Tag Archive for 'Credit crisis'

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US February Employment and Recession vs. Depression

Published on 13 March 2009 in Economics and USA. Tags: , , , , , , , , , , . 2 Comments

The preliminary employment data for February in the USA has been out for a little while now and I thought it worthwhile to update the graphs I did after January’s figures.

As I explained when producing the January graphs, I believe that it’s more representative to look at Weekly Hours Worked Per Capita than at just the number of people with jobs so as to more fully take into account part-time work, the entry of women into the labour force and the effects of discouraged workers.  Graphs that only look at total employment (for example: 1, 2) paint a distorted picture.

The Year-over-Year percentage changes in the number of employed workers, the weekly hours per capita and the weekly hours per workforce member continue to worsen.  The current recession is still not quite as bad as that in 1981/82 by this measure, but it’s so close as to make no difference.

Year-over-year changes in employment and hours worked

Just looking at year-over-year figures is a little deceptive, though, as it’s not just how far below the 0%-change line you fall that matters, but also how long you spend below it.  Notice, for example, that while the 2001 recession never saw catastrophically rapid falls in employment, it continued to decline for a remarkably long time.

That’s why it’s useful to compare recessions in terms of their cumulative declines from peak:

Comparing US recessions relative to actual peaks in weekly hours worked per capitaA few points to note:

  • The figures are relative to the actual peak in weekly hours worked per capita, not to the official (NBER-determined) peak in economic activity.
  • I have shown the official recession durations (solid arrows) and the actual periods of declining weekly hours worked per capita (dotted lines) at the top.
  • The 1980 and 2001 recessions were odd in that weekly hours worked per capita never fully recovered before the next recession started.

The fact that the current recession isn’t yet quite as bad as the 1981/82 recession is a little clearer here.  The 1973-75 recession stands out as being worse than the current one and the 2001 recession was clearly the worst of all.

There’s also some question over the US is actually in a depression rather than just a recession.  The short answer is no, or at least not yet.  There is no official definition of a depression, but a cumulative decline of 10% in real GDP is often bandied around as a good rule of thumb.  Here are two diagrams that illustrate just how much worse things would need to be before the US was really in a depression …

First, from The Liscio Report, we have an estimated unemployment rate time-series that includes the Great Depression:

Historic Unemployment Rates in the USA

Second, from Calculated Risk, we have a time-series of cumulative declines in real gdp since World War II:

Cumulative declines in real GDP (USA)

Remember that we’d need to fall to -10% to hit the common definition of a depression.

An important point

Published on 12 March 2009 in Economics. Tags: , , . 0 Comments

Mark Thoma writes:

Many people want more than just an admission of responsibility, but I don’t think economic recovery depends upon that happening.

Is economics looking at itself?

Published on 11 March 2009 in Academia, Economics and Epistemology. Tags: , , , , , , , , , , , , , . 0 Comments

Patricia Cowen recently wrote a piece for the New York Times:  “Ivory Tower Unswayed by Crashing Economy”

The article contains precisely what you might expect from a title like that.  This snippet gives you the idea:

The financial crash happened very quickly while “things in academia change very, very slowly,” said David Card, a leading labor economist at the University of California, Berkeley. During the 1960s, he recalled, nearly all economists believed in what was known as the Phillips curve, which posited that unemployment and inflation were like the two ends of a seesaw: as one went up, the other went down. Then in the 1970s stagflation — high unemployment and high inflation — hit. But it took 10 years before academia let go of the Phillips curve.

James K. Galbraith, an economist at the Lyndon B. Johnson School of Public Affairs at the University of Texas, who has frequently been at odds with free marketers, said, “I don’t detect any change at all.” Academic economists are “like an ostrich with its head in the sand.”

“It’s business as usual,” he said. “I’m not conscious that there is a fundamental re-examination going on in journals.”

Unquestioning loyalty to a particular idea is what Robert J. Shiller, an economist at Yale, says is the reason the profession failed to foresee the financial collapse. He blames “groupthink,” the tendency to agree with the consensus. People don’t deviate from the conventional wisdom for fear they won’t be taken seriously, Mr. Shiller maintains. Wander too far and you find yourself on the fringe. The pattern is self-replicating. Graduate students who stray too far from the dominant theory and methods seriously reduce their chances of getting an academic job.

