as a 2% symmetrical inflation target as measured by the core personal consumption expenditures deflator) and full employment. At the time of this writing, rate volatility appears to have been triggered by discussions of pulling back on the quantitative throttle by slowing the rate at which the Fed increases the size of their balance sheet. That is reducing the rate at which the Fed is easing, not tightening, although one has to wonder if, in en environment that shrugs off $85B of monthly easing, less easing than expected becomes de facto tightening.

Rate policy is primarily used as a tool to disincentivise borrowing by making it more expensive. Traditionally this was done by changing the Fed Funds target rate. Assuming a negatively sloped demand curve for loans, a higher rate of interest will reduce the quantity of loans demanded and reduce the money created by money multiplication (monetary inflation) in the banking system by limiting velocity. Ultimately, this leads to a decrease in credit-driven Consumption and/or Investment, which puts downward pressure on inflation. Until 5 years ago, FF rate targeting was primarily achieved through a mix of Open Marker Operations and changes to the discount window rate, setting a ceiling or upper bound on the Effective FF rate. In October 3, 2008 the Fed started using a new tool, Interest on Excess Reserves to (IOER), to effectively put a floor on Effective Fed Funds, the success of which has been mixed, at best.

YoY % Credit Growth

Figure 1: YoY % Credit Growth

Therefore, in order for rate increases to be necessary, inflation must approach or exceed the symmetrical target of 2% during a period of close-to or full employment. In contrast to prior cycles, the Fed has “guided” market participants as to the meaning of these measures by disclosing what is colloquially referred to as the “Evan’s Rule,” a threshold (not a trigger) which suggests unemployment to fall below 6.5% and the 2% symmetrical target to be met or breached before the target FF rate is increased.

YoY % Core PCE Price Change

Figure 2: YoY % Core PCE Price Change

Given that core PCE inflation is significantly below its symmetrical target of 2% (Figure 2), unemployment remains elevated and employment growth tepid, and annualized nominal GDP growth has decelerated since Q1 2012 and is has barely registered 3.1% for 3 consecutive quarters, we believe the risk of rapid rate increases in the near future is very low absent a very sudden and very sharp increase in demand for credit-driven Consumption or Investment.

YoY % GDP Growth

Figure 3: YoY % GDP Growth

We are not doomsayers, but it is important for participants to understand how rate policy works and why it is used. While the hand-off from quantitative policy to rate policy is a prerequisite of any change in the FF target rate (quantity or price but not both), it provides us no clue as to when this change will take place. In other words, the role of LSAPs as a signaling tool for rate policy is limited to letting us know when the rate regime has been begun again, not when it will be mobilized. Until we face loan demand that is high enough to fuel an accelerating expansion of the money supply, not only will the rate lever remain unengaged, but there will be no credible risk to price stability to warrant the use of it--we could be at the zero-bound for a long, long time.

Figure 4: KTF Neutral Policy Rate

Figure 4: KTF Neutral Policy Rate

Additionally, from the quantitative angle, many market participants claim that any rate increases will be impossible while the system remains flooded with reserves and starved of collateral, exemplified by the FF-IOER spread and GC-IOER spread or the house favorite here at Contrarian Corner, the KTF Neutral Policy Rate Rule (Figure 4), which despite talks of impeding taper keeps showing the neutral policy rate at new lifetime lows week after week (and, recently, into the uncharted waters of negative nominal rates). Many participants with expertise far beyond mine have opined that, with IOER failing to act as a credible floor, rate increases are actually impossible until reserve pumping not only stops, but a large quantity are mopped op or the size of the economy grows into the new and much-expanded monetary base. Sensei KTF would pose the question, "how many CBs have ever exited?" (spoiler alert: none) however, if the mopping of reserves was ever to become necessary in anticipation for a rate-increase, any credible attempt would begin with the Fed's latest gender-bender toy, the Full Allocation Fixed Rate Reverse Repo Facility (FAFRRRF, or "Death Star" to the initiated), which promises to to provide the invisible floor IOER failed to and bridge us as we take the leap of faith across the impassable ravine from quantity to price. I don't know about you, but I'll have my chalice filled with whiskey, if you don't mind.

