Nassim Nicholas Taleb gave a presentation in New York yesterday which hews closely to a recent piece of his, although his talk did include some additional interesting charts and anecdotes.
The article is worthwhile, and worth your attention, but let me highlight the two things I found most interesting.
First was his “fourth quadrant” construct. He sets up a 2 by 2 matrix. On one axis is phenomena that are normally distributed versus ones that have fat tails or unknown tails or unknown characteristics. On the other axis is the simple versus payoff from events. Simple payoffs are yes/no (dead or alive, for instance). “How much” payoffs are complex.
Models fail in the quadrant where you have fat or unknown tails and complex payoffs. A lot of phenomena fall there, such as epidemics, environmental problems, general risk management, insurance, natural catastrophes. And there are phenomena in that quadrant that have very complex payoffs, like payoffs from innovation, errors in analysis of deviation, derivative payoffs.
The other part that caught my attention was the estimation of fat tail risk.
As most readers know, all the fundamental models of finance theory use Gaussian (normal) distributions. Trading markets do not have normal probability distributions. Eurointelligence had a bit of fun with the particularly wild ride of last October:
October 2008 was certainly a spectacular month in the stock markets….
Those of us who studied modern finance theory, however, were truly astonished by the sheer improbability of the events occurring in the stock markets during that fateful month. One of the basic assumptions used in almost all our finance models is that returns are normally distributed. These models are widely used to price derivatives and other complex financial products. What do these models tell us about the probabilities of the events that occurred in October?
The following table gives an answer. We selected the six largest daily percentage changes in the Dow Jones Industrial Average during October, and asked the question of how frequent these changes occur assuming that, as is commonly done in finance models, these events are normally distributed. The results are truly astonishing. There were two daily changes of more than 10% during the month. With a standard deviation of daily changes of 1.032% (computed over the period 1971-2008) movements of such a magnitude can occur only once every 73 to 603 trillion billion years. Since our universe, according to most physicists, exists a mere 20 billion years we, finance theorists, would have had to wait for another trillion universes before one such change could be observed. Yet it happened twice during the same month. A truly miraculous event. The other four changes during the same month of October have a somewhat higher frequency, but surely we did not expect these to happen in our lifetimes.
Now supposedly quants have developed some fixes to various pricing and risk management models to allow for tail risk (can quant readers in the audience please tell us about them in comments, as in how they work and how successful do you believe them to be? I assume it’s GARCH, but confirmation/elaboration/additions welcome).
Taleb casts doubts on these fixes:
Let us start with the inverse problem of rare events and proceed with a simple, nonmathematical argument. In August 2007, The Wall Street Journal published a statement by one financial economist, expressing his surprise that financial markets experienced a string of events that “would happen once in 10,000 years”. A portrait of the gentleman accompanying the article revealed that he was considerably younger than 10,000 years; it is therefore fair to assume that he was not drawing his inference from his own empirical experience (and not from history at large), but from some theoretical model that produces the risk of rare events, or what he perceived to be rare events.
Alas, the rarer the event, the more theory you need (since we don’t observe it). So the rarer the event, the worse its inverse problem. And theories are fragile (just think of Doctor Bernanke).
The tragedy is as follows. Suppose that you are deriving probabilities of future occurrences from the data, assuming (generously) that the past is representative of the future. Now, say that you estimate that an event happens every 1,000 days. You will need a lot more data than 1,000 days to ascertain its frequency, say 3,000 days. Now, what if the event happens once every 5,000 days? The estimation of this probability requires some larger number, 15,000 or more. The smaller the probability, the more observations you need, and the greater the estimation error for a set number of observations. Therefore, to estimate a rare event you need a sample that is larger and larger in inverse proportion to the occurrence of the event.
If small probability events carry large impacts, and (at the same time) these small probability events are more difficult to compute from past data itself, then: our empirical knowledge about the potential contribution—or role—of rare events (probability × consequence) is inversely proportional to their impact. This is why we should worry in the fourth quadrant!
The issue is that when you do find one of these outliers, and you are working in a region where those extreme events are big enough to worry about, like days when the markets are really roiled, you wind up having so few of the super extreme events that one can wind up distorting how you estimate the significance of tails (Taleb goes through this in geekier form in his technical appendix).