My reaction is to say “Yes.  And No.”  Here, for example, is a small list of prominent economists thinking about economics (the position is that author’s ranking according to ideas.repec.org):

There are plenty more. The point is that there is internal reflection occurring in economics, it’s just not at the level of the journals.  That’s for a simple enough reason – there is an average two-year lead time for getting an article in a journal.  You can pretty safely bet a dollar that the American Economic Review is planning a special on questioning the direction and methodology of economics.  Since it takes so long to get anything into journals, the discussion, where it is being made public at all, is occurring on the internet.  This is a reason to love blogs.

Another important point is that we are mostly talking about macroeconomics.  As I’ve mentioned previously, I pretty firmly believe that if you were to stop an average person on the street – hell, even an educated and well-read person – to ask them what economics is, they’d supply a list of topics that encompass Macroeconomics and Finance.

The swathes of stuff on microeconomics – contract theory, auction theory, all the stuff on game theory, behavioural economics – and all the stuff in development (90% of development economics for the last 10 years has been applied micro), not to mention the work in econometrics; none of that would get a mention.  The closest that the person on the street might get to recognising it would be to remember hearing about (or possibly reading) Freakonomics a couple of years ago.

How to value toxic assets (part 6)

Published on 23 February 2009 in Economics, Epistemology, Finance, UK and USA. Tags: , , , , , . 0 Comments

Via Tyler Cowen, I am reminded (again) that I should really be reading Steve Waldman more often.  Like, all the time.  After reading John Hempton’s piece that I highlighted last time, Waldman writes, as an afterthought:

There’s another way to generate price transparency and liquidity for all the alphabet soup assets buried on bank balance sheets that would require no government lending or taxpayer risk-taking at all. Take all the ABS and CDOs and whatchamahaveyous, divvy all tranches into $100 par value claims, put all extant information about the securities on a website, give ‘em a ticker symbol, and put ‘em on an exchange. I know it’s out of fashion in a world ruined by hedge funds and 401-Ks and the unbearable orthodoxy of index investing. But I have a great deal of respect for that much maligned and nearly extinct species, the individual investor actively managing her own account. Individual investors screw up, but they are never too big to fail. When things go wrong, they take their lumps and move along. And despite everything the professionals tell you, a lot of smart and interested amateurs could build portfolios that match or beat the managers upon whose conflicted hands they have been persuaded to rely. Nothing generates a market price like a sea of independent minds making thousands of small trades, back and forth and back and forth.

I don’t really expect anybody to believe me, but I’ve been thinking something similar.

CDOs, CDOs-squared and all the rest are derrivatives that are traded over the counter; that is, they are traded entirely privately.  If bank B sells some to hedge fund Y, nobody else finds out any details of the trade or even that the trade took place.  The closest we come is that when bank B announces their quarterly accounts, we might realise that they off-loaded some assets.

On the more popularly known stock and bond markets, buyers publicly post their “bid” prices and sellers post their “ask” prices. When the prices meet, a trade occurs.[*1] Most details of the trade are then made public – the price(s), the volume, the particular details of the asset (ordinary shares in XXX, 2-year senior notes from XXX with an expiry of xx/xx/xxxx, etc) – everything except the identity of the buyer and seller. Those details then provide some information to everybody watching on how the buyer and seller value the asset. Other market players can then combine that with their own private valuations and update their own bid or ask prices accordingly. In short, the market aggregates information. [*2]

When assets are traded over the counter (OTC), each participant can only operate on their private valuation. There is no way for the market to aggregate information in that situation. Individual banks might still partially aggregate information by making a lot of trades with a lot of other institutions, since each time they trade they discover a bound on the valuation of the other party (an upper bound when you’re buying and the other party is selling, a lower bound when you’re selling and they’re buying).

To me, this is a huge failure of regulation. A market where information is not publicly and freely available is an inefficient market, and worse, one that expressly creates an incentive for market participants to confuse, conflate, bamboozle and then exploit the ignorant. Information is a true public good.

On that basis, here is my idea:

Introduce new regulation that every financial institution that wants to get support from the government must anonymously publish all details of every trade that they’re party to. The asset type, the quantity, the price, any time options on the deal, everything except the identity of the parties involved. Furthermore, the regulation would be retroactive for X months (say, two years, so that we get data that predates the crisis).  On top of that, the regulation would require that every future trade from everyone (whether they were receiving government assistance or not) would be subject to the same requirementes.  Then everything acts pretty much like the stock and bond markets.

The latest edition of The Economist has an article effectively questioning whether this is such a good idea.

