Tag Archives: asset bubbles

anatomy of an asset bubble, explosive processes, noncausal autoregressive models

An Update on Bitcoin

Fairly hum-drum days of articles on testing for unit roots in time series led to discovery of an extraordinary new forecasting approach – using the future to predict the present.

Since virtually the only empirical application of the new technique is predicting bubbles in Bitcoin values, I include some of the recent news about Bitcoins at the end of the post.

Noncausal Autoregressive Models

I think you have to describe the forecasting approach recently considered by Lanne and Saikkonen, as well as Hencic, Gouriéroux and others, as “exciting,” even “sexy” in a Saturday Night Live sort of way.

Here is a brief description from a 2015 article in the Econometrics of Risk called Noncausal Autoregressive Model in Application to Bitcoin/USD Exchange Rates

noncausal

I’ve always been a little behind the curve on lag operators, but basically Ψ(L-1) is a function of the standard lagged operators, while Φ(L) is a second function of offsets to future time periods.

To give an example, consider,

yt = k1yt-1+s1yt+1 + et

where subscripts t indicate time period.

In other words, the current value of the variable y is related to its immediately past value, and also to its future value, with an error term e being included.

This is what I mean by the future being used to predict the present.

Ordinarily in forecasting, one would consider such models rather fruitless. After all, you are trying to forecast y for period t+1, so how can you include this variable in the drivers for the forecasting setup?

But the surprising thing is that it is possible to estimate a relationship like this on historic data, and then take the estimated parameters and develop simulations which lead to predictions at the event horizon, of, say, the next period’s value of y.

This is explained in the paragraph following the one cited above –

noncausal2

In other words, because et in equation (1) can have infinite variance, it is definitely not normally distributed, or distributed according to a Gaussian probability distribution.

This is fascinating, since many financial time series are associated with nonGaussian error generating processes – distributions with fat tails that often are platykurtotic.

I recommend the Hencic and Gouriéroux article as a good read, as well as interesting analytics.

The authors proposed that a stationary time series is overlaid by explosive speculative periods, and that something can be abstracted in common from the structure of these speculative excesses.

Mt. Gox, of course, mentioned in this article, was raided in 2013 by Japanese authorities, after losses of more than $465 million from Bitcoin holders.

Now, two years later, the financial industry is showing increasing interest in the underlying Bitcoin technology and Bitcoin prices are on the rise once again.

bitcoin

Anyway, the bottom line is that I really, really like a forecast methodology based on recognition that data come from nonGaussian processes, and am intrigued by the fact that the ability to forecast with noncausal AR models depends on the error process being nonGaussian.

forecasting asset bubbles, stock market forecasts, vector autoregressions

Rational Bubbles

A rational bubble exists when investors are willing to pay more for stocks than is justified by the discounted stream of future dividends. If investors evaluate potential gains from increases in the stock price as justifying movement away from the “fundamental value” of the stock – a self-reinforcing process of price increases can take hold, but be consistent with “rational expectations.”

This concept has been around for a while, and can be traced to eminences such as Oliver Blanchard, now Chief Economist for the International Monetary Fund (IMF), and Mark Watson, Professor of Economics at Princeton University – (See Bubbles, Rational Expectations and Financial Markets).

In terms of formal theory, Diba and Grossman offer a sophisticated analysis in The Theory of Rational Bubbles in Stock Prices. They invoke intriguing language such as “explosive conditional expectations” and the like.

Since these papers from the 1980’s, the relative size of the financial sector has ballooned, and valuation of derivatives now dwarfs totals for annual value of production on the planet (See Bank for International Settlements).

And, in the US, we have witnessed two, dramatic stock market bubbles, here using the phrase in a more popular “plain-as-the-hand-in-front-of-your-face” sense.

SP500

Following through the metaphor, bursting of the bubble leaves financial plans in shambles, and, from the evidence of parts of Europe at least, can cause significant immiseration of large segments of the population.

It would seem reasonable, therefore, to institute some types of controls, as a bubble was emerging. Perhaps an increase in financial transactions taxes, or some other tactic to cause investors to hold stocks for longer periods.

The question, then, is whether it is possible to “test” for a rational bubble pre-emptively or before-the-fact.

So I have been interested recently to come on more recent analysis of so-called rational bubbles, applying advanced statistical techniques.

I offer some notes and links to download the relevant papers.

These include ground-breaking work by Craine in a working paper Rational Bubbles: A Test.

Then, there are two studies focusing on US stock prices (I include extracts below in italics):

Testing for a Rational Bubble Under Long Memory

We analyze the time series properties of the S&P500 dividend-price ratio in the light of long memory, structural breaks and rational bubbles. We find an increase in the long memory parameter in the early 1990s by applying a recently proposed test by Sibbertsen and Kruse (2009). An application of the unit root test against long memory by Demetrescu et al. (2008) suggests that the pre-break data can be characterized by long memory, while the post-break sample contains a unit root. These results reconcile two empirical findings which were seen as contradictory so far: on the one hand they confirm the existence of fractional integration in the S&P500 log dividend-price ratio and on the other hand they are consistent with the existence of a rational bubble. The result of a changing memory parameter in the dividend-price ratio has an important implication for the literature on return predictability: the shift from a stationary dividend-price ratio to a unit root process in 1991 is likely to have caused the well-documented failure of conventional return prediction models since the 1990s. 