Taleb gathered every kind of market and macroeconomic data item he could locate (stock prices in various markets, commodities, interest rates, currencies, inflation, etc) where he could have a reasonably long time series. For the ones where he had 40 years. single events would take up most of the estimate of the tail risk. For instance, the 1987 crash is (from memory) 78% of the estimate of the tail risk for the S&P 500. For silver, it was even worse, nearly 90% (click to enlarge):
The text of the article is here.
Taleb was relaxed and funny at points during his talk, and seemed to enjoy chatting with the audience afterwards. I suspect his prickly streak comes to the fore when dealing with types he calls “charlatans”, particularly when they know enough to know better.
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Typo:
On one axis is phenomenal => On one axis is phenomena
The talk pretty must puts a stake through the heart of current finance theories and assumptions. Taleb has always been an empiricist and it shows. This is not a bad thing. Without observational data, theorists wouldn’t have a job since they’d have nothing to model. His point about biological systems having redundancy is spot on. There is so much redundancy in biological systems it’s rather amazing. From the molecular to the organism, there are two or more copies of something. If there’s one copy, it’s damage resistant. The heart and liver are single point failures. The liver has an amazing ability to regenerate. This was even recognized by the Greeks over 2000 years ago in the Prometheus myth. Current research is discovering that the heart has regenerative abilities as well. Human control systems are brittle. If you don’t believe me, see how long you can run your firefox browser or computer before it crashes completely. But then natural systems are grounded in what works and they build on that. If you fail in the real world, your genes don’t get passed on to the species.Finance needs more robustness and more redundancy. But in the end, security is a psychological illusion whether its financial, physical, or national.
In 2005 Nomura Securities did a presentation at the annual securitization conference in the US on Fat Tail VAR. What good that innovation did the industry. The problem is that all these systems try to put a rate of occurrence on these events, therefore attaching a probability and allowing a firm to have what they perceive is an acceptable amount of risk. The problem is not so much the models, they do give an idea of ever day risk, the problem is that their approach to risk is wrong. How have we survived as a race being such bad predictors then? We use reverse engineer (well the smart ones who survive do), you see where your catastrophic breaking point and make sure you dont get there given that events are sometimes unexpected – this has become impossible in an over-levered system because the only solution is to be under-levered, to have a cushion for a rainy day.
trying to forecast tail risk is impossible and even if it wasn’t, it’s still useless, because if one out of 10 people in a room will get shot that statistic is not much consolation to the one who gets shot. You should just not be in the room and if you are you should wear a bullet proof vest and if you do you still might get shot in the face.
If there were an update of Charles McKay’s Classic ‘Extraordinary Popular Delusions and the Madness of Crowds’ contemporary finanical/risk management theory would surely have a chapter of its own — right next to the witch burnings, inquisitions and end of the world prophecies.
Two things about all this astonish me (and sicken me to a large degree, too).
1. The ideas themselves that Taleb articulates are so clear, so obvious, so demonstrable from the evidence. The math is elegant and creative, but it’s a restatement of the obvious in a language that allows the obvious to be manipulated in its component parts.
2. The adoption and use of these theories comes only with a willfull suppression of integrity, so that conscience doesn’t interfere with personal profit and career advancement at the expense of society at large. The cloak of credibility offered by the math is an obfuscation mechanism that distracts clear thinking from consideration of the consequences of action. This is the logic of a mob and the devolutionary force of history, which has been countered from time to time only by the strong resistance of the individual consciousness.
To say that contemporary financial theory and risk management is a fraud is a narrow understatement.
In a larger sense, it is a manifestation of a disease of collective consciousness. It is the suppression of Logos and the embrace of Thanatos. It is, in a purest sense, evil. This may be something of an arid intellecualization and sort of Over the Top, but there is a theoretical purity to it. Active in other spheres, the same underlying psychic structures produce mob violence, pogroms and holocausts.