[T]ransparency and liquidity are close relatives. One enemy of liquidity is “asymmetric information”. To illustrate this, look at a variation of the “Market for Lemons” identified by George Akerlof, a Nobel-prize-winning economist, in 1970. Suppose that a wine connoisseur and Joe Sixpack are haggling over the price of the 1998 Château Pétrus, which Joe recently inherited from his rich uncle. If Joe and the connoisseur only know that it is a red wine, they may strike a deal. They are equally uninformed. If vintage, region and grape are disclosed, Joe, fearing he will be taken for a ride, may refuse to sell. In financial markets, similarly, there are sophisticated and unsophisticated investors, and unless they have symmetrical information, liquidity can dry up. Unfortunately transparency may reduce liquidity. Symmetry, not the amount of information, matters.

I’m completely okay with this. Symmetric access to information and symmetric understanding of that information is the ideal. From the first paragraph and then the last paragraph :

… Not long ago the cheerleaders of opacity were the loudest. Without privacy, they argued, financial entrepreneurs would be unable to capture the full value of their trading strategies and other ingenious intellectual property. Forcing them to disclose information would impair their incentive to uncover and correct market inefficiencies, to the detriment of all …

Still, for all its difficulties, transparency is usually better than the alternative. The opaque innovations of the recent past, rather than eliminating market inefficiencies, unintentionally created systemic risks. The important point is that financial markets are not created equal: they may require different levels of disclosure. Liquidity in the stockmarket, for example, thrives on differences of opinion about the value of a firm; information fuels the debate. The money markets rely more on trust than transparency because transactions are so quick that there is little time to assess information. The problem with hedge funds is that a lack of information hinders outsiders’ ability to measure their contribution to systemic risk. A possible solution would be to impose delayed disclosure, which would allow the funds to profit from their strategies, provide data for experts to sift through, and allay fears about the legality of their activities. Transparency, like sunlight, needs to be looked at carefully.

This strikes me as being around the wrong way.  Money markets don’t rely on trust because their transactions are so fast; their transactions are so fast because they’re built on trust.  The scale of the crisis can be blamed, in no small measure, because of the breakdown in that trust.

I also do not buy the idea of opacity begetting market efficiency.  It makes no sense.  The only way that information disclosure can remove the incentive to “uncover and correct” inefficiencies in the market is if by making the information public you reduce the inefficiency.  I’m not suggesting that we force market participants to reveal what they discover before they get the chance to act on it.  I’m only suggesting that the details of their action should be public.

[*1] Okay, it’s not exactly like that, but it’s close enough.

[*2] Note that information aggregation does not necessarily imply that the Efficient Market Hypothesis (EMH), but the EMH requires information aggregation to work.

Other posts in this series:  1, 2, 3, 4, 5, [6].

Whyte is wrong to think that Brown is wrong

Published on 22 February 2009 in Economics and Finance. Tags: , , . 0 Comments

Writing in Friday’s FT, Jamie Whyte argues that Gordon Brown is wrong to think that regulating bankers’ bonuses to stop the culture of short-term thinking will avoid future financial crises.  He writes:

[I]magine you are the manager of a lottery company. Your job is similar to a banker’s. You sell tickets (make loans) that have a certain probability of winning a prize (of defaulting). To ensure long-run profits, you must set a price for the tickets (charge a rate of interest) that is sufficient to pay out the lottery winnings (cover the cost of defaulting borrowers).

But suppose you were a greedy lottery company manager, concerned more with your own bonus than with your shareholders’ interests. Here is a trick you might play. Offer jackpots, ticket odds and ticket prices that in effect give your customers money. For example, offer $1 tickets with a one-in-5m chance of winning a $10m prize. A one-in-5m chance of winning $10m is worth $2 . So each ticket represents a gift of $1 to its purchaser.

With such an attractive “customer value proposition” you would leave your competitors for dead. And if you limited ticket sales to, say, 1m a year, the chances are no one would win the prize. In most years you will earn $1m in ticket sales and pay nothing in prizes. When someone finally wins the $10m prize, and your company collapses, that will be a problem for shareholders and creditors; you will probably have pocketed a few nice bonuses already.

To prevent such wickedness, Mr Brown may insist that lottery managers be paid bonuses on the basis of long-term profits: five years’, let us say. No problem: simply set the prize at $100m and the chance of winning at one in 50m. Then you will be unlucky if anyone wins in a five-year period, and you can be confident of walking away with a fat bonus.