The bubble component captures the part of the share price that is due to expected future price changes. Thus, the price contains a rational bubble, if investors are ready to pay more for the share, than they know is justified by the discounted stream of future dividends. Since they expect to be able to sell the share even at a higher price, the current price, although exceeding the fundamental value, is an equilibrium price. The model therefore allows the development of a rational bubble, in the sense that a bubble is fully consistent with rational expectations. In the rational bubble model, investors are fully cognizant of the fundamental value, but nevertheless they may be willing to pay more than this amount… This is the case if expectations of future price appreciation are large enough to satisfy the rational investor’s required rate of return. To sustain a rational bubble, the stock price must grow faster than dividends (or cash flows) in perpetuity and therefore a rational bubble implies a lack of cointegration between the stock price and fundamentals, i.e. dividends, see Craine (1993).

Testing for rational bubbles in a co-explosive vector autoregression

We derive the parameter restrictions that a standard equity market model implies for a bivariate vector autoregression for stock prices and dividends, and we show how to test these restrictions using likelihood ratio tests. The restrictions, which imply that stock returns are unpredictable, are derived both for a model without bubbles and for a model with a rational bubble. In both cases we show how the restrictions can be tested through standard chi-squared inference. The analysis for the no-bubble case is done within the traditional Johansen model for I(1) variables, while the bubble model is analysed using a co-explosive framework. The methodology is illustrated using US stock prices and dividends for the period 1872-2000.

The characterizing feature of a rational bubble is that it is explosive, i.e. it generates an explosive root in the autoregressive representation for prices. 

This is a very interesting analysis, but involves several stages of statistical testing, all of which is somewhat dependent on assumptions regarding underlying distributions.

Finally, it is interesting to see some of these methodologies for identifying rational bubbles applied to other markets, such as housing, where “fundamental value” has a somewhat different and more tangible meaning.

Explosive bubbles in house prices? Evidence from the OECD countries

We conduct an econometric analysis of bubbles in housing markets in the OECD area, using quarterly OECD data for 18 countries from 1970 to 2013. We pay special attention to the explosive nature of bubbles and use econometric methods that explicitly allow for explosiveness. First, we apply the univariate right-tailed unit root test procedure of Phillips et al. (2012) on the individual countries price-rent ratio. Next, we use Engsted and Nielsen’s (2012) co-explosive VAR framework to test for bubbles. Wefind evidence of explosiveness in many housing markets, thus supporting the bubble hypothesis. However, we also find interesting differences in the conclusions across the two test procedures. We attribute these differences to how the two test procedures control for cointegration between house prices and rent.

anatomy of an asset bubble, causal discovery, Federal Reserve policies and impacts, Medical data analytics, power laws

2014 in Review – I

I’ve been going over past posts, projecting forward my coming topics. I thought I would share some of the best and some of the topics I want to develop.

Recommendations From Early in 2014

I would recommend Forecasting in Data-Limited Situations – A New Day. There, I illustrate the power of bagging to “bring up” the influence of weakly significant predictors with a regression example. This is fairly profound. Weakly significant predictors need not be weak predictors in an absolute sense, providing you can bag the sample to hone in on their values.

There also are several posts on asset bubbles.

Asset Bubbles contains an intriguing chart which proposes a way to “standardize” asset bubbles, highlighting their different phases.

BubbleAnatomy

The data are from the Hong Kong Hang Seng Index, oil prices to refiners (combined), and the NASDAQ 100 Index. I arrange the series so their peak prices – the peak of the bubble – coincide, despite the fact that the peaks occurred at different times (October 2007, August 2008, March 2000, respectively). Including approximately 5 years of prior values of each time series, and scaling the vertical dimensions so the peaks equal 100 percent, suggesting three distinct phases. These might be called the ramp-up, faster-than-exponential growth, and faster-than-exponential decline. Clearly, I am influenced by Didier Sornette in choice of these names.

I’ve also posted several times on climate change, but I think, hands down, the most amazing single item is this clip from “Chasing Ice” showing calving of a Greenland glacier with shards of ice three times taller than the skyscrapers in Lower Manhattan.

See also Possibilities for Abrupt Climate Change.

I’ve been told that Forecasting and Data Analysis – Principal Component Regression is a helpful introduction. Principal component regression is one of the several ways one can approach the problem of “many predictors.”

In terms of slide presentations, the Business Insider presentation on the “Digital Future” is outstanding, commented on in The Future of Digital – I.

Threads I Want to Build On

There are threads from early in the year I want to follow up in Crime Prediction. Just how are these systems continuing to perform?

Another topic I want to build on is in Using Math to Cure Cancer. I’d like to find a sensitive discussion of how MD’s respond to predictive analytics sometime. It seems to me that US physicians are sometimes way behind the curve on what could be possible, if we could merge medical databases and bring some machine learning to bear on diagnosis and treatment.

I am intrigued by the issues in Causal Discovery. You can get the idea from this chart. Here, B → A but A does not cause B – Why?

casualpic

I tried to write an informed post on power laws. The holy grail here is, as Xavier Gabaix says, robust, detail-independent economic laws.

Federal Reserve Policies

Federal Reserve policies are of vital importance to business forecasting. In the past two or three years, I’ve come to understand the Federal Reserve Balance sheet better, available from Treasury Department reports. What stands out is this chart, which anyone surfing finance articles on the net has seen time and again.

FedMBandQEgraph

This shows the total of the “monetary base” dating from the beginning of 2006. The red shaded areas of the graph indicate the time windows in which the various “Quantitative Easing” (QE) policies have been in effect – now three QE’s, QE1, QE2, and QE3.

Obviously, something is going on.

I had fun with this chart in a post called Rhino and Tapers in the Room – Janet Yellen’s Menagerie.