A knowledgeable colleague sent me this quote from Jeremy Grantham on Taleb:
Second, Nassim Taleb and the Black Swan logic, which I have previously admired in public. Taleb is completely dismissive – in a way only he can be – of any near certainties. He implies that we have just suffered from an outlier event crashing up against standard risk modeling that only assumes that events will occur in an approximately normal way. He argues that modeling the 95% or 99% normal range in Value at Risk (VaR) misses the whole point: that the real game is played out in the final 1%. It’s hard to disagree with this criticism of VaR, but is it relevant in this case? Was the recent breaking of our credit and asset bubbles a totally unpredictable outlier? We believe that we live in a world where bubbles routinely form and where there are – in complete contrast to Nassim Taleb’s belief – some near certainties. One is that bubbles will break. Bernanke should not have said, “U.S. house prices have never declined,” thus implying that they never would. He should have said, “Never before has a threesigma, 1 in 100, U.S. housing bubble occurred, and be advised that all such analogous bubbles in other asset classes and in housing in other countries have always burst.” (Robert Shiller for the Fed! He would have said almost exactly that.) The bursting of the U.S. and U.K.housing bubbles, the profit margins, and the risk premium in global asset prices were all “near certainties.” This was a White Swan, a particularly White Swan. Taleb’s work will no doubt be correct when we have a genuine BlackSwan, but this was most definitely not it. (Okay, Nassim.I can hear you thinking: this guy Grantham is a complete loser who has obviously missed my entire point.)
Dave
The purpose of theory is to explain observable phenomena; there is no other purpose. One can make hypotheses to posit or arrange to test what is not observed, but theory has to explain real actions. Taleb's emphasis that in a series one needs, at a minimum, three times the date sequence to the duration examined is a fine rule of thumb, by the way.
Financial systems . . . Systems have their own intrinsic 'logic;' a poor word but as an explanatory heuristic one we can all understand. Consider _why_ living systems have redundancy. If one takes as the best available theory, and I do, that the organizational driver in the existence of a biological system is metabolism, and that secondarily the source of metabolism was in self-catlytic cycles in fecund environments, it is easy to see why redundancy is so basic to such systems. They organized around self-repetition. Thus as the simplest enduring self-catalytic metabolic entities diversified their structure they added what cushioned self-repetition, not what could, with a single malfunction, disrupt it. Continuity via self-similarity in behavior is the 'logic,' not uniqueness. Adding layers and layers of function have made biological systems colossally complex, but the logic of self-continuity through self-similarity pervades them, and in essence forces most new occurrences in them to conform to that logic. Individual organisms which generate their own singularities have a higher probability of disrupting their own metabolism—they die abrubtly, and so tend to weed themselves from the gene pool of their kind.
Not so in the financial system. It is important to understand that the financial system is an 'accidental system' in its way. It's processes and parts were not designed to function together, either by self-organization at the fine scale or any putative creator. [I'll exclude the hyopthetical that the financial system is the Devil's plaything as a needless complication of the argument.] Yet even while the components of large-scale financial processes are an assemblage rather than organic in their integration, their _is_ a logic of a kind which pervades their systemicity: complete the transaction. And that is a problem. Where interactions in the financials system biased to continue processes, they would conserve actions which make for continuity, and hence intentionally or not conserve actions which make for stability. But the 'logic' is to complete individual transactions rather than to sustain transaction interactivity. So in my view, that 'logic' tends to promote discontinuous states, and accordingly disjunct systemic components: "I'm incomplete; you're relevant. –> I'm complete; you're irrelevant." The _intrinsic_ logic of the financial system does not promote continuity, stability, or continuous reciprocity. And far from singularity being undesirable, singularity can be highly desirable if one can optimize the system around ones own order or utilize resources not systemically pervasive.
So the financial system is not only disparate in its consitutent parts, it's inherent 'logic' does not promote stable continuity but rather unique states with asymmetric distributions as i see it. This is why stability has to be externally imposed on the financial system: that's called 'regulation,' for those who don't have the glossary handy.
Word verification says that this should be called the Buleb Analogy.
So Dave, per your citation from Grantham, yes exactly, but. The bursting of the bubble was a very high probability, a Dead Duck event. But the severity of the burst has had some fat tail knock-ons. For example, the speed with which the commercial paper market collapsed at the onset of the crisis, if not a true Black Swan is an ‘out in the 1%’ kind of outcome. I agree with Grantham’s basic point, and have thought as much myself; that said, it’s important to keep in mind that the crisis is not a singular process but the interaction of parallel processes, some of them closely linked, some of them loosely linked. One can make completely accurate by diametrically opposite arguments about different processes in the crisis. It’s important to reference remarks with specific observations, as Grantham did linking the _housing bubble_ to an analogy of its change probability.