Interpreting Very Scary Macro Charts

Earlier today, Fund Manager and well-known weekly market comment author John P Hussman posted the following chart on a tweet, illustrating the exponential decay of the short term interest rate as the Fed's balance sheet grew with respect to nominal GDP:

BNS4lNpCQAACxra

My first reaction was one of shock, particularly because the last 3 days of rates increasing have been painful enough as it is! There seems to be a very clear trend here that makes for a Very Scary Macro Chart. However, let's try to digest this calmly. The "You are here," arrow points to a place that looks to correspond to a 0.20 ratio of MB to NGDP. Loosely following the sample path leads us to believe that the area between 0.08 and 0.10 is home to the 2% 3m bill rate. Considering the below-target level of inflation and Chairman Bernanke's wording about not selling assets, we can conclude that, in the near-term, it would have to be an increase in NGDP that led to those higher 3m rates. Using the 0.1 barrier for the sake of simplicity, it would roughly mean NGDP doubling. To gauge the true scariness of Dr Hussman's chart, we could look at forward rates and find when the yield curve is pricing in a 2% 3m bill. Lucky for us, the Eurodollar curve provides a fast and easy way to find this out. Assuming a 15bp FF-LIBOR spread, a 2.15% 3-m LIBOR is being priced somewhere between June and September 2016 (EDM6, EDU6). Assuming no further changes to the Fed's balance sheet (although, as he indicated, more growth is planned and currently happening) it would take an annualized increase in NGDP of ~26% to get to a MB/NGDP ratio that justifies the current forward curve's pricing of a 2% 3m bill 3 years from now.

What's the interpretation? Well, it appears that the Very Scary Macro Chart would indicate that balance sheet expansion is way too dovish and that rates are too damn high, making eay for a flatter, lower yield curve; a high level of NGDP growth; and a shrinking Fed Balance sheet to be able to peacefully coexist. If that is the case, we would consider Mr Hussman to be much more of a bright-eyed, bushy-tailed optimist than he sometimes lets on.

xoxo,

GRD

The Low Return of High Yield

Over the last few weeks, as High Yield indices’ yields have continued to fall, we have seen a lot of commentary from talking heads (which, presumably, once belonged to the now-headless chickens doing the actual trading) about a potential bubble in high yield corporate bonds. This talk is often accompanied with, at worst, a chart of the HYG share price and, at best, a chart of the yield of a high yield index. These are incomplete and inaccurate indicators of risk, prospective return, and future return attribution. In this post I hope to illustrate how someone whose only career objective is to maximize P and minimize L should approach and quantify expected risk and returns.

The Laws of the Land

Rational risk taking is done at the margin

For fixed income PMs, the world is a scary place right now. Low yields, low spreads and long durations make almost every purchase unappetizing. But our job is not to worry about that, it is to make money, and you make money by taking risks. That is why it is important to avoid getting caught-up in panic and to ignore every fund manager armed with Very Scary Macro Charts.  Successful investors avoid getting caught-up in indecision and instead measure and compare risks, taking a diversified basket of the most attractive ones.

Ex-ante cash-flows with a maturity date are exempt from asset price bubbles

Bubble assets are characterized by the promise of exponential returns. Outside of negative nominal rates, the maximum return of any fixed-income asset is, by definition, fixed. Therefore Fixed Income is not, has never been and will be a bubble. Bubbles are generally dependent on significant amounts of leverage to bid-up prices. Bond yields, if bid-up with borrowed money, would trend towards the cost of financing, at which point no rational party would finance the assets. That, however, doesn’t mean that buyers of bonds are not accepting uneconomic credit risk premiums leading to low, or negative, expected returns. Only once we accept this as truth, can we move past hubris propagated by talking-heads and make rational economic decisions.