OK, folks, for this intermission, you might want to take a look at Malcolm Gladwell on the 10,000 Hour Rule


So what happens if you immerse yourself in all aspects of the forecasting field?

Coming – how posts in Business Forecast Blog pretty much establish that rational expectations is a concept way past its sell date.

Guy contemplating with wine at top from dreamstime.

 

asset bubbles, cointegration in time series, forecasting research

Forecasting the Downswing in Markets – II

Because the Great Recession of 2008-2009 was closely tied with asset bubbles in the US and other housing markets, I have a category for asset bubbles in this blog.

In researching the housing and other asset bubbles, I have been surprised to discover that there are economists who deny their existence.

By one definition, an asset bubble is a movement of prices in a market away from fundamental values in a sustained manner. While there are precedents for suggesting that bubbles can form in the context of rational expectations (for example, Blanchard’s widely quoted 1982 paper), it seems more reasonable to consider that “noise” investors who are less than perfectly informed are part of the picture. Thus, there is an interesting study of the presence and importance of “out-of-town” investors in the recent run-up of US residential real estate prices which peaked in 2008.

The “deviations from fundamentals” approach in econometrics often translates to attempts to develop or show breaks in cointegrating relationships, between, for example, rental rates and housing prices. Let me just say right off that the problem with this is that the whole subject of cointegration of nonstationary time series is fraught with statistical pitfalls – such as weak tests to reject unit roots. To hang everything on whether or not Granger causation can or cannot be shown is to really be subject to the whims of random influences in the data, as well as violations of distributional assumptions on the relevant error terms.

I am sorry if all that sounds kind of wonkish, but it really needs to be said.

Institutionalist approaches seem more promising – such as a recent white paper arguing that the housing bubble and bust was the result of a ..

supply-side phenomenon, attributable to an excess of mispriced mortgage finance: mortgage-finance spreads declined and volume increased, even as risk increased—a confluence attributable only to an oversupply of mortgage finance.

But what about forecasting the trajectory of prices, both up and then down, in an asset bubble?

What can we make out of charts such as this, in a recent paper by Sornette and Cauwels?

negativebubble

Sornett and the many researchers collaborating with him over the years are working with a paradigm of an asset bubble as a faster than exponential increase in prices. In an as yet futile effort to extend the olive branch to traditional economists (Sornette is a geophysicist by training), Sornette evokes the “bubbles following from rational expectations meme.” The idea is that it could be rational for an investor to participate in a market that is in the throes of an asset bubble, providing that the investor believes that his gains in the near future adequately compensate for the increased risk of a collapse of prices. This is the “greater fool” theory to a large extent, and I always take delight in pointing out that one of the most intelligent of all human beings – Isaac Newton – was burned by exactly such a situation hundreds of years ago.

In any case, the mathematics of the Sornette et al approach are organized around the log-periodic power law, expressed in the following equation with the Sornette and Cauwels commentary (click to enlarge).

LPPL

From a big picture standpoint, the first thing to observe is that there is a parameter tc in the equation which is the “critical time.”

The whole point of this mathematical apparatus, which derives in part from differential equations and some basic modeling approaches common in physics, is that faster than exponential growth is destined to reach a point at which it basically goes ballistic. That is the critical point. The purpose of forecasting in this context then is to predict when this will be, when will the asset bubble reach its maximum price and then collapse?

And the Sornette framework allows for negative as well as positive price movements according to the dynamics in this equation. So, it is possible, if we can implement this, to predict how far the market will fall after the bubble pops, so to speak, and when it will turn around.

Pretty heady stuff.

The second big picture feature is to note the number of parameters to be estimated in fitting this model to real price data – minimally constants A, B, and C, an exponent m, the angular frequency ω and phase φ, plus the critical time.

For the mathematically inclined, there is a thread of criticism and response, more or less culminating in Clarifications to questions and criticisms on the Johansen–Ledoit–Sornette financial bubble model which used to be available as a PDF download from ETC Zurich.

In brief, the issue is whether the numerical analysis methods fitting the data to the LPPL model arrive at local, instead of global maxima. Obviously, different values for the parameters can lead to wholly different forecasts for the critical time tc.

To some extent, this issue can be dealt with by running a great number of estimations of the parameters, or by developing collateral metrics for adequacy of the estimates.

But the bottom line is – regardless of the extensive applications of this approach to all manner of asset bubbles internationally and in different markets – the estimation of the parameters seems more in the realm of art, than science, at the present time.

However, it may be that mathematical or computational breakthroughs are possible.

I feel these researchers are “very close.”

In any case, it would be great if there were a package in R or the like to gin up these estimates of the critical time, applying the log-periodic power law.

Then we could figure out “how low it can go.’

And, a final note to this post – it is ironic that as I write and post this, the stock markets have recovered from their recent swoon and are setting new records. So I guess I just want to be prepared, and am not willing to believe the runup can go on forever.

I’m also interested in methodologies that can keep forecasters usefully at work, during the downswing.

Big Data, gasoline price forecasts, geopolitical risk, predictive analytics

When the Going Gets Tough, the Tough Get Going

Great phrase, but what does it mean? Well, maybe it has something to do with the fact that a lot of economic and political news seem to be entering kind of “end game.” But, it’s now the “lazy days of summer,” and there is a temptation to sit back and just watch it whiz by.

What are the options?

One is to go more analytical. I’ve recently updated my knowledge base on some esoteric topics –mathematically and analytically interesting – such as kernel ridge regression and dynamic principal components. I’ve previously mentioned these, and there are more instances of analysis to consider. What about them? Are they worth the enormous complexity and computational detail?