This is why, even if Mr Brown were right that short-term bonus plans caused the financial crisis, his proposed remedy would not help. Whatever time frame he mandates, it will always be too short. For, like lottery managers, bank managers can manipulate the “risk profile” of the bank so that large losses, although inevitable in the long run, are unlikely during the mandated period.

I like Mr. Whyte’s analogy, but as far as I can see, there are three problems in his logic.  For the sake of some numbers to talk about, I’ll consider the idea of a five-year delay in high-end bankers having access to their bonuses.

First, he’s missing the fact that for his lottery company to offer a prize of $100 million, it’s going to need some backers with much deeper pockets than if his prize is only $10 million.  Whyte quite correctly points out that risk has been mispriced, but provided that it’s got some price, scaling up without a larger customer pool (the equivalent of increasing the leverage of your bank) must come with extra costs.  Even if the wholesale market is willing to stand behind you, one option is to increase the duration until the size needed to outflank it would require bank mergers that would run foul of competition law.

Second, a key feature long-term bonuses is that they accumulate.   If bonuses are awarded annually but placed into escrow for five years, then even if the bad event doesn’t happen until year 10, there will be five years of bonuses available for claw-back.  All the bankers are currently giving up one year of bonuses.  By putting bonuses to one side, we magnify the value at risk faced by the bankers themselves.

Third, we need to recognise that nobdy lives forever, and while one year might not be so long when measured against a career, five years is a serious block of time.  The reputational effects of any failure would be increased and, I hope, institutional memory would be improved.

As I say, I agree that a mispricing of risk lies at the heart of the credit crisis.  I simply disagree with Mr. Whyte on why it occured.  I’m not sure why he thinks it occured, but I think that part of the cause is the short-term nature of bank incentives.

How to value toxic assets (part 5)

Published on 17 February 2009 in Economics, Epistemology, Finance, UK and USA. Tags: , , , , , . 0 Comments

John Hempton has an excellent post on valuing the assets on banks’ balance sheets and whether banks are solvent.  He starts with a simple summary of where we are:

We have a lot of pools of bank assets (pools of loans) which have the following properties:
  • The assets sit on the bank’s balance sheet with a value of 90 – meaning they have either being marked down to 90 (say mark to mythical market or model) or they have 10 in provisions for losses against them.
  • The same assets when they run off might actually make 75 – meaning if you run them to maturity or default the bank will – discounted at a low rate – recover 75 cents in the dollar on value.

The banks are thus under-reserved on an “held to maturity” basis. Heavily under-reserved.

He then gives another explanation (on top of the putting-Humpty-Dumpty-back-together-again idea I mentioned previously) of why the market price is so far below the value that comes out of standard asset pricing:

Before you go any further you might wonder why it is possible that loans that will recover 75 trade at 50? Well its sort of obvious – in that I said that they recover 75 if the recoveries are discounted at a low rate. If I am going to buy such a loan I probably want 15% per annum return on equity.

The loan initially yielded say 5%. If I buy it at 50 I get a running yield of 10% – but say 15% of the loans are not actually paying that yield – so my running yield is 8.5%. I will get 75-80c on them in the end – and so there is another 25cents to be made – but that will be booked with an average duration of 5 years – so another 5% per year. At 50 cents in the dollar the yield to maturity on those bad assets is about 15% even though the assets are “bought cheap”. That is not enough for a hedge fund to be really interested – though if they could borrow to buy those assets they might be fun. The only problem is that the funding to buy the assets is either unavailable or if available with nasty covenants and a high price. Essentially the 75/50 difference is an artefact of the crisis and the unavailability of funding.

The difference between the yield to maturity value of a loan and its market value is extremely wide. The difference arises because you can’t eaily borrow to fund the loans – and my yield to maturity value is measured using traditional (low) costs of funds and market values loans based on their actual cost of funds (very high because of the crisis).

The rest of Hempton’s piece speaks about various definitions of solvency, whether (US) banks meet each of those definitions and points out the vagaries of the plan recently put forward by Geithner.  It’s all well worth reading.

One of the other important bits:

Few banks would meet capital adequacy standards. Given the penalty for even appearing as if there was a chance that you would not meet capital adequacy standards is death (see WaMu and Wachovia) and this is a self-assessed exam, banks can be expected not to tell the truth.