I am not sure I am happy with the idea that this was a once in a million event that was unpredictable. I had uneasy feelings about the way the economy was going as did many others, and many risk management teams were screaming at the top of their voices only to be removed or ignored. Lots of people knew the risks they just did not expect the cascade of problems that entailed. The problem was not solely that risk models were inadequate because even if they were they would have been ignored. Too many people were making too much money ignoring the risk in the hope they could escape before the house of cards tumbled down. Lets not forget there were many who did make it out before things tumbled.
That many were so wholly unprepared is because the lessons of previous generations were relaxed a little at a time. Our perceptions are shaped by our own experiences rather than those that went before and by nature we change things to reflect the reality we see. 80 years from now we are most likely doomed to repeat this episode as the new generations assume they understand and are better equipped to deal with things. It is a natural cycle of tightening and relaxing of regulation which in our arrogance we are doomed to repeat.
Non-gaussianity is one pitfall. Non-stationarity is another. A world of Gaussian variables loses predictablity when variance and covariance suddenly change. We see terabytes of data in exploration seismology. Non-stationarity is the larger problem.
put together a management team that can only play golf along with quants that can crunch numbers and probalities on the datasets given without ever dealing with the markets themselves and this is what you get: excuses that things that happen once in x trillion billion years have ocurred.
really pathetic, but entirely due to lack of common sense.
I think most of what Taleb has to say is pretty trivial, if not flat out wrong in many places. The meltdown wasn't a black swan. Everyone knows Hume's problem of induction. Everyone knows that models are MODELS. Most experts in VaR were quite aware of the limitations, it was other people that used the models as cover for reckless activities. David Li was quite clear about the limitations of Copula models, but no one listened.
IF Taleb were right about option pricing, there wouldn't be a volatility smile – but obviously there is. If Taleb was right about insurance & fat tail events General Re and Warren Buffett would be bankrupt – instead, it was Taleb's Empirica fund that collapsed in 2006 buying overpriced puts.
Is it just me, or is Taleb a complete charlatan? How do people not see that he is a total huckster?
It’s really quite distressing to see that the ‘issue’ of ‘fat tails’ is endlessly discussed and never actually stated correctly. Taleb does not go anywhere near far enough, which either shows shoddy thinking or cognitive capture to the empirical finance community.
Here’s the reality: the prices of financial instruments are not a probabilistic phenonema at all. Rather, the methodology of science is only capable of working with probabilities (in fact) and so see everything in this way.
Proof: in order to believe that the stock market (for example) is a random series you have to believe that it is equally likely for the stock market to drop the day after 9/11 as it is to raise that day. You also have to believe it is equally likely to drop before 9/11 as it is drop after.
I’ve got a huge supply of bridges available for sale (cheap!) to anyone who believes the foregoing. Seriously.
It is beyond absurd that this charade of modern finance is allowed to persist. Note that the above issue is much more significant than ‘fat tails’.
I have to offer a big thanks to craazyman. Not only did you offer the best comment here, you inspired (with some help from Aristophanes) my user name. Maybe we know each other in real life?
Anyway, Taleb’s celebrity means that he is discussed more widely than he is understood. He has said for YEARS that finance was fragile, due to faithful application of fraudulent theories. If you go to his website, he calls all who attribute the current crisis to black swans “imbeciles”. Tough language, but true. He hs NEVER said this crisis was a black swan–actually quite the opposite.
The misplaced understanding makes me wonder if the real problem with finance isn’t just intellectual imbreeding. Even portfolio theory has independent actors as an assumption. But what if the actors have the same view of the world? I.E. they study the same things at the same schools and become indoctrinated into the same simple mindedness. When I think of the problem like that, I actually kind of wish Taleb was more prickly and that there were people with knowledge of philosophy, math, Ancient Greek(ha!), etc. taking pot shots at the finance/econ establishment.
If I give you a time series of returns and ask you to compute some risk metric from it, there are a few ways to go.
One way to go is to compute its volatility. This is common. More so at traditional buyside firms than sellside, but it is prevalent enough that it is worth talking about. The very fact that you compute volatility implicitly means that you are assuming the time series is Gaussian. Otherwise, volatility contains little information.
Once you have volatility, you can approximate VaR by
VaR = 2.33*Volatility – Mean
People who do this should be fired. A lot of people should be fired.