Another is to embrace the humming, buzzing confusion and consider “geopolitical risk.” The theme might be the price of oil and impacts, perhaps, of continuing and higher oil prices.

Or the proliferation of open warfare.

Rarely in recent decades have we seen outright armed conflict in Europe, as appears to be on-going in the Ukraine.

And I cannot make much sense of developments in the Mid-East, with some shadowy group called Isis scooping up vast amounts of battlefield armaments abandoned by collapsing Iraqi units.

Or how to understand Israeli bombardment of UN schools in Gaza, and continuing attacks on Israel with drones by Hamas. What is the extent and impact of increasing geopolitical risk?

There also is the issue of plague – most immediately ebola in Africa. A few days ago, I spent the better part of a day in the Boston Airport, and, to pass the time, read the latest Dan Brown book about a diabolical scheme to release an aerosol epidemic of sorts. In any case, ebola is in a way a token of a range of threats that stand just outside the likely. For example, there is the problem of the evolution of immune strains of bacteria, with widespread prescription and use.

There also is the ever-bloating financial bubble that has emerged in the US and elsewhere, as a result of various tactics of central and other banks in reaction to the Great Recession, and behavior of investors.

Finally, there are longer range scientific and technological possibilities. From my standpoint, we are making a hash of things generally. But efforts at political reform, by themselves, usually fall short, unless paralleled by fundamental new possibilities in production or human organization. And the promise of radical innovation for the betterment of things has never seemed brighter.

I will be exploring some of these topics and options in coming posts this week and in coming weeks.

And I think by now I have discovered a personal truth through writing – one that resonates with other experiences of mine professionally and personally. And that is sometimes it is just when the way to going further seems hard to make out that concentration of thought and energies may lead to new insight.

asset bubbles, Chinese economy, developed country forecast, developing country forecast, emerging market forecasts, Fed tapering, geopolitical risk, global business forecasts

The Next Recession – Will It Be A Global Meltdown?

One my focuses is the global economy and any cracks in the firmament which might presage the next recession. I rely a lot on my Twitter account to keep me on the crest of the wave, in this regard.

I’m really concerned, as are many of my colleagues and contacts in business and government.

We’ve hardly escaped the effects of last recession 2008-2009. Those are US dates, of course, set by the National Bureau of Economic Research (NBER) the official recession “dater” in this country.

There have been a series of rolling impacts and consequences of this so-called “Great Recession.”

Europe

Housing or real estate bubbles were present in Europe, too, particularly in Spain and Ireland. Then, there was the problem of the Greek economy and state, which did not support the level of public debt that had been garnered by, in some cases, corrupt public officials. And European problems were complicated by the currency union of the euro in a context where there is not, as yet, a centralized EU state. Anyway, not to reprise the whole matter blow-by-blow, but most of Europe, with the exception of Germany, plunged into recession and struggled with austerity policies that made things worse for Main Street or, as they like to say in Britain, “High Street.”

Many European countries are just now coming out of recession, and overall, the growth rate in the EU area is almost indistinguishable from zero.

So another recession in the next one to two years would really set them back.

China

Part of the problem China has been experiencing is related to the persisting downturn in most of Europe, since Europe is a big trading partner. And so, for that matter is the United States, which bought less from China during the recession years.

But another problem is that China now is experiencing a mojo big property bubble of its own.

Newly wealthy Chinese do not really have any place to put their money, except real estate. The Chinese, like the Japanese, are big savers, and for many middle class families, buying the second apartment or even a house is an investment for the future. Yet Chinese real estate prices have skyrocketed, leaving the average Chinese wage earner in the dust, with less and less hope of ever owning a residence.

Apparently, in connection with this real estate speculation, a large shadow banking system has emerged. Some estimates circulate on Twitter suggesting this rivals the size of the official Chinese banking system.

Can “market socialism” or “market Leninism” experience a financial crisis, based on too many debts that cannot be paid?

I’ve been to China a few times, and done some business there – all the while trying to understand how things are set up. My feeling is that one should not impute banking practices that seem pro forma in, say, Great Britain or the US, to the Chinese. I think they are much more ready to “break the rules” in order to keep the party going (which is sort of a pun).

Having said that, I do think a Chinese crisis could develop if property values collapse, as they are wont to do in bubble mode.

Again, it’s hard to say how this might play out, since the victims and suffering would be among the nouveau riche of China, of whom there are millions, and many more average families who have invested their nest egg in a hot property.

But I can’t think that collapse of real estate values in modern China would not have worldwide repurcussions.

The Rest of the World

Regrettably, I cannot go through other major regions, one-by-one, but I’d have to say that things are not so good. The BRIC’s as a group all have more problems than a few years back, when they were hailed as the bright new centers of economic growth by that Goldman Sachs analyst. That’s Brazil, Russia, India, and China, of course.

Possibilities of Increased Conflict

There is a kind of axiom of geopolitics and social interaction that when the pie is growing and everybody can get more, even though their slice may not have been very big to begin with, there is a tendency for people to make do, go about their business and so forth. Reverse this and you have the concept that shrinking the pie – as austerity policies and the Great Recession have done – tends to increase levels of conflict. At first, to the extent that people have the idea that “we are all in this together” there may be increased cooperation. But that is not the current situation in almost any society. Quite the contrary, as Piketty and the Occupy Movement highlight, there is growing awareness of inequality of wealth and income.