In credit, yield to maturity is not expected return

Bond issuers default, speculative issuers default more than others. Whenever one is analyzing a large basket of independent debtors, it is important to adjust yield to maturity for expected default rates, making sure to account for the loss of the next coupon. For speculative-grade issuers, 1982-2012 mean annual historic default, recovery and loss rates are reproduced  (Moody’s Investors Service, Inc. , 2013).

spec grade default stats

As illustrated, high yield bonds default at a non-trivial rate, therefore it is important to adjust yields for expected credit losses; this is commonly referred to as loss-adjusted yield (LAY). Credit losses can be thought as a function of default rates and severities (1-recovery), a surface which can be visualized below where lighter color mean smaller credit losses and the x and y axes represent default rates and severities (in the second, you can think of the z axis as the remaining principal in a pool of bonds) (Wolfram Alpha)

severity topo mapseverity surface

A rough way to estimate expected returns for a static default rate and recovery rate for a pool paying annual coupons trading close to par would be:

E[r] = (YTM * (1 - d)) - (d * (1-r))

Where E[r] represents your expected return, YTM is Yield to Maturity, d is the annual default rate and r is the expected recovery rate. For example, assuming a 4.6% default rate, 42% recovery rates and 5% YTM the expected return would be about 2.1%.

0.021 =  .05 * (1 - 0.046) - 0.046 * (1 - 0.42)

Yields, spreads and break-even rates

For non-floating, non-callable bonds, the coupon rate is primarily composed of two rates, the risk-free interest rate, as measured by the US Treasury curve, and the credit spread. It is important to note here that credit spread must compensate buyers not only for credit losses, but also for the lost income on the base rate of the bond. Expanding the equation above,

E[s] = s - (YTM * d) - (d * (1-r))

Assuming a spread of 3% and keeping other variables constant this would mean that the additional return realized for taking the credit risk is only 0.1%. With a little bit of simple high-school algebra (or calculus if you are so inclined) we can set  E[s] to zero, and solve for the default rate at which no risk premium would be realized, the break-even default rate, which would be 4.5% using the same assumptions. You can calculate the break-even default rate by rearranging the equation to:

d = spread / (YTM + (1 – recovery))

Because spread and YTM are known ex-ante, we can think about the break-even default rate as function of recovery rate and calculate default scenarios for various recovery scenarios. Below is an illustration of what the break-even default rate (y-axis) would be as a function of recovery rates (x-axis) using a 5% yield and 3% spread (Wolfram Alpha)

break even default rate curve

The term premium and risk adjustments

Ex-ante cash-flows and zero credit risk make US Treasury strips the best benchmark to use when comparing risk premiums; we know with precision exactly what the final rate-of-return of an investment has to beat in order to be worth considering and because duration is equal to maturity we can estimate price volatility using past historical interest rate volatility. For example, a UST strip with a maturity of May 2018 (S 0 05/31/18 Govt  912834KJ6) traded at a mid-yield of 0.79% on May 8th. This means that, assuming a 5y expected-life, in order for any isolated risk to be economic, it would need to have an expected return above 0.79%.

Louise Yamada never misses an opportunity to remind us that there are only two types of losses: losses of opportunity and losses of capital and that there will always be another opportunity if you protect capital. We can apply this philosophy to speculative grade bonds by considering negative loss-adjusted yields losses of capital and mark-to-market risk (adverse price volatility) a loss of opportunity.  Limit the potential of loss of capital (negative loss-adjusted returns) and take measured opportunity risk (liquidity risk) when the compensation is adequate.

The foundation of measuring and comparing the aforementioned risk of loss of opportunity is that a risk-free 0.79% is superior to an uncertain 0.79%. There is different ways to adjust for this risk, the simplest of which is weigh the volatility of both assets  and calculate how much additional expected return we are receiving  for every unit of additional unit of price volatility. For the marginal risk taking to be rational, it must offer a volatility-adjusted premium in addition to being economic (unlikely to breach the break-even default rate for our recovery scenarios).  Rational risk-taking should increase the ratio of expected-return (E[r]) to expected volatility (E[sd]). Because funding isn’t free and we can’t leverage efficient assets at a zero cost, it makes sense to subtract the cost-of-funding from our calculations of this ratio. If this seems familiar, it is because this measure is the Sharpe ratio.