Another thing you can do is partition the returns into various regions, e.g. these are “tail events”, these are not, etc. This gets you into a realm “Beyond the Bell Curve” in that you can study properties of the tail events. However, this is exactly the thing that Taleb warns you about. By their very definition, there are few tail events. To get enough samples, you would have to go back so far in time that the period you are sampling may be so different than today to render the analysis useless.
There is an alternative, and this is what I like to do…
One of the important statistical theorems is the central limit theorem. Traditionally, this meant that under a few reasonable assumptions that after you observe things long enough their distribution begins to resemble a Gaussian distribution.
The central limit theorem is one of the reasons (if not the reason) why Gaussian distributions are so prevalent in science and finance.
However, the crucial underpinning of the central limit theorem is also contained in a more general class of distributions, i.e. stable distributions. Here is a wikipedia quote:
ApplicationsStable distributions owe their importance in both theory and practice to the generalization of the Central Limit Theorem to random variables without second (and possibly first) order moments and the accompanying self-similarity of the stable family. It was the seeming departure from normality along with the demand for a self-similar model for financial data (i.e. the shape of the distribution for yearly asset price changes should resemble that of the constituent daily or monthly price changes) that led Benoît Mandelbrot to propose that cotton prices follow a an alpha-stable distribution with α equal to 1.7. Lévy distributions are frequently found in analysis of critical behavior and financial data (Voit 2003 § 5.4.3).Modeling time series as a stable distribution has the advantage that it uses all data, not just the tails. This approach is much better at getting at the tails. It is not perfect of course, but in my experience, if you had a risk management system based on stable distributions (which I do fortunately) and coherent risk measures (VaR is not a coherent risk measure), then you would be much better off today. Doing so, these 10,000 sigma events suddenly seem a lot less surprising.
Just off the top of my head, the issue with silver goes back to a post I always go back to over and over. The point being, massive corruption by The Hunts resulted in an unusual sigma event; a series of events that were not plugged into any models. It’s funny how the models are adjusted later, versus prior, because the mis-calculated inputs never get it right!
FYI: Beginning in the early 1970s, Hunt and his brother William Herbert Hunt began accumulating large amounts of silver. By 1979, they had nearly cornered the global market.[6] In the last nine months of 1979, the brothers earned an estimated $2 billion to $4 billion in silver speculation, with estimated silver holdings of 100 million ounces.
I think that trading profit would be around $10 Billion today, so those dudes wouldn’t even be a small hedge fund or any where close to a Madoff, or a TARP Pirate, or any of the games associated with the current trillion dollar theft — which is thus an event that is beyond generational — and to think that this amount being lost today is going to be recovered within the next few decades is totally retarded; hence, for Obama to be suggesting that we have a recovery under way, or for Bernanke to continue bullshitting that party line, is just hogwash with a massive tail that is beyond calculations!
SILVER MADNESS AND BIG FAT FURRY TAILSDaniel Dicker, a former oil trader writing at TheStreet.com, contends that there is a way to test the hypothesis that speculation is influencing oil prices (a view that Dicker supports). Exchanges could impost a "liquidiation only" requirement, which was last used to break the Hunt brothers' attempted corner of the silver market in the early 1980s (hat tip reader Michael).
In one instance, however, the speculation premium was "successfully" tested – in the silver markets in 1980 when the Hunt brothers attempted to corner the market. As silver approached $50 an ounce in January 1980, the commercial participants asked for relief from the enormous margin calls from ever-rising prices. The CFTC and the Comex (the predecessor to the Nymex) responded effectively by imposing "liquidation-only" trading — traders were allowed only to close existing positions and not permitted to initiate new positions.
This forced purely speculative positions to be closed rapidly, as they could no longer be "rolled" into future months at expiration. This caused the price of silver to drop by $12 the day after it was imposed, a decrease of over 20%! Over the course of the next three months, as contract months expired, the price dropped over 50%.
>> Is it to late to question economic theory and fat tail events like the oil suppliers manipulating the global oil market, i.e, in terms of classic economic theory, the spike in oil should have resulted in greater production by suppliers who would have had an incentive to produce more oil while the price was high, yet, the supply was decreased and a manipulated shortage rationed the supply and helped the cost of oil scream higher every day — meanwhile, the market continued to push prices higher and higher specifically for commodity manipulation — and meanwhile The Bush Coup allowed this to occur as they failed to regulate a market that was clearly not working properly.