There are armed conflicts in Syria, the Ukraine, Afghanistan (resurgent Taliban), and areas and regions in Africa. The Indian elections recently installed a Hindu nationalist who hopefully will be a reformer, but may, if the going gets tough, revert or acquiesce to more conflict with Pakistan and with non-Hindu populations within India. Pakistan, one of the world’s nuclear powers, appears to be extremely unstable politically. There are deep civil divisions in Thailand between city and rural areas that parallel class divisions. China is flexing its muscles in the South China Sea.

And we may be moving from an era of US-centric global capitalism to a time when the Eurasian supercontinent will become significantly more important and perhaps decoupled from Wall Street and the City of London. Already, there are threats to dollar supremacy, and, historically, as US economic power is eclipsed by the more rapidly growing economies of Asia, some adjustment seems predictable.

In all this, Hollywood can be counted on to roll out some really corking new international intrigue films, perhaps (although I doubt it) with more complex plots.

The Situation with the US Federal Reserve Bank

The point of this international survey and reprise of recent business history is to highlight areas where surprises may originate, shaking the markets, and perhaps triggering the next recession.

But the most likely suspect is the US Federal Reserve Bank.

Two graphs speak volumes.

interestratesnew

Fedassets

Seeking to encourage economic recovery, the US Federal Reserve dropped the federal funds rate to a number effectively almost zero – a historically low number. This zero bound federal funds rate has persisted since the end of 2009, or for about five years.

The Fed also has engaged in new policies, whereby it goes into private bond markets and buys long term bonds – primarily mortgage-backed securities. The second chart tracks this inasmuch as a good portion of the more than 4 trillion in Fed assets (for which there are corresponding liabilities, of course) are these mortgage-backed securities. In effect, the Fed has purchased a sizeable portion of the US housing market – one might say “nationalize” except that would be forgetting the fact that the Fed is actually a private institution whose governance is appointed by the Executive Branch of the US government.

In any case, this bond-buying is the famous “quantitative easing” (QE) and is mirrored in the accumulation of excess reserves by the banking system. Generally, that is, banks and financial institutions issue mortgages, sell them among themselves to be packaged in mortgage-backed securities, and the Fed has been buying these.

Banks can easily loan these excess reserves, but they consistently have not. Why is an interesting question beyond the scope of this discussion, but the consequence is that the Fed’s actions are “firewalled” from increasing the rate of inflation, which is what ordinarily you might think would occur given that various metrics of money supply also have surged upward.

Now “Fed-watching” is its own little cottage industry among financial commentators, and I am not going to second-guess the media here. The Fed has announced a plan to “taper” these purchases of long term bonds. This is likely to increase the mortgage rates and, probably to some extent, based on expectations already has.

So, the long and the short of it are that this set of policies – zero federal funds rate and bond buying cannot go on forever.

If economic growth has been low-grade since 2010 with these low interest rates, what is the reasonable outlook for a higher interest rate regime?

Timing of the Next Recession

When is the most likely time for a recession, for example? Would it be later in 2014, in 2015, or thereafter, maybe in 2016.

Here is a table of all the recessions in the US since the middle 1850’s along with facts about their duration (source: NBER).

NBERRecess

Without even considering averages, the maximum period of trough to trough – that is, from the bottom of one recession to the bottom of the next – has been 128 months or ten years and eight months. Here, incidentally, the month numbers begin January 1800, for what that’s worth.

Thus, at the outside, based on these empirics, the trough of the next recession is likely to occur no later than early 2020.

Note that we have already blown through the average length from trough to trough of about 58.4 months or about five years from June 2009.

On a simple probabilistic basis, therefore, we are moving into the tail of the distribution of business cycle durations, suggesting that the chances of a downturn are in some sense already above 50 percent.

And note that the experience of the current business recovery is nothing like this historically maximum span in the 1990’s between the trough of the recession of 1990-1991 and the trough of November 2001.

This business recovery persistently seems to move ahead just above or, in the last quarter of 2013, below “stall speed.”

Seemingly, a fairly minor perturbation could set off a chain reaction, given the advanced frothiness in the stock market and softness in housing prices.

More of the Same, Worse

Neil Baroifsky was special inspector with oversight authority for the TARP during the bailout phase of the Great Recession, and currently is a partner in the Litigation Department of national law firm Jenner & Block LLP.

He’s also an author and often is called on for his opinion about developments in malfeasance writ large among the finance giants – such as the Credit Suisse settlement. In connection with a recent NPR interview, Barofsky said,

Although it is good that we averted a catastrophe back in 2008, the way that we did so I believe has unfortunately set the stage for an even more devastating financial crisis in the future.

HOBSON: In the future? How far?

BAROFSKY: Well, if I knew that, Michael Lewis would be writing his next book about people who made billions on timing the markets perfectly about me, which would be great.

(LAUGHTER)

BAROFSKY: But if you look, a lot of the same broken incentives from 2008 are still there. It’s just a question of when, not if. You can’t look at the fundamental broken incentives in the financial system and really come to a conclusion other than that we’re headed down the same dangerous path that we were that culminated in the explosion of ’08.

Barofsky’s point is readily supported by facts, such as –

The US and global financial system is even more concentrated today than in 2007, making “too big to fail”and even bigger potential problem now, than before the Great Recession. Even Alan Greenspan has taken note.

And the “pass the buck” system, whereby bond rating agencies are paid by the originators to evaluate exotic securities (“financial innovations”) created by the banking and shadow banking industries, securities which are then passed on to pension funds and hapless investors – this system appears to still be completely in place. Talk about the concept of “moral hazard.”

Global Impact

I think you get the picture.