The dynamic nature of fair credit premiums

In the last section I differentiated between economic marginal risk-taking, where E[r] increases with marginal risk-taking, and rational marginal risk-taking, where E[r] marginally increases in a proportion greater than E[sd].  If marginal risk taking is performed on a relative basis, this means that—all else equal—lower short-term risk-free rates combined with lower term premiums (a flatter yield curve) would mean that lower credit spreads are justified as long as they remain economic. In other words, as credit-risk-free yields decline, the spread above expected losses at which a risk becomes rational to take declines. In simpler terms, lower term premiums justify lower credit spreads and vice-versa.

Enforcing the Laws

A simple example

On May 8th, the BofA Merrill Lynch US High Yield CCC index had an effective yield (EY) of 8.31% and an option adjusted spread (OAS) of 7.53%. There is no average maturity published, but the 78bp gap between OAS and EY hints that it is probably close to 5 years (a May 31st strip traded at a mid-yield of 0.79%).

According to the table above the average recovery rate for Senior Unsecured bonds rated Caa-C is approximately 36%, which would make the break-even default rate for this pool of bonds 10%, about 0.5sd below the long term mean. If we look at the historical mean annual credit loss rates, we can see that the spread only compensates the holders for approximately 50% of the mean credit loss rate.  If past default trends are indicative of future default-trends, buying this index will likely be uneconomic and there is a significant risk of loss of capital.

We’ve identified what it takes for this risk to be uneconomic, but a more interesting question is: at what spread does it becomes rational to take this risk? Assuming the coupon rate is equal to the YTM and a 5-year life, the duration of the index would be 4.2y. Using historic data, the annualized standard deviation of the index price would be 14.5% and that of the aforementioned STRIP 5.34%. That means that, assuming a 15bp funding cost[i], the expected return of the CCC index would have to be higher than:

min E[r] = funding-cost + (risky-price-volatility * (RF yield – funding cost) / RF-price-volatility
min E[r] = 2.77% = 0.15% + (14.5% * (0.79% - 0.15%)) / 3.54%

Plugging in 2.77% and rearranging our E[r] equation for a function d(r):

d = 0.0554 / (1.0831 - r) = (2.77% - 8.31%) / (r - 8.31% - 1) = (E[r] - YTM) / (r - YTM - 1)

This means that, if assuming a recovery rate of 36%, the default-rate below which buying the index becomes rational is 7.6%. Alternatively, we could start with the median default rate of 17.7% and solve for the fair yield[ii]

YTM = 16.5% = 2.77% + (17.7% * (1 - 36%)) / (1 - 17.7%) = (E[r] + (d * (1 - r))) / (1-d)

A look at the current environment

The following example applies the methods described above to the 5, 7, and 10-year STRIPS as well as the BofA Merrill Lynch US High Yield BB, B and CCC indices to examine, using present conditions, what the relative value of speculative grade credit is. You will notice that the following tables eschew default-rates and use historical credit-loss rates (default rate * severity) which we believe convey expected losses just-as, if not more, accurately and allow us to present the results in a more concise format.

1-givens 2-exp-loss-vol 3-lay-sharpe

The first table shows us the observed inputs for each asset class, the second table highlights our assumptions calculated from historical data for 3 different scenarios and the third table presents the results for each asset under each scenario.

The first thing you should notice is that expected returns for Ccc credit are negative in all three scenarios. There is significant risk of loss of capital, therefore comparing them to other assets based on price volatility does not make sense.   Because the expected returns are positive or all remaining assets, we can now compare BB and B bonds vs a benchmark. To identify the bench mark, we attempt to find the most efficient risk-free investment with a similar holding period. In this case, the 7y STRIP has the highest expected ratio in both the adverse and baseline scenarios, and is close to being the highest in the optimistic scenario as well. We can consider the 7y STRIP our benchmark to test for economic viability of a risk. In the case of B bonds, we find that our expected value is inferior to the benchmark rate and, while the optimistic scenario is positive, the adverse scenario has a very low expected return. Taking credit risk in B-rated bonds is likely to be uneconomical. That leaves BB-rated bonds which have an expected yield that readily exceeds the benchmark in all scenarios and, additionally, are subject to less price volatility. Taking credit risk in these bonds would be both economical and rational according to the model.