That government-back fraud and antitrust-like behavior goes back to another favorite post, which is related to people like Brooksley Born that understand that government should regulate corruption versus being involved in it as a key player!
Full Disclosure: The Author is currently digesting breakfast and feels stressed out by this bullshit…. and friggin refuses to spellcheck or go back over this post.
Jon Claerbout is absolutely right. Nonstationarity renders traditional statistical approaches useless. Finance is afflicted with a particularly interesting form of nonstationarity–adaptivity–whereby new analyses and information immediately become part of the system and change its dynamics.
We have a huge body of mathematics that was developed to understand physical phenomena governed by unchanging natural laws, and we keep trying to use this math to develop predictive models about finance (and ecology and many other nonstationary things as well). These constructs can be useful and informative, but never predictive over the long term. We clearly need another approach and another way of thinking about things. Taleb’s suggestion is that we acknowledge that we simply can’t model these systems, and that’s probably the best we can do right now. But I wonder if we can do better.
And I’d add my voice to those pointing out that Taleb hasn’t cast the recent crisis as a black swan. I remember an interview from maybe a year ago where, speaking about Bernanke, he said something like, This crisis wasn’t a black swan to me, but it was to him–I wouldn’t use that guy to drive my car.
The more things change …
I’ll be immodest and quote myself from a 1998 article:
“Market returns are neither NID nor stationary, and regardless of how one massages
the data, violations of those assumptions can lead to serious practical consequences.”and
The array of powerful statistical techniques available to the risk manager – to the extent that they depend on the assumptions of normality at the extremes, serial independence and stationarity – are founded on quicksand.One month after the article was published LTCM collapsed. That was a dress rehearsal.
The puzzlement about the manifestation of very unlikely events often arises because no attempt is made to understand the process that generates the data.
This is often the case with global risk models in a bank. You collect only data about the “risk factors” where these are understood to be quantities that are needed to price your positions.
Thus you might collect data about stock prices, volatilities, interest rates, fx rates, …
Often you don’t care to take into account macroeconomic data that are undoubtedly related to the risc factors but are not a direct input to you
pricing models (which could be done fairly easily). You make no attempt to understand the physical process that generates the data
(admittedly a tall order in economic theory).
This is a bit like living at the base of a mountain in a narrow valley through which there flows a creek.
The creek once produced a minor flood flood and is thus considered a risk.
Instead of investigating how the flood came about you simply
build a risk model by collecting daily data about the flow rate
and deriving a distribution. The distribution has a fat tail, you have an extreme value theory, …
A flood twice the size of the last one is conceivable but unlikely.
You do not care to ever go up the mountain to see what’s going on up there.
If you did you might discover that the glacier has been melting and an enormous lake has formed with a dam consisting of ice which is getting thinner by the day.
When it breaks …
The Hunt Brothers (in conjunction with the Saudis) were buying futures and taking delivery.
There is nothing corrupt about that.
It is however utterly corrupt to install the trading rules you alluded to.
Apart from the Hunt brothers many other people had silver stashes — admittedly not as large. The smelters were smoking day and night.
Only commercial users were suffering and these only because they were not properly hedged.
The Hunt brothers killed themselves because they were huge buyers at astronomical prices — thus doomed one way or the other.
All this talk about whether or if the financial systems can be modeled, and what those models should assume about the system being modeled. That sounds a bit odd to me.
The financial system itself IS a model. You start to sense this when you recognize all the abstraction and regulatory processes being offered up as substance. Well none of this stuff is real, none of it is fundamental to anything, none of it emerges whole from the workings of the universe like Athena from the brow of Zeus. It’s a bunch of conventions is all, random ideas and parameters about how “some system” is operating on the back end.
I’ll hazard a guess about what that “unknown system” is and why we are modeling it:
The financial system is modeling the price and availability of goods, and their location. We need this model because the world is too big and the goods too numerous and providers too anonymous. The world/market used to be very small (you bartered with your neighbor over food stuffs, pricing or exchange rates were simply understood) but as it got bigger we began using abstractions to get around the price discovery issues. We set prices, we negotiate trade agreements, we then have to factor in the cost of risk should it turn out we “modeled” this stuff wrong. We buy insurance. We float loans. It’s a huge model of two people haggling over a fence about the exchange rate of my eggs for your turnips.