For one reason or another, some fairly minor event is likely to set off a cascade of consequences in US and global financial markets, leading to the next recession. Probably, within one, two, or three years, as a matter of fact. Because the US Fed, and, for that matter, other central banks will still be working their way out of the last recession, there may be fewer “policy tools” to halt the stampede to sell, cutback, and so forth. Governments could respond with aggressive fiscal policy, but that option appears limited unless there are major changes in the political climate in the US and Europe.

Personally, I think wholly new directions of policy should be contemplated at the personal, local, regional, and of course at national levels.

We need to create what I have started to call “islands of stability.” This is the old idea of local self-reliance, but in new packaging. I really think there should be discussions widely across the US at least about how to decouple from the global economy and, indeed, from the financial concentrations on Wall Street. As a matter of self-preservation, until such time as more courageous national policies can be undertaken to reign in such obvious risks.

anatomy of an asset bubble, asset bubbles, predicting turning points, stock market forecasts

Daily Updates on Whether Key Financial Series Are Going Into Bubble Mode

Financial and asset bubbles are controversial, amazingly enough, in standard economics, where a bubble is defined as a divergence in a market from fundamental value. The problem, of course, is what is fundamental value. Maybe investors in the dot.com frenzy of the late 1990’s believed all the hype about never-ending and accelerating growth in IT, as a result of the Internet.

So we have this chart for the ETF SPY which tracks the S&P500. Now, there are similarities between the upswing of the two previous peaks – which both led to busts – and the current surge in the index.

sp500yahoo

Where is this going to end?

Well, I’ve followed the research of Didier Sornette and his co-researchers, and, of course, Sornette’s group has an answer to this question, which is “probably not well.” Currently, Professor Sornette occupies the Chair of Entreprenuerial Risk at the Swiss Federal Institute of Technology in Zurich.

There is an excellent website maintained by ETH Zurich for the theory and empirical analysis of financial bubbles.

Sornette and his group view bubbles from a more mathematical perspective, finding similarities in bubbles of durations from months to years in the concept of “faster than exponential growth.” At some point, that is, asset prices embark on this type of trajectory. Because of various feedback mechanisms in financial markets, as well as just herding behavior, asset prices in bubble mode oscillate around an accelerating trajectory which – at some point that Sornette claims can be identified mathematically – becomes unsupportable. At such a moment, there is a critical point where the probability of a collapse or reversal of the process becomes significantly greater.

This group is on the path of developing a new science of asset bubbles, if you will.

And, by this logic, there are positive and negative bubbles.

The sharp drop in stock prices in 2008, for example, represents a negative stock market bubble movement, and also is governed or described, by this theory, by an underlying differential equation. This differential equation leads to critical points, where the probability of reversal of the downward price movement is significantly greater.

I have decided I am going to compute the full price equation suggested by Sornette and others to see what prediction for a critical point emerges for the S&P 500 or SPY.

But actually, this would be for my own satisfaction, since Sornette’s group already is doing this in the Financial Crisis Observatory.

I hope I am not violating Swiss copyright rules by showing the following image of the current Financial Crisis Observatory page (click to enlarge)

FCO

As you notice there are World Markets, Commodities, US Sectors, US Large Cap categories and little red and green boxes scattered across the page, by date.

The red boxes indicate computations by the ETH Zurich group that indicate the financial series in question is going into bubble mode. This is meant as a probabilistic evaluation and is accompanied by metrics which indicate the likelihood of a critical point. These computations are revised daily, according to the site.

For example, there is a red box associated with the S&P 500 in late May. If you click on this red box, you  produces the following chart.

SornetteSP500

The implication is that the highest red spike in the chart at the end of December 2013 is associated with a reversal in the index, and also that one would be well-advised to watch for another similar spike coming up.

Negative bubbles, as I mention, also are in the lexicon. One of the green boxes for gold, for example, produces the following chart.

Goldnegbubble

This is fascinating stuff, and although Professor Sornette has gotten some media coverage over the years, even giving a TED talk recently, the economics profession generally seems to have given him almost no attention.

I plan a post on this approach with a worked example. It certainly is much more robust that some other officially sanctioned approaches.

accuracy of forecasts, asset bubbles, Big Data, data reduction, financial forecasting, forecasting turning points, multivariate modeling, principal component models and forecasts, real estate forecasts

Forecasting Housing Markets – 3

Maybe I jumped to conclusions yesterday. Maybe, in fact, a retrospective analysis of the collapse in US housing prices in the recent 2008-2010 recession has been accomplished – but by major metropolitan area.

The Yarui Li and David Leatham paper Forecasting Housing Prices: Dynamic Factor Model versus LBVAR Model focuses on out-of-sample forecasts for house price indices for 42 metropolitan areas. Forecast models are built with data from 1980:01 to 2007:12. These models – dynamic factor and Large-scale Bayesian Vector Autoregressive (LBVAR) models – are used to generate forecasts of the one- to twelve- months ahead price growth 2008:01 to 2010:12.

Judging from the graphics and other information, the dynamic factor model (DFM) produces impressive results.

For example, here are out-of-sample forecasts of the monthly growth of housing prices (click to enlarge).

DFMhousing

The house price indices for the 42 metropolitan areas are from the Office of Federal Housing Enterprise Oversight (OFEO). The data for macroeconomic indicators in the dynamic factor and VAR models are from the DRI/McGraw Hill Basic Economics Database provided by IHS Global Insight.

I have seen forecasting models using Internet search activity which purportedly capture turning points in housing price series, but this is something different.

