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Paul Wilmott Wants To Retrain and Reform Wall Street's Quants

theodp writes "What if an aeronautics engineer couldn't reconcile his elegant design for a state-of-the-art jumbo jet with Newton's second law of motion and decided to tweak the equation to fit his design? In a way, Newsweek reports, this is what's happened in quantitative finance, which is in desperate need of reform. And 49-year-old Oxford-trained mathematician Paul Wilmott — arguably the most influential quant today — thinks he knows where to start. With his CQF program, Wilmott is out to save the quants from themselves and the rest of us from their future destruction. 'We need to get back to testing models rather than revering them,' says Wilmott. 'That's hard work, but this idea that there are these great principles governing finance and that correlations can just be plucked out of the air is totally false.'"

26 of 198 comments (clear)

  1. Wow! by viyh · · Score: 4, Insightful

    What a concept! Basing conclusions on experimental evidence from testing via trial and error rather than warping reality to fit your business model. That's incredible!

    --
    "I have never let my schooling interfere with my education." --Mark Twain
    1. Re:Wow! by dov_0 · · Score: 4, Insightful

      What a concept! Basing conclusions on experimental evidence from testing via trial and error rather than warping reality to fit your business model. That's incredible!

      It will never last in the 'real' world...

      --
      sudo mount --milk --sugar /cup/tea /mouth /etc/init.d/relax start
    2. Re:Wow! by rtfa-troll · · Score: 4, Insightful

      I'm glad you got insightful not funny. You are right. This is one of the case where experimentalism actually breaks down in the real world.

      The problem is that the quant's model is in its self an input to the reality and the processes aren't statistical and stochastic. When I know (or even partly correctly guess) what model you are using for investing, then I can gain several benefits from altering my behavior. I can create false investment opportunities which match well with your model. I can predict when you will need to buy something and push up the price just before hand. I can guess when you will become over exposed to some asset and force you to sell too cheap.

      The models are useful, but in the end lots of business stuff just has to come down to gut feelings and judgement. You also just have to do analysis which goes beyond the empirical (nobody has ever tricked us before) into risk control (what can we do to make sure nobody can do that in the future; how would we tell if they were trying to).

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      =~ s,(.*),<sarcasm>$1</sarcasm>,g if any_point_you_wish();
    3. Re:Wow! by BlackSabbath · · Score: 4, Insightful

      > I reject your reality and substitute my own!
      > /mythbuster
      > or /Wallstreet broker

      Disdain for the "reality based community" is nothing new.

      > In all seriousness, this does not sound like a field that needs saving from itself.
      > ...
      > Something doesn't get to be common practice unless a good portion of the field believes that it is good to do so, or at the very least, not harmful.

      I agree with your last statement in application to almost any discipline other than economics and (more specifically) finance. I must disagree with your first though. The ability of greed to short-circuit the mind's ability for critical thought is unparalleled as is the obstinate willingness of great swathes of people to swallow snake oil by the gallon on the merest suggestion of the slightest whiff of profit. My memory may be hazy, but I can recall at least two occurrences of a "new economy" in the last three decades. And of course each "new economy" marks a break with "outdated" beliefs/dogma/tradition (you know - like that outdated belief that you can't make something out of nothing, or that other one about a "turd by any other name would smell as sweet").

      I don't doubt that given another fifteen years or so, we'll have forgotten the "hard lessons", the sincere abjuration of pernicious practices and every other skerrick of common sense. The new "new economy" will have arrived. Only a fool would fore-go the chance to make real money. May I suggest however, that you watch this infotaining interview before you invest in the new "new economy".

    4. Re:Wow! by richg74 · · Score: 4, Informative
      The problem is that the quant's model is in its self an input to the reality

      You are right, this is one important source of problems. I started out in quantitative finance back in the 1970s. (I worked as a research assistant for Fischer Black when I was in grad school.) The initial application of many of the quants' techniques were in markets like US equities, or listed options, where the assumptions that one participant couldn't affect the overall market much and that there were reliable sources of information were probably reasonable.