(It was Warren Buffett who first – at least to my hearing – described financial accounts as a self-assessed exam for which the penalty for failure is death. I think he was talking about insurance companies – but the idea is the same. Truth is not expected.)

Other posts in this series:  1, 2, 3, 4, [5], 6.

Perspective (Comparing Recessions)

Published on 11 February 2009 in Economics and USA. Tags: , , , , , , , , , , , , , , . 0 Comments

This is quite a long post.  I hope you’ll be patient and read it all – there are plenty of pretty graphs!

I have previously spoken about the need for some perspective when looking at the current recession.  At the time (early Dec 2008), I was upset that every regular media outlet was describing the US net job losses of 533k in November as being unprecedentedly bad when it clearly wasn’t.

About a week ago, the office of Nancy Pelosi (the Speaker of the House of Representatives in the US) released this graph, which makes the current recession look really bad:

Notice that a) the vertical axis lists the number of jobs lost and b) it only includes the last three recessions.  Shortly afterward, Barry Ritholtz posted a graph that still had the total number of jobs lost on the vertical axis, but now included all post-World War Two recessions:

Including all the recessions is an improvement if only for the sake of context, but displaying total job losses paints a false picture for several reasons:

  1. Most importantly, it doesn’t allow for increases in the population.  The US residential population in 1974 was 213 million, while today it is around 306 million.  A loss of 500 thousand jobs in 1974 was therefore a much worse event than it is today.
  2. Until the 1980s, most households only had one source of labour income.  Although the process started slowly much earlier, in the 1980s very large numbers of women began to enter the workforce, meaning that households became more likely to have two sources of labour income.  As a result, one person in a household losing their job is not as catastrophic today as it used to be.
  3. There has also been a general shift away from full-time work and towards part-time work.  Only looking at the number of people employed (or, in this case, fired) means that we miss altogether the impact of people having their hours reduced.
  4. We should also attempt to take into account discouraged workers; i.e. those who were unemployed and give up even looking for a job.

Several people then allowed for the first of those problems by giving graphs of job loses as percentages of the employment level at the peak of economic activity before the recession.  Graphs were produced, at the least, by Justin Fox, William Polley and Calculated Risk.  All of those look quite similar.  Here is Polley’s:

The current recession is shown in orange.  Notice the dramatic difference to the previous two graphs?  The current recession is now shown as being quite typical; painful and worse than the last two recessions, but entirely normal.  However, this graph is still not quite right because it still fails to take into account the other three problems I listed above.

(This is where my own efforts come in)

The obvious way to deal with the rise of part-time work is to graph (changes in) hours worked rather than employment.

The best way to also deal with the entry of women into the workforce is to graph hours worked per member of the workforce or per capita.

The only real way to also (if imperfectly) account for discouraged workers is to just graph hours worked per capita (i.e. to compare it to the population as a whole).

This first graph shows Weekly Hours Worked per capita and per workforce member since January 1964:

In January 1964, the average member of the workforce worked just over 21 hours per week.  In January 2009 they worked just under 20 hours per week.

The convergence between the two lines represents the entry of women into the workforce (the red line is increasing) and the increasing prevalence of part-time work (the blue line is decreasing).  Each of these represented a structural change in the composition of the labour force.  The two processes appear to have petered out by 1989. Since 1989 the two graphs have moved in tandem.

[As a side note: In econometrics it is quite common to look for a structural break in some timeseries data.  I'm sure it exists, but I am yet to come across a way to rigorously handle the situation when the "break" takes decades occur.]

The next graph shows Year-over-Year percentage changes in the number of employed workers, the weekly hours per capita and the weekly hours per workforce member:

Note that changes in the number of workers are consistently higher than the number of hours per workforce member or per capita.  In a recession, people are not just laid off, but the hours that the remaining employees are given also falls, so the average number of hours worked falls much faster.  In a boom, total employment rises faster than the average number of hours, meaning that the new workers are working few hours than the existing employees.

This implies that the employment situation faced by the average individual is consistently worse than we might think if we restrict our attention to just the number of people in any kind of employment.  In particular, it means that from the point of view of the average worker, recessions start earlier, are deeper and last longer than they do for the economy as a whole.