The claim here is that calculating dynamic or generalized principal components of some 141 macroeconomic time series can lead to forecasting models which accurately capture fluctuations in cumulative growth rates of metropolitan house price indices over a forecasting horizon of up to 12 months.

That’s pretty startling, and I for one would like to see further output of such models by city.

But where is the publication of the final paper? The PDF file linked above was presented at the Agricultural & Applied Economics Association’s 2011 Annual Meeting in Pittsburgh, Pennsylvania, July, 2011. A search under both authors does not turn up a final publication in a refereed journal, but does indicate there is great interest in this research. The presentation paper thus is available from quite a number of different sources which obligingly archive it.

Yarui

Currently, the lead author, Yarui Li, Is a Decision Tech Analyst at JPMorgan Chase, according to LinkedIn, having received her PhD from Texas A&M University in 2013. The second author is Professor at Texas A&M, most recently publishing on VAR models applied to business failure in the US.

Dynamic Principal Components

It may be that dynamic principal components are the new ingredient accounting for an uncanny capability to identify turning points in these dynamic factor model forecasts.

The key research is associated with Forni and others, who originally documented dynamic factor models in the Review of Economics and Statistics in 2000. Subsequently, there have been two further publications by Forni on this topic:

Do financial variables help forecasting inflation and real activity in the euro area?

The Generalized Dynamic Factor Model, One Sided Estimation and Forecasting

Forni and associates present this method of dynamic prinicipal componets as an alternative to the Stock and Watson factor models based on many predictors – an alternative with superior forecasting performance.

Run-of-the-mill standard principal components are, according to Li and Leatham, based on contemporaneous covariances only. So they fail to exploit the potentially crucial information contained in the leading-lagging relations between the elements of the panel.

By contrast, the Forni dynamic component approach is used in this housing price study to

obtain estimates of common and idiosyncratic variance-covariance matrices at all leads and lags as inverse Fourier transforms of the corresponding estimated spectral density matrices, and thus overcome(s)[ing] the limitation of static PCA.

There is no question but that any further discussion of this technique must go into high mathematical dudgeon, so I leave that to another time, when I have had an opportunity to make computations of my own.

However, I will say that my explorations with forecasting principal components last year have led to me to wonder whether, in fact, it may be possible to pull out some turning points from factor models based on large panels of macroeconomic data.

accuracy of forecasts, asset bubbles, financial forecasts, forecasting asset bubbles, multivariate modeling, real estate forecasts, time series forecasting

Forecasting Housing Markets – 2

I am interested in business forecasting “stories.” For example, the glitch in Google’s flu forecasting program.

In real estate forecasting, the obvious thing is whether quantitative forecasting models can (or, better yet, did) forecast the collapse in housing prices and starts in the recent 2008-2010 recession (see graphics from the previous post).

There are several ways of going at this.

Who Saw The Housing Bubble Coming?

One is to look back to see whether anyone saw the bursting of the housing bubble coming and what forecasting models they were consulting.

That’s entertaining. Some people, like Ron Paul, and Nouriel Roubini, were prescient.

Roubini earned the soubriquet Dr. Doom for an early prediction of housing market collapse, as reported by the New York Times:

On Sept. 7, 2006, Nouriel Roubini, an economics professor at New York University, stood before an audience of economists at the International Monetary Fund and announced that a crisis was brewing. In the coming months and years, he warned, the United States was likely to face a once-in-a-lifetime housing bust, an oil shock, sharply declining consumer confidence and, ultimately, a deep recession. He laid out a bleak sequence of events: homeowners defaulting on mortgages, trillions of dollars of mortgage-backed securities unraveling worldwide and the global financial system shuddering to a halt. These developments, he went on, could cripple or destroy hedge funds, investment banks and other major financial institutions like Fannie Mae and Freddie Mac.

NR

Roubini was spot-on, of course, even though, at the time, jokes circulated such as “even a broken clock is right twice a day.” And my guess is his forecasting model, so to speak, is presented in Crisis Economics: A Crash Course in the Future of Finance, his 2010 book with Stephen Mihm. It is less a model than whole database of tendencies, institutional facts, areas in which Roubini correctly identifies moral hazard.

I think Ron Paul, whose projections of collapse came earlier (2003), was operating from some type of libertarian economic model.  So Paul testified before House Financial Services Committee on Fannie Mae and Freddy Mac, that –

Ironically, by transferring the risk of a widespread mortgage default, the government increases the likelihood of a painful crash in the housing market,” Paul predicted. “This is because the special privileges granted to Fannie and Freddie have distorted the housing market by allowing them to attract capital they could not attract under pure market conditions. As a result, capital is diverted from its most productive use into housing. This reduces the efficacy of the entire market and thus reduces the standard of living of all Americans.

On the other hand, there is Ben Bernanke, who in a CNBC interview in 2005 said:

7/1/05 – Interview on CNBC 

INTERVIEWER: Ben, there’s been a lot of talk about a housing bubble, particularly, you know [inaudible] from all sorts of places. Can you give us your view as to whether or not there is a housing bubble out there?

BERNANKE: Well, unquestionably, housing prices are up quite a bit; I think it’s important to note that fundamentals are also very strong. We’ve got a growing economy, jobs, incomes. We’ve got very low mortgage rates. We’ve got demographics supporting housing growth. We’ve got restricted supply in some places. So it’s certainly understandable that prices would go up some. I don’t know whether prices are exactly where they should be, but I think it’s fair to say that much of what’s happened is supported by the strength of the economy.

Bernanke was backed by one of the most far-reaching economic data collection and analysis operations in the United States, since he was in 2005 a member of the Board of Governors of the Federal Reserve System and Chairman of the President’s Council of Economic Advisors.