      But if you look at one of the key "villains" in this last mess, the credit-default swap [CDS] market, it's an entirely different story. I have read Li's paper on the Gaussian copula function, and had a look at an implementation. What it is essentially doing is using a statistical sampling function to estimate the expected lifetime to failure (= default) for a population of debt instruments. Now, there is nothing wrong with the math per se; similar approaches are used in manufacturing for quality assurance. However, there is big difference: estimating the failure rate of, say, light bulbs does not in itself have any effect on that rate. But in the case of the CDS, the failure rate is being used as an input to the model that is used to price the swap. If the default rate estimate is too low (too optimistic), the prices will be too high -- and that, in turn, will lead to lower estimates of the default rate. In essence, there is a built-in feedback mechanism that can act as an error amplifier, a problem that is exacerbated by the lack of transparency and liquidity in the CDS market.

      There's plenty of blame to go around. The managements, who should have known better, were bedazzled by the dollar signs floating out of their economic perpetual-motion machine. The quants knew the math, and their hubris led them to think that nothing else was needed. And the investors, while proving the truth of P.T. Barnum's Law of Applied Economics, forgot that there ain't no free lunch.

  2. How about... by blahplusplus · · Score: 4, Insightful

    ... getting back to the real economy? Many financial products don't add anything to the real economy at all.

    1. Re:How about... by stephanruby · · Score: 3, Informative

      Ok, the link didn't work. I'll try again:

      December 9, 2004

      Caissa founders in court battle

      By Joe Morgan and Richard Irving

      A FORMER Oxford University professor, a chess grandmaster and a software developer are locked in a court fight in New York over ownership of a £172 million hedge fund in which top City names such as Gavyn Davies have invested tens of millions of pounds.

      Paul Wilmott, Ron Henley and Jonathan Kinlay, who set up a secretive hedge fund, Caissa Capital, accuse each other of trying to lure top investors to rival funds after the partnership collapsed in acrimony earlier this year.

      The dispute erupted in September when Mr Kinlay, who has developed a computer program that highlights profitable trading strategies in volatile markets, filed a lawsuit in the Supreme Court of the State of New York against his two former partners. Mr Kinlay is seeking $800,000 (£414,000) in damages for alleged breaches of intellectual property rights.

      Mr Wilmott, a former Oxford University professor, and Mr Henley, who trained the former chess world champion Anatoly Karpov, hit back with a $10 million counter-suit, claiming that Mr Kinlay had tried to wind up Caissa by luring investors to a new fund that he was secretly trying to set up without their knowledge.

      The dispute flared up after Mr Kinlay returned from a holiday in Italy in August to find that Mr Wilmott and Mr Henley had given back $40 million to Caissa's biggest investor, Prisma Capital Partners. Prisma is run by Gavyn Davies, former Chairman of the BBC, Girish Reddy, former head of derivatives for Goldman Sachs, and Tom Healey, a star banker.

      Mr Wilmott and Mr Henley claim that Prisma demanded the return of its money after Mr Kinlay approached its backers with a proposition to invest in his new venture, the Proteom hedge fund. It is understood that the partners were sceptical of the strategy Mr Kinlay planned to use, which essentially involved using the same technology that looks for patterns in genes to predict stock and bond market moves.

      Mr Kinlay, meanwhile, alleges it was his two former partners who were trying to lure Prisma into a new fund that they were also trying to set up. Both sides deny the allegations.

      After the initial dispute, the trio decided to go their separate ways and arrangements were made to write a separation agreement in August. On the basis of this agreement, Mr Kinlay agreed to sell his interests in Caissa Capital and Caissa Capital International, an overseas offshoot, to Mr Wilmott and Mr Henley. He also agreed to terminate software licences granting access rights to his trading program. He collected $377,759 from his former partners under the deal.

      However, court papers allege that the separation agreement negotiated by Mr Kinlay was just a smokescreen while he asked a bank, which was helping him to set up the Proteom fund, to find a legal "rottweiler . . . who would sue his two partners and Caissa's investors".

      It is alleged Mr Kinlay began to plot against his former partners almost a year earlier after deciding that neither "had the time nor the skill set to make a meaningful contribution".

      Court documents allege that Mr Kinlay set up the Proteom fund because, as a minority partner in Caissa, he could not force Mr Henley and Mr Wilmott out. Mr Kinlay denies the allegations.