Uneconomic spreads and the inefficient market

Doe-eyed believers in efficient markets might be wondering how Ccc expected yields could be negative and why B-rated bonds are overpriced. We don’t pretend to know or care about why people willingly enter into investments likely to provide poor returns but, if you are interested, Eric Falkenstein wrote an excellent book about it, The Missing Risk Premium: Why Low Volatility Investing Works, in which—amongst other great ideas—he theorizes that risk premiums are negative for volatile assets with compelling data to support this thesis. Our go-to explanation is much simpler: people can’t resist a high-coupon and overestimate their ability to pick the bond that won’t default.

Caveats

  • No attempt has been made at quantifying returns from roll-down, bonds being called, tendered, or undergoing a change in rating other than default.
  • The calculations presented ignore convexity for simplicity of the example
  • Positive expected returns may result in losses due to spread widening
  • As mentioned in, “The Dynamic Nature of Fair Credit Premiums” a steeper yield curve would, all-else equal, mean higher “fair” spreads. This means that interest rate risk is understated by duration alone.
  • The speculative-grade market is relatively new and the data sample is limited
  • Due to lack of time-series data, we cannot confirm the accuracy of recovery rates.
  • We make no attempt to quantify or consider an additional liquidity premium
  • The loss rates cited are for Senior Unsecured obligations. The bonds constituting the index may be junior or secured, leading to lower or higher expected rates of recovery respectively.

Works Cited

Moody’s Investors Service, Inc. . (2013). Annual Default Study: Corporate Default and Recovery Rates, 1920-2012.

Standard & Poors. (2013, February 07). Global Weakest Links And Default Rates: The Weakest Links Count Fell To 149 In February. Retrieved from http://www.standardandpoors.com/ratings/articles/en/us/?articleType=HTML&assetID=1245348670006

Wolfram Alpha. (n.d.). y=.03/(.05+(1-x)), x=0..1. Retrieved from http://www.wolframalpha.com/input/?i=y%3D.03%2F%28.05%2B%281-x%29%29%2C+x%3D0..1&a=*MC.0!.!.1-_*NumberMath-

Wolfram Alpha. (n.d.). z = 1-(y * x), y=0..1, x=0..1. Retrieved from Wolfram Alpha: http://www.wolframalpha.com/input/?i=z+%3D+1-%28y+*+x%29%2C+y%3D0..1%2C+x%3D0..1&a=*MC.0!.!.1-_*NumberMath-

 


[i] Using a 0.015% cost of funds (the yield of a 1y STRIP) might seem low for investors borrowing at the call-money rate, but it isn’t too far from the rates available for repo of USTs or the embedded cost-of-funds in derivatives.

[ii] This example exaggerates the yield required due to the effect of convexity. If the pool’s yield was 16.5% the duration of the pool would diminish by ~15%.

[iii] In positively-sloped yield curve environment, bonds will “roll” and YTM understates their expected total return over shorter holding periods

[iv] Maturities were estimated using the difference between effective yield and Option-Adjusted Spreads.

[v] Assumed credit loss rates were derived using historical rolling 5-year loss data. The baseline, optimistic and adverse scenarios correspond to the annual loss rate of a 50, 25 and 75 percentile periods respectively

[vi] In positively-sloped yield curve environments, expected price volatility for STRIPS will be overstated due to the falling duration of yield not being internalized

How Leveraged Money Becomes Less Leveraged (and less money)

One of the seminal moments in my career was when—during a long rant full of words like “structural, “endogenous” and “secular” in which I tried to defend the reason why I did not want to make an investment—I was interrupted by an older and wiser colleague who said to me, “kid, kid, shut-up. Just shut-up. You don’t get paid to have opinions; you get paid to have positions.” A fitting complement for Sensei KTF’s favorite, “It’s all about the positions.”