So that’s kind of how it is. Outsiders, like Roubini and perhaps Paul, make the correct call, but highly respected and well-placed insiders like Bernanke simply cannot interpret the data at their fingertips to suggest that a massive bubble was underway.

I think it is interesting currently that Roubini, in March, promoted the idea that Yellen Is Creating another huge Bubble in the Economy

But What Are the Quantitative Models For Forecasting the Housing Market?

In a long article in the New York Times in 2009, How Did Economists Get It So Wrong?, Paul Krugman lays the problem at the feet of the efficient market hypothesis –

When it comes to the all-too-human problem of recessions and depressions, economists need to abandon the neat but wrong solution of assuming that everyone is rational and markets work perfectly.

Along these lines, it is interesting that the Zillow home value forecast methodology builds on research which, in one set of models, assumes serial correlation and mean reversion to a long-term price trend.

Zillow

Key research in housing market dynamics includes Case and Shiller (1989) and Capozza et al (2004), who show that the housing market is not efficient and house prices exhibit strong serial correlation and mean reversion, where large market swings are usually followed by reversals to the unobserved fundamental price levels.

Based on the estimated model parameters, Capozza et al are able to reveal the housing market characteristics where serial correlation, mean reversion, and oscillatory, convergent, or divergent trends can be derived from the model parameters.

Here is an abstract from critical research underlying this approach done in 2004.

An Anatomy of Price Dynamics in Illiquid Markets: Analysis and Evidence from Local Housing Markets

This research analyzes the dynamic properties of the difference equation that arises when markets exhibit serial correlation and mean reversion. We identify the correlation and reversion parameters for which prices will overshoot equilibrium (“cycles”) and/or diverge permanently from equilibrium. We then estimate the serial correlation and mean reversion coefficients from a large panel data set of 62 metro areas from 1979 to 1995 conditional on a set of economic variables that proxy for information costs, supply costs and expectations. Serial correlation is higher in metro areas with higher real incomes, population growth and real construction costs. Mean reversion is greater in large metro areas and faster growing cities with lower construction costs. The average fitted values for mean reversion and serial correlation lie in the convergent oscillatory region, but specific observations fall in both the damped and oscillatory regions and in both the convergent and divergent regions. Thus, the dynamic properties of housing markets are specific to the given time and location being considered.

The article is not available for free download so far as I can determine. But it is based on earler research, dating back to the later 1990’s in the pdf The Dynamic Structure of Housing Markets.

The more recent Housing Market Dynamics: Evidence of Mean Reversion and Downward Rigidity by Fannie Mae researchers, lists a lot of relevant research on the serial correlation of housing prices, which is usually locality-dependent.

In fact, the Zillow forecasts are based on ensemble methods, combining univariate and multivariate models – a sign of modernity in the era of Big Data.

So far, though, I have not found a truly retrospective study of the housing market collapse, based on quantitative models. Perhaps that is because only the Roubini approach works with such complex global market phenomena.

We are left, thus, with solid theoretical foundations, validated by multiple housing databases over different time periods, that suggests that people invest in housing based on momentum factors – and that this fairly obvious observation can be shown statistically, too.

asset bubbles, causal discovery, causes of deflation, global conflicts, predictive analytics

Links – April 18

Ukraine

US financial showdown with Russia is more dangerous than it looks, for both sides Ambrose Evans-Pritchard at his most incisive.

How the Ukraine crisis ends Henry Kissinger, not always one of my favorites, writes an almost wise comment on the Ukraine from early March. Still relevant.

The West must understand that, to Russia, Ukraine can never be just a foreign country. Russian history began in what was called Kievan-Rus. The Russian religion spread from there. Ukraine has been part of Russia for centuries, and their histories were intertwined before then. Some of the most important battles for Russian freedom, starting with the Battle of Poltava in 1709 , were fought on Ukrainian soil. The Black Sea Fleet — Russia’s means of projecting power in the Mediterranean — is based by long-term lease in Sevastopol, in Crimea. Even such famed dissidents as Aleksandr Solzhenitsyn and Joseph Brodsky insisted that Ukraine was an integral part of Russian history and, indeed, of Russia.

China

The Future of Democracy in Hong Kong There is an enlightening video (about 1 hour long) interview with Veteran Hong Kong political leaders Anson Chan and Martin Lee. Beijing and local Hong Kong democratic rule appear to be on a collision course.

Inside Look At Electric Taxis Hitting China In Mass This Summer China needs these. The pollution in Beijing and other big cities from cars is stifling and getting worse.

taxi

Economy

Detecting bubbles in real time Interesting suggestion for metric to guage bubble status of an asset market.

Fed’s Yellen More Concerned About Inflation Running Below 2% Target Just a teaser, but check this Huffington Post flash video of Yellen, still at the time with the San Francisco Fed, as she lays out the dangers of deflation in early 2013. Note also the New Yorker blog on Yellen’s recent policy speech, and her silence on speculative bubbles.

Yellen

Data Analytics

Manipulate Me: The Booming Business in Behavioral Finance

Hidden Markov Models: The Backwards Algorithm

Suppose you are at a table at a casino and notice that things don’t look quite right. Either the casino is extremely lucky, or things should have averaged out more than they have. You view this as a pattern recognition problem and would like to understand the number of ‘loaded’ dice that the casino is using and how these dice are loaded. To accomplish this you set up a number of Hidden Markov Models, where the number of loaded die are the latent variables, and would like to determine which of these, if any, is more likely to be using rigged dice.