      PARTNERSHIP BREAKDOWN SEEN AS CHESS CONFLICT

      Page 1 of 2

    2. Re:How about... by stephanruby · · Score: 3, Informative

      Here is the second page:

      "There is nothing that would persuade me to remain in a partnership with someone as stupid, duplicitous and untrustworthy as you have proved yourself to be. As we discussed, I shall make arrangements for your exit from this firm at the earliest convenient opportunity."

      Jonathan Kinlay to Ron Henley, July 20, 2004

      "How like a game of chess this is. Except you are toying, not with wooden pieces, but with people's lives. And you are about to discover in this game it is I who am the grandmaster."

      Jonathan Kinlay to Ron Henley, July 25, 2004

      "White's attack has been successfully put down. Black sacrifices the terminally weakened pawn in order to open lines for the coming counter-attack. Meanwhile, the white knight has been neutralised and lies isolated and vulnerable at the edge of the board, waiting to be picked off by the black forces at their leisure."

      Jonathan Kinlay to Ron Henley and Paul Wilmott, August 15, 2004

      THE BRAIN'S WHO FELL OUT

      JONATHAN KINLAY

      The chief executive of Investment Analytics, a mathematical research firm that develops software programmes to exploit volatile stock markets.

      He started his career at NatWest in the early 1980s but had left long before the investment bank found an £80 million black hole in its options trading book.

      After a spell at Chase Manhattan, he joined the proprietary trading desk of EMC International, a European hedge fund, specialising in privately negotiated derivatives contracts. He is well known on the lecture circuit and has taught financial engineering at Carnegie Mellon in New York and at Oxford and Cambridge.

      RON HENLEY

      Few people span the diverse worlds of chess and high finance, and fewer rise to the top of both. Ron Henley is a chess grandmaster, but he is best known for training Anatoly Karpov, the former chess world champion. His interest in derivatives dates back to 1985, when he became a member of the American Stock Exchange.

      He soon earned himself a reputation as a derivatives whizz kid, rising to become a specialist options trader for Cohen, Duffy & McGowan, one of the top options markets makers on the exchange floor. He is a regular chess commentator and runs an internet site that raises funds to sponsor young US players, including Irena Krush.

      JONATHAN KINLAY

      The chief executive of Investment Analytics, a mathematical research firm that develops software programmes to exploit volatile stock markets.

      He started his career at NatWest in the early 1980s but had left long before the investment bank found an £80 million black hole in its options trading book.

      After a spell at Chase Manhattan, he joined the proprietary trading desk of EMC International, a European hedge fund, specialising in privately negotiated derivatives contracts. He is well known on the lecture circuit and has taught financial engineering at Carnegie Mellon in New York and at Oxford and Cambridge.

      Page 2 of 2

  3. You can't blame it all on the qunats. by tjstork · · Score: 4, Informative

    Quants only produce models that act in the way that traders expect, and traders do not want bad news. I've done a small bit of modelling before and you always reach a point where there's this one number that is completely made up, and you kinda set things up so the trader makes the call. In this sense, all these models that everyone talks about are not so much as analysis tools as they are communications tools - you sorta code the insight of the trader as to how he or she thinks the market will move. It's a very human business, not one of a bunch of computers run amok. Quants that say otherwise are just full of themselves...

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    This is my sig.
    1. Re:You can't blame it all on the qunats. by florescent_beige · · Score: 5, Interesting

      ...you always reach a point where there's this one number that is completely made up...

      ***Try a sensitivity analysis using Monte Carlo techniques. That sounds hard but it isn't. Take the parameter that you have doubts about and give it a distribution (Gaussian or rectangular or something) with the mean at your best guess and the std deviation chosen to be big enough to cover the range it might reasonably vary over.

      ***Use Gaussian if you have an idea of what the parameter probably is but aren't exactly sure, rectangular if you really have no idea. A rectangular distribution says "I have no idea, the parameter could be anywhere within this particular range."

      ***Then run your analysis a million times with the parameter selected randomly from it's distribution for each run.

      ***That gives you a stochastic dataset of results. You can run simple stats on that data set to find the mean and std dev of the result value. You will then know how sensitive your results are to your poorly understood input parameter. If your 2-sigma output tells you the expected rate of return on a particular investment varies between -50% to +50% then you will know your model is pretty much useless and you will be doing a better job than the vast majority of professional analysts.

