CDZ Do you welcome lower oil prices?

[320 Years of History]

Unless you work in or otherwise are heavily invested in the oil industry lower oil prices are better. The problem with most economics in the past 30 years is the legacy use of continuous function shortcuts even though it is more reliable and increasingly less costly to use brute strength in the form of computer power to get the right answer the first time.

??? I think you need to explain that one. Are you saying that economists use of, say, a continuous function such as f(x) = x^3 − 6x^2 − x + 30 rather than a discontinuous one such as f(x)= 2/(x^2 − x) is problematic? Also, how is it that using a continuous function is the mutually exclusive alternative to using brute force. Please clarify what you are saying.

http://antonbekkerman.com/classes/ecns309/MathOverviewKeat.pdf

 

[william the wie]

Sales contracts in all capital markets require a legally defined and discrete price and likewise with most other contracts. the math background, if any, of the judge/referee is not known before hand so economists are assuming a degree of numeracy in market participants that may not exist in reality. Data mining is a far more effective way of predicting or explaining economic behavior than economic assumptions. And the further a way from the data is data and what works works the greater the error rate.

 

[320 Years of History]

Sales contracts in all capital markets require a legally defined and discrete price and likewise with most other contracts. the math background, if any, of the judge/referee is not known before hand so economists are assuming a degree of numeracy in market participants that may not exist in reality. Data mining is a far more effective way of predicting or explaining economic behavior than economic assumptions. And the further away from the data is data and what works works the greater the error rate.

Green:
How can that be? Are you getting at merely identifying patterns, or are you going a step further to using them as predictors of future events? Relying on them to portend the continuance of a pattern?

Where I think data mining can play a significant role is in identifying previously unrecognized, and thus unaccounted for, factors in the marketplace, be they objective or subjective. Using data mining, economists can certainly do that and thereby develop better -- that is, more consistently, accurately, precisely and reliably predictive -- econometric models, models from which relevant variables are added or removed as befits the situation at the time.

Red:
Who/what with regard to your remarks above is, or routinely is, the judge/referee?

Blue:
What I think you are getting at is the matter of the predictive value of behavioral economics. To that end, I see the matter you've presented in the same way I think Camerer would.

Economists like to point out the natural division of labor between scientific disciplines: Psychologists should stick to individual minds, and economists to behavior in games, markets, and economies. But the division of labor is only efficient if there is effective coordination, and all too often economists fail to conduct intellectual trade with those who have a comparative advantage in understanding individual human behavior. All economics rests on some sort of implicit psychology. The only question is whether the implicit psychology in economics is good psychology or bad psychology. We think it is simply unwise, and inefficient, to do economics without paying some attention to good psychology.​

I don't think that the "judge/referee's" mathematical acuity is at issue. If need be, anyone can learn the math if they are indeed interested in fully grasping the subject matter before them.

Truly, the one thing I think that is very problematic with scores of economic models isn't the mathematical literacy of the recipients of economic predictions and would be users of theorems, but rather the most basic of economic assumptions: market participants behave rationally. Since economics is necessarily a social science pertaining to humans, it's that behavioral assumption and others like it that strike me as the most likely points at which predictions "break down," as it were. I think the incongruous "knowledge gap," if you will, isn't between economists' and users of economic predictions, but rather between economists and psychologists.

To illustrate, when I put an excess of food out for my cats, I know exactly what will happen. If I toss a toy they love across the room, similarly, I know exactly what they'll do. When I play "chase the laser dot" with them, I know how each of them will behave when the dot gets close to one of the other cats that is also interested in the dot.

Using similarly intriguing stimuli with humans, while I can, say, leave a $20 bill on the sidewalk, I cannot reliably predict what each person who encounters it will do. Were I to send an attractive young woman into a singles bar to "hit on" a bunch of guys, I have the same problem saying how they'll behave; the same is so were it a young man I sent to "hit on" young women. I can fairly well predict the range of response types, but as to what any given individual will do or what will be the predominant reaction, I cannot say. Heck, as I sit in this chair typing, I can't even say with 100% confidence what I'd do were I the "target."

 

[william the wie]

That is not known beforehand as in can you correctly name the shape of the distribution curve in a chaotical system or how it is obtained?

Virtually.no economist has had real world success and been hired a second time for a non-academic/non-governmental job has found your assumption useful. By the way Long Term Capital Management is considered the definitive repudiation of your position.

 

[320 Years of History]

That is not known beforehand as in can you correctly name the shape of the distribution curve in a chaotical system or how it is obtained?

Virtually.no economist has had real world success and been hired a second time for a non-academic/non-governmental job has found your assumption useful. By the way Long Term Capital Management is considered the definitive repudiation of your position.

It's not that I doubt you; it's that I don't know the topic well enough to know whether I do or not. I'd to know what I think about it, but neither what is so for "virtually no economist" or how "long term capital management" repudiates my remarks is not at all known to me. Forgive me, but I'm not going to just take your word for it.

You know, I am one of the few folks on here who will, when presented with it, will read scholarly papers and or members scholarly (i.e., duly referenced and contextually relevant factual support for premises/assertions not supported in the essay itself via a well developed argument) original essays presented on this site. Would you please provide me with some sort of scholarly reference materials that explain in much better detail some of the points/ideas you have made.....the emboldened ones in particular? What I'd like is:

• To see some empirical evidence supporting your claim about what "virtually no economist" who works outside of government and academia finds with regard to what they find useful.
• To know which of my assumptions are you referring?
• What specific aspects of LTCM's history repudiate my position and which of the three I mentioned is it you have in mind with regard to LTCM.