What I am referring to, of course, is that prices are set on the margin and that knowing the sensitivity of the marginal actor can and does give you an insight into how price will change. As Kevin hinted earlier this weekend in “A Poorly Run Casino,” we are now facing the economic consequences of the early aughts’ “commodities as an asset class” meme which was further aggravated with the popularity of momentum and/or “trend following” strategies. Money has flowed into commodities, initially from top-down allocators armed with spreadsheets and sharpe-ratios[1], followed my mom and pop’s registered rep who has now accepted as dogma that 15% of a portfolio should go into commodities funds, all but evaporating whatever dubious risk-premium (n)ever existed. Unfortunately for me and you and everyone we know, the size of the flows means that the open interest in many markets is now dominated by speculators—not just any speculator, but “managed money” aka, people like me, people paid to take positions, people who are by and large insensitive to current conditions and must take positions. For those in the cheap seats, let me spell it out for you: the marginal actor has become increasingly price insensitive. A poorly run casino, indeed.

Dr Copper needs a nurse

Despite CFTC reporting parties only making-up 15% of the global copper futures markets, we have strong evidence suggesting that, since 2006, managed-money (the marginal actor) has been dominated by non-economic actors with low price-elasticity, i.e. shameless price-takers. In other words, the volatility and slope of both the supply and demand curves (and by extension the marginal transaction and therefore price) is under the control of a very fickle and non-economically motivated herd that has exploded rapidly in size.

The chart below contains 4 time series,

  1. Weekly spot price of LME copper (bars) 20 week moving average (gray) and  2 standard-deviation Bollinger Bands (red and green)[2]
  2. Money Manager Net Positioning (# of  net-long contracts) / Open interest in high grade copper
  3. % BB of Chart 2 (Price - lower band) / (upper band - lower band)
  4. % BB of Chart 1 (Price - lower band) / (upper band - lower band)

copper bands

What is primarily of note here is the extremely high coincidence of the lower two oscillators, which tell us that money manager sentiment is highly coincident with over-bought and over-sold conditions. Second, it is of note that the money manager net-position can be as high as 20% of total open interest (currently, managed money constitutes 38.4% of the short and 13.9% of the long contracts). This strongly suggests that the overall sentiment of non-economic buyers is the responsible for practically all of the short-term (20 weeks, in this case) volatility in price. In fact, as the scatter-plot below illustrates, the correlation between the two oscillators is a high 0.84, and regressing money manager sentiment to predict price deviation results in an R-squared of 0.90, with a coefficient of 0.954 when electing to use no intercept. In other words, managed money is always longest before the drop and shortest before the rally.

copper pct BB scatter

The scatter-plot above shows the values of both oscillators and their very clear linear relationship, with observations less than one standard deviation from the model’s prediction in grey, those between 1 and 2 in blue, between 2 and 3 in green and greater than 3 in red. The latest observation is circled in red.

Those that would like to play devil’s advocate will counter that “correlation does not imply causation” and regular readers will reproduce Kevin’s “nothing changes people’s opinion like price.” It is our official opinion (and our position) that managed-money is the marginal price-setter and managed-money acts based on past price movements. This creates and perpetuates momentum-sparked self-feedback loops that all but guarantee that, as we approach inflection points, leveraged managed money (whether long or short) will become both less leveraged and less money. As very much economically-motivated buyers and sellers with real positions, real P&L and real margin clerks, the last thing we want to see is a rapidly increasing crowd running at increasingly faster speeds from one side of the boat to the other, which is, as you can see below, exactly what has been happening for the last seven years.

copper mm OI


[1] Inker, Ben. February 2013. We Have Met the Enemy and He Is Us

[2] LMCADY Index. LME spot copper price London settle

Too much money, not enough paper, redux

In my October 18th piece, Too much money, not enough paper, I pointed out that the Fed’s LSAP programs had created an excess of money and a scarcity of interest-bearing assets, a period I’ve jokingly referred to as The Great Incredible Paper Chase for the last year. In that post I mentioned that “debt issuers have done what any rational party would have done when there is more demand than supply of a good they can produce, they've stepped up production.” And that, “As long as the Fed is expanding their balance sheet, the market will have the capacity to absorb new issuance.” Since the onset of the first LSAP program, corporate bond debt has grown by $1,386B (+34.4%)  while non-financial business (corporate and non-) excluding corporate bond debt has shrunk by $765B (-5.18%). However, despite this breath-taking corporate debt issuance and record-setting deficits (supply), long-term rates have managed to fall for all four of the years in question thanks to $1,277B in balance sheet expansion by the Fed.