      ****Monte Carlo is great for those of us who don't care to learn the arcane minutiae of stat math. If you have a working model it takes an hour or two to extend it so you get stochastic results. Note that it's no harder to give a distribution to all your input parameters not just one. In which case you will be doing the kind of work that people who make 500 grand a year do.

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      Equine Mammals Are Considerably Smaller
    2. Re:You can't blame it all on the qunats. by tjstork · · Score: 4, Funny

      ***Use Gaussian if you have an idea of what the parameter probably is but aren't exactly sure, rectangular if you really have no idea. A rectangular distribution says "I have no idea, the parameter could be anywhere within this particular range."

      Guassian : So, make up three numbers, and assume that the middle number is -really- most likely.

      Rectangular : make up two numbers, assuming that any number in between them is good.

      In which case you will be doing the kind of work that people who make 500 grand a year do

      ROTFLOL. Yes, but they dress more nicely.

      --
      This is my sig.
    3. Re:You can't blame it all on the qunats. by hoytak · · Score: 3, Informative

      ...you always reach a point where there's this one number that is completely made up...

      This is true, but your proposed methods do not eliminate this. Yes, a sensitivity analysis can help. But the only advantage of prior distributions over parameters is that they encourage one to put all their assumptions on the table, whereas frequentist statistical methods (fixing parameters) tend to hide things. Other than that, you will always be subject to your modeling assumptions.
       

      ***Try a sensitivity analysis using Monte Carlo techniques. That sounds hard but it isn't.

      Yes, it's easy to just "do"; doing it correctly in a way that never gives you false information or gives you accurate confidence bounds can be extremely difficult. Not that it doesn't work a lot of the time, but there are dozens of gotchas that can cause the answer to be complete rubbish and no one would know without a lot of very careful math and analysis. Yes, it can be better than other methods, but only if used properly.
       

      Take the parameter that you have doubts about and give it a distribution (Gaussian or rectangular or something) with the mean at your best guess and the std deviation chosen to be big enough to cover the range it might reasonably vary over.

      Um, yes, and those parameters have a big influence on your results... For example, if you center your prior on the parameter you think it is, it is generally NOT true that the mean you get out will be even close to the value when just plugging in that parameter. Most real data in industry and finance is not subject to the natural processes that seem to turn most things Gaussian.
       

      ***Use Gaussian if you have an idea of what the parameter probably is but aren't exactly sure, rectangular if you really have no idea. A rectangular distribution says "I have no idea, the parameter could be anywhere within this particular range."

      HaHAHAHA. Can I quote that next time I teach? A bounded uniform prior, when done with Monte Carlo, often denotes MUCH stronger assumptions than does a Gaussian or t-distribution. It is basically say, "there is no chance whatsoever the parameter can be out of this range." So, you say, make your rectangle large enough. Well, that only works for undergrad stats courses, not in most of the models I've worked with or dealt with. It also breaks down phenomenally fast in higher dimensions. It may work as a hack, but I would NEVER trust such results unless there is good reason to use a bounded uniform.
       

      ****Monte Carlo is great for those of us who don't care to learn the arcane minutiae of stat math. If you have a working model it takes an hour or two to extend it so you get stochastic results. Note that it's no harder to give a distribution to all your input parameters not just one. In which case you will be doing the kind of work that people who make 500 grand a year do.

      If you don't go through the math and simply treat it as a black box, you WILL MESS monte carlo up and give false results. We need more people in that sector who really know statistics, which means mastering statistical math (and I'm curious why you think it's arcane), not just think they do and plow blindly through minefields of gotchas you never learn in undergrad stats courses. Yes, MC is a great tool; it may be a step up, but I would never trust it without having good theoretical justification that it works. On the models in the financial industry, this is much more difficult than you might think.

      See sig!!!

      --
      Does having a witty signature really indicate normality?
  4. how about reforming pay? by Anonymous Coward · · Score: 3, Interesting

    How about bringing their pay down in line with the pay of others (engineers and scientists) that do analysis of a similar level of difficulty? This is just a guess, but it would seem increased pay attracts people who want to make more money, not those that are genuinely interested in solving the problems in a field.