Here is the comparison of recessions since 1964 from the point of view of Weekly Hours Worked per capita, with figures relative to those in the month the NBER determines to be the peak of economic activity:

The labels for each line are the official (NBER-determined) start and end dates for the recession.  There are several points to note in comparing this graph to those above:

  • The magnitudes of the declines are considerably worse than when simply looking at aggregate employment.
  • Declines in weekly hours worked per capita frequently start well before the NBER-determined peak in economic activity.  For the 2001 recession, the decline started 11 months before the official peak.
  • For two recessions out of the last seven – those in 1980 and 2001 – the recovery never fully happened; another recession was deemed to have started before the weekly hours worked climbed back to its previous peak.
  • The 2001 recession was really awful.
  • The current recession would appear to still be typical.

Since so many of the recessions started – from the point of view of the average worker – before the NBER-determined date, it is helpful to rebase that graph against the actual peak in weekly hours per capita:

Now, finally, we have what I believe is an accurate comparison of the employment situation in previous recessions.

Once again, the labels for each line are the official (NBER-determined) start and end dates for the recession.  By this graph, the 2001 recession is a clear stand-out.  It fell the second furthest (and almost the furthest), lasted by far the longest and the recovery never fully happened.

The current recession also stands out as being toward the bad end of the spectrum.  It is the equally worst recession by this point since the peak.  It will need to continue getting a lot worse quite quickly in order to maintain that record, however.

After seeing Calculated Risk’s graph, Barry Ritholtz asked whether it is taking longer over time to recover from a recession recoveries (at least in employment).  This graph quite clearly suggests that the answer is “no.”  While the 2001 and 1990/91 recessions do have the slowest recoveries, the next two longest are the earliest.

Perhaps a better way to characterise it is to compare the slope coming down against the slope coming back up again.  It seems as a rough guess that rapid contractions are followed by just-as-rapid rises.  On that basis, at least, we have some slight cause for optimism.

If anybody is interested, I have also uploaded a copy of the spreadsheet with all the raw data for these graphs.  You can access it here:  US Employment (excel spreadsheet)

For reference, the closest other things that I have seen to this presentation in the blogosphere are this post by Spencer at Angry Bear and this entry by Menzie Chinn at EconBrowser.  He provides this graph of employment versus aggregate hours for the current recession only:

Alex Tabarrok has also been comparing recessions (1, 2, 3).

The velocity of money and the credit crisis

Published on 2 February 2009 in Economics. Tags: , , , , . 1 Comment

This is another one for my students of EC102.

Possibly the simplest model of aggregate demand in an economy is this equation:

MV = PY

The right-hand side is the nominal value of demand, being the price level multiplied by the real level of demand.  The left-hand side has the stock of money multiplied by the velocity of money, which is the number of times the average dollar (or pound, or euro) goes around the economy in a given time span.  The equation isn’t anything profound.  It’s an accounting identity that is always true, because V is constructed in order to make it hold.

The Quantity Theory of Money (QTM) builds on that equation.  The QTM assumes that V and Y are constant (or at least don’t respond to changes in M) and observes that, therefore, any change in M must only cause a corresponding change in P.  That is, an increase in the money supply will only result in inflation.

A corresponding idea is that of Money Neutrality.  If money is neutral, then changes in the money supply do not have any effect on real variables.  In this case, that means that a change in M does not cause a change in Y.  In other words, the neutrality of money is a necessary, but not sufficient condition for the QTM to hold; you also need the velocity of money to not vary with the money supply.

After years of research and arguing, economists generally agree today that money neutrality does not hold in the short run (i.e. in the short run, increasing the money supply does increase aggregate demand), but that it probably does hold in the long run (i.e. any such change in aggregate demand will only be temporary).

The velocity of money is an interesting concept, but it’s fiendishly difficult to tie down.

  • In the long-run, it has a secular upward trend (which is why the QTM doesn’t hold in the long run, even if money neutrality does).
  • It is extremely volatile in the short-run.
  • Since it is constructed rather than measured, it is a residual in the same way that Total Factor Productivity is a residual.  It is therefore a holding place for any measurement error in the other three variables.  This will be part, if not a large part, of the reason why it is so volatile in the short-run.
  • Nevertheless, the long run increases are pretty clearly real (i.e. not a statistical anomaly). We assume that this a result of improvements in technology.
  • Conceptually, a large value for V is representative of an efficient financial sector. More accurately, a large V is contingent on an efficient turn-around of money by the financial sector – if a new deposit doesn’t go out to a new loan very quickly, the velocity of money is low. The technology improvements I mentioned in the previous point are thus technologies specific to improving the efficiency of the finance industry.
  • As you might imagine, the velocity of money is also critically dependent on confidence both within and regarding banks.
  • Finally, the velocity of money is also related to the concept of fractional reserve banking, since we’re talking about how much money gets passed on via the banks for any given deposit.  In essence, the velocity of money must be positively related to the money multiplier.