On December 12th, the Fed announced a new LSAP program aimed at replacing Operation Twist 2 in which the Fed will buy $45B in notes and bonds in an attempt to “maintain downward pressure on longer-term interest rates.” For those of you keeping score, that—when combined with the Agency MBS purchases of $40B—amounts to $85B in monthly Fed Balance Sheet expansion, or $1,020B/yr. The actions were pooh-poohed by many a commentator and talking-head that don’t know their Ps from their Ls, many of which, as usual, called for higher rates, in no doubt riding the coat-tails of a certain Westport, CT hedge fund manager who predicted that “at some point” there would be a vast fortune to be made by “someone” by “shorting bonds.” Your not-so-humble correspondent would like to remind you know that said commentators have absolutely no clue how supply and demand actually work and said hedge fund managers are merely trying to scare you into indecision. While it is true that the FOMC’s September 2012 announcement to buy $40B/mo. in Agency MBS has had little effect so far, commentators are ignorant as to prepayment and settlement mechanics of the TBA market which mean that, while the risk has changed hands already, the cash and product are lagging a few weeks behind. But make no mistake, the balance sheet will swell (in fact, it just started doing so ~7wks ago) and once the full effect is felt there will be an increasingly acute shortage of yield-bearing high quality assets. In particular, my calculations indicate there is $87.6B in as-of today unsettled Agency MBS purchases due to settle in the next 8 weeks.

To put the LSAP numbers in context, according to the Federal Reserve’s Z.1 release, the combined private sector, as of the latest reported quarter, is creating debt at a yearly rate of $345B/yr. (+1.08%), of which $481B (+2.54%) can be attributed to non-financial business and -$132B (-1.04%) can be attributed to households ex-student loans owned by the federal government (1). The bulk of household deleveraging took place through mortgage debt reduction (by repayment or cancellation) of $270B (-2.78%) and the bulk of business credit creation took place in the corporate bond market, where bond issuance was an astonishing $426B (+8.55%). The state & local government sector had no significant change in debt issuance, with long-term municipal security issuance remaining relatively unchanged at $3.2B (+0.10%). In that same period, federal debt held by the public increased by $1,142B (+11.27%) and  total Agency- and GSE-Backed security liabilities shrank by $41B (-0.54%) despite growth in the mortgage market share, growth which will be difficult to repeat without a TARP-like program open to households with non-Agency backed mortgages. In fact, even though the economy-wide deleveraging has stopped, outside of auto loans, student loans and corporate bonds, deleveraging and net negative security issuance remains an ongoing theme.

At the of this writing, our good-for-nothing legislators still have not managed to produce a budget, which means that I am unable to give you a read on next year’s deficit, but assuming a deficit range of $900B-$1,200B and continued net negative supply in the Agency- and GSE-Backed security markets to the tune of $0 to $40B, it is quite probable that net issuance of (credit) risk-free assets will be either flat or negative in 2013. That is to say, there will be even more money and even less paper ($1,107.6B to be precise; $1,020B in new purchases and $87.6B unsettled Agency MBS purchases). Add to that the approximately $1,500B in accounts currently covered by TAG, much of which I expect to move to T-Bills and other high-quality money market instruments, and it may very well be that the theme of 2013 will be one of a scarcity of high-quality collateral as we face net negative supply and increasing demand, which is something that I urge those of you who do know your Ps from your Ls to consider before going all-in shorting treasuries intoxicated by some fantasy of Soros-esque riches and fame.

  1. we subtract these so as not to double-count as we consider these to ultimately be public obligations