We NEED to model two people haggling over a fence because we still have this fundamental issue of getting food into our kitchens. It would be better if people could just haggle over what they needed, then we quickly wouldn’t even NEED a financial system to simulate over many months — and at some measurable risk of failure — what they could accomplish in minutes with zero risk.
Coming back to my point; you really shouldn’t model the model at all. Therein lay ruin. Therein lay the path to intellectual flights of fantasy; you have just convinced yourself that the “model” financial system you cobbled together out of random ideologies and pseudo-science is the real thing.
Guess what; the whole thing is a fiction. From the top to the bottom. Well except for the genesis of it, where two people where haggling over a fence.
cougar
FYI I believe I was mistaken in including silver in the “40 years of data” comment. I recall in the talk someone brought up the Hunts, and Taleb said no, the series did not include that, the spike shown was from 1986.
Cougar,
“the whole thing is a fiction”
Right on!
Wonderful post and comments by all, better than coffee in the morning to get the gray machine going and full of morality, ethics, math, physics, philosophy, tank think, DE-bugging of the human action = reaction construct…
If the Universe does not complicate our lives enough, we have to make things even harder more complicated via paranoid species syndrome. Prediction of outcomes to our actions multiplied by the number of actors/groups/cubed, all with self interest as motivation hahahaha!
Every time we learn a new trick (desirable outcome), we over extend it until the big break or resulting contraction. Hay Doc H don’t worry about the HALOC fat tails, try the Quantum computer, imagine the mess we can create with that baby, its not the tool that distorts reality, but the operator and his choices of input and analysis of the output.
Skippy…are we the Fat Tail (creator) in the model? Can we model our behaver/interaction to the atomic level.
There seems to be some question about the reality of not only the data and not only the models and not only the financial system, but on the economy, and therefore, to me, on economics.
Well enough that it should be studied, but the reward of its pontifications cloud there being any true scientific effort.
As a result, there is talk of the natural, even biologic economy being of more realism than that of the monied and academic aristocracy presently in charge.
Those seeking such a realistic, scientific and biological model would do well to review Frederick Soddy’s more Ecological Economy.
Maybe we can finally realize the true nature of this phenomenon that we call our political economy.
A recent NYT commentary on the subject:
http://www.nytimes.com/2009/04/12/opinion/12zencey.html?pagewanted=all
Are those allowed?
Frederick Soddy’s scientific view of the economic system, and its foundation of its money system, led him to originate the principle of full-reserve banking.
Why are the economists blind and vacuous of thought towards full-reserve banking?
The search for differentially greater financial returns, what a wonderful positive feedback system. :)
The financial system is a stochastic (or may be ultimately deterministic, doesn’t matter) non-linear control system.
Any sufficiently complex system is unpredictable unless one performs an equivalent amount of computation/work to the system’s computation to reach its state.
A mathematical equation is basically a shortcut to the work performed on a system, and as such, there are no equations that can model the outcomes of complex systems.
Monte carlo simulations are useful in determining the forces on a system given a state at time t and can expose stationary distributions where one can compute or predict outcomes of events (ie ripple effects of defaults, counterparty events, etc.,) but can’t give completely accurate estimations on low probability events.
I wish Taleb had also touched on Minksy’s financial instability hypothesis (or did he), that the lack of low probability events tends to have a positive feedback on greater risk taking and leverage.
The example I give is building property in a flood plain. Land values in the flood plains are less because of the low probability occurrences of 100 year floods. People then build a 100 year flood levee. That sets up an incentive to have lower costs and build in flood plains to get a greater delta on return on use of land. As people see the greater returns from the flood plain land owners, they rush into developing the flood plain land even more to chase the same delta. Prices rise to close to equilibrium with the highlands, until the 200 year flood comes and wipes out the lowland development.
Despite the many well thought out, well articulated comments on this post, I cannot help but recall a book I read many decades ago, The Structure of Scientific Revolutions. A main thesis was that much of what passes for developing knowledge is not much more that technical refinements of the dominant paradigm. Many of these comments leave me with the feeling that such is the case with respect to this discussion. Despite the recognition, and attempted reconciliation, of phenomena not adequately explained by prevailing modern financial theory, a broader conceptual framework proposing an alternative framework appears to be absent.
A “Unified Field Theory”, if you will, might include the impact of socio-economic-political factors in the validation of a specific body of knowledge such as financial theory. For example, there has been a entire academic industry created by the perpetuation of the mythology of modern portfolio theory.