Those last few points then feed us into the credit crisis and the recession we’re all now suffering through.

It’s fairly common for some people to blame the crisis on a global savings glut, especially after Ben Bernanke himself mentioned it back in 2005.  But, as Brad Setser says, “the debtor and the creditor tend to share responsibility for most financial crises. One borrows too much, the other lends too much.”

So while large savings in East-Asian and oil-producing countries may have been a push, we can use the idea of the velocity of money to think about the pull:

  1. There was some genuine innovation in the financial sector, which would have increased V even without any change in attitudes.
  2. Partially in response to that innovation, partially because of a belief that thanks to enlightened monetary policy aggregate uncertainty was reduced and, I believe, partially buoyed by the broader sense of victory of capitalism over communism following the fall of the Soviet Union, confidence both within and regarding the financial industry also rose.
  3. Both of those served to increase the velocity of money and, with it, real aggregate demand even in the absence of any especially loose monetary policy.
  4. Unfortunately, that increase in confidence was excessive, meaning that the increases in demand were excessive.
  5. Now, confidence both within and, in particular, regarding the banking sector has collapsed.  The result is a fall in the velocity of money (for any given deposit received, a bank is less likely to make a loan) and consequently, aggregate demand suffers.

How to value toxic assets (part 4)

Published on 29 January 2009 in Economics, Epistemology, Finance, UK and USA. Tags: , , , , , , , , . 0 Comments

Okay.  First, a correction:  There is (of course) a market for CDOs and other such derivatives at the moment.  You can sell them if you want.  It’s just that the prices that buyers are willing to pay is below what the holders of CDOs are willing to accept.

So, here are a few thoughts on estimating the underlying, or “fair,” value of a CDO:

Method 1. Standard asset pricing considers an asset’s value to be the sum of the present discounted value of all future income that it generates.  We discount future income because:

  • Inflation will mean that the money will be worth less in the future, so in terms of purchasing power, we should discount it when thinking of it in today’s terms.
  • Even if there were no inflation, if we got the money today we could invest it elsewhere, so we need to discount future income to allow for the (lost) opportunity cost if current investment options generate a higher return than what the asset is giving us.
  • Even if there were no inflation and no opportunity cost, there is a risk that we won’t receive the future money.  This is the big one when it comes to valuing CDOs and the like.
  • Even if there’s no inflation, no opportunity cost and no risk of not being paid, a positive pure rate of time preference means that we’d still prefer to get our money today.

The discounting due to the risk of non-payment is difficult to quantify because of the opacity of CDOs.  The holders of CDOs don’t know exactly which mortgages are at the base of their particular derivative structure and even if they did, they don’t know the household income of each of those borrowers.  Originally, they simply trusted the ratings agencies, believing that something labeled “AAA” would miss payment with probability p%, something “AA” with probability q% and so on.  Now that the ratings handed out have been shown to be so wildly inappropriate, investors in CDOs are being forced to come up with new numbers.  This is where Knightian Uncertainty is coming into effect:  Since even the risk is uncertain, we are in the Rumsfeldian realm of unknown unknowns.

Of course we do know some things about the risk of non-payment.  It obviously rises as the amount of equity a homeowner has falls and rises especially quickly when they are underwater (a.k.a. have negative equity (a.k.a. they owe more than the property is worth)).  It also obviously rises if there have been a lot of people laid off from their jobs recently (remember that the owner of a CDO can’t see exactly who lies at the base of the structure, so they need to think about the probability that whoever it is just lost their job).

The first of those is the point behind this idea from Chris Carroll out of NYU:  perhaps the US Fed should simply offer insurance against falls in US house prices.

The second of those will be partially addressed in the future by this policy change announced recently by the Federal Housing Finance Agency:

[E]ffective with mortgage applications taken on or after Jan. 1, 2010, Freddie Mac and Fannie Mae are required to obtain loan-level identifiers for the loan originator, loan origination company, field appraiser and supervisory appraiser … With enactment of the S.A.F.E. Mortgage Licensing Act, identifiers will now be available for each individual loan originator.