As goes LTCM, what I know/believe about it is limited. You must certainly know something about it/them that I do not. Largely, what I understand as the important points to have taken from LTCM's rise and fall are:

1. LTCM partners " badly misjudged market dynamics and volatility, making common risk management mistakes on a grand scale."
2. When Nobel laureates goof, it's on a grand scale.
3. The combination of tremendous leverage and illiquid markets is a very dangerous one. A fund such as LTCM can be highly leveraged if it is in highly liquid markets, since the liquidation of assets for the purpose of meeting margin calls would not have a great effect on the market. Similarly, a fund can operate in highly illiquid markets if it is not highly leveraged, since it could never be forced to liquidate its positions. However, the combination of the two factors is a risky one. Even if LTCM had perfect foresight about the future value of its investments, it could not judge the intermediate steps that its securities would take on their path towards their end state. As Ayman Hindy, an LTCM strategist, put it “[t]he models tell you where things will be in five years. But they don’t tell you what happens before you get to the moment of certainty.”
4. A highly volatile market, such as the one seen in August and September of 1998 can cause an increase in the anomalies that funds like LTCM believed were inefficient and were betting against. Even a short-term deviation from the path to efficiency that a hedge fund manager has planned out can cause tremendous problems such as those experienced by LTCM in 1998.
5. The LTCM debacle also points to the need for a greater role for game theory in trading models. LTCM appears to have believed too fervently in its models of rational markets and options pricing. What these models did not account for, apparently, was that trading is, in part, a game against rational agents. LTCM’s models seemed to have ignored the possibility that it could be perfectly rational for another trader, such as AIG, to trade against LTCM in order to weaken LTCM's position, or if not rational (economics sense), something people nevertheless did.
   
   [What may make that behavior seem rational is a host of psychological motivators as well as the objective quest/demand for rapid short-term over long-term gains. There is no denying that consumers demanding the short gain would have moved opposite LTCM for in the short-term, such actions may move prices away from their “rational” levels this may be a more profitable strategy than waiting for the return to the long-run equilibrium price. One may take that stance, but doing so is defining rational behavior in terms of the goal rather than in its own right as well as trying best as one can to remove the "social" from the science of economics. LTCM's example shows the peril of doing that.]

On August 17, 1998 Russia devalued the ruble and declared a moratorium on 281 billion rubles ($13.5 billion) of its treasury debt. This default created calamity in financial markets as many Russian banks and securities firms exercised force majeure clauses on their derivative contracts that allowed them to terminate these contracts. Many customers who had been using these contracts to hedge their Russian currency and debt positions were then left with unprotected positions that had lost much of their value. These actions caused a massive move to quality in global financial markets that worked against LTCM on many of the positions that it had taken. LTCM had undertaken many trades in which they felt that so-called “quality” liquid investments were overpriced with respect to less liquid or less creditworthy investments. When Russia defaulted, the enormous demand for high quality investments caused spreads between high quality and lower quality investments to widen. These were the exact spreads LTCM had wagered would narrow.

Moreover, LTCM's founders didn’t simply believe in the models that they were using -- they had helped to create them. This reportedly led to a tremendous amount of hubristic faith in the models behind LTCM’s investments. The founders of this fund believed that historical trends in securities movements were an accurate predictor of future movements. For example, the founders believed that historical volatility was a good proxy for the future volatility of stocks. Their faith in this belief led them to sell options in which the implied volatility was higher than the historical volatility. In the words of Victor Haghani, one of LTCM’s primary strategists, “[w]hat we did is rely on experience. And all science is based on experience. And if you’re not willing to draw any conclusions from experience, you might as well sit on your hands and do nothing.”

With equal confidence were viewed the VaR models LTCM applied to historical data to project information about future price movements. Those models project the probability of various losses based on the prior history of similar events. Unfortunately, the past is not a perfect indicator of the future. That is exactly why I asked you the question I did about the predicting the future based on a pattern of historic events.

On October 18, 1987, for example, two-month S&P futures contracts fell by 29%. Under a lognormal hypothesis, with annualized volatility of 20% (approximately the historical volatility on this security), this would have been a -27 standard deviation event. In other words, the probability of such an event occurring would have been 10^160. This is such a remote probability that it would be virtually impossible for it to happen. Similarly, on October 13, 1989 the S&P 500 fell about 6%, which under the above assumptions would be a five standard deviation event. A five standard deviation event would only be expected to occur once every 14,756 years. And yet it happened....

Based on the above, it seems to me that the LTCM example illustrates that:

• What economics in advanced applications assumes will be the rational behavior of buyers and sellers in a marketplace does not always and reliably for long enough pan out as rational. The behavior patterns of humans, as I stated;
• Past events are imperfect predictors of the future, be it with regard to coin tosses or hedge fund management.

With that said, I hope that you will endeavor to respond in equally plain and unambiguous language (I notice you sometimes like to dwell in the world of finance industry jargon) either offer a rebuttal, explanation or third party, scholarly references to that end, that is if you don't agree.

 

[william the wie]

I see the source of confusion now. LTCM used models based on a modified long tail normal curve distribution. The witch's/Fireman's hat that emerges with iterative mapping of opening and/or closing prices with say N =X axis and N + 1 = y axis results, for broad stock indices, in a peak 8% to the right of zero. For bonds and mortgages the odds are harder to compute due to interest rate risk, ratings down grade risk and default risk. What blew them up was the assumption that hedging all risks at a profit was possible. For example, in writing puts as I do the break point is a 10% decline because that triggers margin calls. As long as I hedge 12-15% below the S&P my losses will be greatly limited. On Danish mortgages as at LTCM I am unaware of any possible affordable hedge.

 

[Dovahkiin]

Yes, and tax oil while prices are low.