“This represents a major industry change. Requiring identifiers allows the Enterprises to identify loan originators and appraisers at the loan-level, and to monitor performance and trends of their loans,” said Lockhart [, director of the FHFA].

It’s only for things bought by Fannie and Freddie and it’s only for future loans, but hopefully this will help eventually.

Method 2. The value of different assets will often necessarily covary.  As a absurdly simple example, the values of the AAA-rated and A-rated tranches of a CDO offering must provide upper and lower bounds on the value of the corresponding AA-rated tranche.  Statistical estimation techniques might therefore be used to infer an asset’s value.  This is the work of quantitative analysts, or ”quants.”

Of course, this sort of analysis will suffer as the quality of the inputs falls, so if some CDOs have been valued by looking at other CDOs and none of them are currently trading (or the prices of those trades are different to the true values), then the value of this analysis correspondingly falls.

Method 3. Borrowing from Michael Pomerleano’s comment in rely to Christopher Carroll’s piece, one extreme method of valuing CDOs is to ask at what price a distressed debt (a.k.a. vulture) fund would be willing to buy them at with the intention of merging all the CDOs and other MBSs for a given mortgage pool so that they could then renegotiate the debt with the underlying borrowers (the people who took out the mortgages in the first place).  This is, in essense, a job of putting Humpty Dumpty back together again.  Gathering all the CDOs and other MBSs for a given pool of mortgage assets will take time.  Identifying precisely those mortgage assets will also take time.  There will be sizable legal costs.  Some holders of the lower-rated CDOs may also refuse to sell if they realise what’s happening, hoping to draw out some rent extraction from the fund.  The price that the vulture fund would offer on even the “highly” rated CDOs would therefore be very low in order to ensure that they made a profit.

It would appear that banks and other holders of CDOs and the like are using some combination of methods one and two to value their assets, while the bid-prices being offered by buyers are being set by the logic of something like method three.  Presumably then, if we knew the banks’ private valuations, we might regard the difference between them and the market prices as the value of the uncertainty.

Other posts in this series:  1, 2, 3, [4], 5, 6.

How to value toxic assets (part 3)

Published on 21 January 2009 in Economics and Finance. Tags: , , , , , . 0 Comments

Continuing on from my previous thoughts (1, 2, 3, 4) …

In the world of accounting, the relevant phrase here is “fair value.”  In the United States (which presently uses a different set of accounting requirements to the rest of the world, although that is changing), assets are classified as being in one of three levels (I’m largely reproducing the Wikipedia article here):

Level one assets are those traded in liquid markets with quoted prices.  Fair value (in a mark-to-market sense) is taken to be the current price.

Level two and level three assets are not traded in liquid markets with quoted prices, so their fair values need to be estimated via a statistical model.

Level two assets are those whose fair value is able to be estimated by looking at publicly-available market information.  As a contrived example, maybe there is currently no market for a particular AA-rated tranche of CDOs, but there are recent prices for the corresponding AAA-rated and A-rated tranches, so the AA-rated stuff should be valued somewhere in between those two.

Level three assets are those whose fair value can only be estimated by appealing to information that is not publicly observable.

These are listed in the U.S. Financial Accounting Standards Board (FASB) Statement 157.  In October of last year, the FASB issued some clarification/guidance on valuing derivatives like CDOs when the market for them had dried up.

Brad DeLong, in early December last year, was given a list of reasons from Steve Ross why we might not want to always mark-to-market (i.e. assume that the fair value is the currently available market price):

  • If you believe in organizational capital–in goodwill–in the value of the enterprise’s skills, knowledge, and relationships as a source of future cash flows–than marking it to market as if that organizational capital had no value is the wrong thing to do.
    • Especially as times in which asset values are disturbed and impaired are likely to be times when the value of that organizational capital is highest.
  • If you believe in mean reversion in risk-adjusted asset values, mark-to-market accounting is the wrong thing to do.
  • If you believe that transaction prices differ from risk-adjusted asset values–perhaps because transaction prices are of particular assets that are or are feared to be adversely selected and hence are not representative of the asset class–than mark-to-market accounting is the wrong thing to do.
  • If you believe that changes in risk-adjusted asset values are unpredictable, but also believe:
    • in time-varying required expected returns do to changing risk premia;
    • that an entity’s own cost of capital does not necessarily move one-for-one with the market’s time-varying risk premia;
    • then mark-to-market accounting is the wrong thing to do.

Other posts in this series:  1, 2, [3], 4, 5, 6.