  5. Hang on... by grcumb · · Score: 5, Interesting

    I'm a long way from New York, so someone correct me if I'm wrong[*], but I've always understood the problem to lie more with the people feeding data into the equations, rather than with the equations themselves.

    Now, I accept that risk calculations consisted of a great deal of voodoo because, as Taleb tells us, they tended to ignore 'Black Swan' events (where the 1 in a million catastrophe wasn't going to happen just yet) and saw patterns where only chaos existed, but as I understand it, the core of the problem was simple greed: money-hungry mortgage and securities dealers deliberately feeding bad data into the system.

    So-called quants may be decidedly imperfect, but if someone's willing to game the system to make a buck, nothing the quant does can stop it.

    If Wilmott doesn't have an answer to that, I fear that his efforts will only obscure the real problem.

    --
    Crumb's Corollary: Never bring a knife to a bun fight.
    1. Re:Hang on... by Guil+Rarey · · Score: 3, Interesting

      Admittedly without reading TFA, that sounds like his point - that what "quants" should be doing is developing good empirically good heuristic models rather than wanking over what are essentially hypothetical analytical ones based on complete SWAG parameters, where the parameters supplied by salesmen will invevitably be optimistic best case ones (and that's putting it charitably).

      --
      Do not taunt Happy Fun Ball
    2. Re:Hang on... by Guil+Rarey · · Score: 4, Interesting

      You're not wrong, but I think the author referenced in the original post and you are addressed different parts of the whole problem of financial markets. The willingness of financial services salespeople - mortgage brokers, stock brokers, etc - to basically lie their asses off because there's so much money on the line is one problem.

      "Quant" analysis of financial markets is, really, another, related problem. The same moral hazard of too much money to make cutting corners worth it exists, but the basic problem here is that many "quant" models are bullshit. Quantitive models for derivative securities can be realistically valued -- if and only if the risk of the underlying primary asset has been properly assessed (along with several other critical assumptions about the marketplace for the security -- but that's the JUDGMENTAL assumption fundamentally inherent in the models.)

      Risk assessment is not actually that difficult -- insurance is built on the ability to do risk assessment. The real problem with the current financial problems were that NO ONE KNEW WHAT THE UNDERLYING PRIMARY ASSETS WERE and everyone operated on the belief that Nothing Could Ever Possibly Go Wrong (because no one could prove otherwise, because no one knew what the hell was actually going on).

      This is and was every bit as monumentally stupid an assumption in the financial realm as it is engineering, computer programming, science, or any other real-world discipline.

      I think what Wilmott is proposing is the development of models that are more reactive to real-world inputs, models that are much more Bayesian in nature in their ability to refine and revise their predictive nature based on actual events.

           

      --
      Do not taunt Happy Fun Ball
  6. The elephant in the room by owlnation · · Score: 5, Insightful

    You know, this is just tinkering. It's a way of passing the buck. It's a way of devolving blame. It MUST be the equations, or the software, or some geek or some technological prblem that caused the economics failures.

    It wasn't. It isn't.

    The reason why we have economic problems is the same old one from the beginning of time -- good old fashioned human greed.

    Equations, and new software isn't going to change that. What you need to do is ensure that the people operating systems and processes are ethical and honest. It's really that simple, and also, unfortunately, that difficult.

  7. The problem with economics is by RichMan · · Score: 4, Insightful

    The problem with economics is that is probably more a sociological study than a idealized science.

    Economics talks of supply and demand and perfect markets.
    Yet we all know the advertising and social herd behavior affect purchases much more than any real needs or demands.

  8. The One True Law of Finance by dplentini · · Score: 5, Insightful

    Nice idea, but Wilmot seems to have forgotten the most basic law of finance---nothing matters so long as you're making lots of money. Does he really think that the Quants on Wall Street and in London care about robust models and statistical significance? No! We're talking about used car salespersons in $5,000.00 suits. The financial industry is completely amoral. The only law is the law of the jungle. You can't confuse greed with a lack of quality control.

    1. Re:The One True Law of Finance by allmanbro2 · · Score: 3, Insightful

      I would say he has kept this in mind: he is making (boatloads of) money teaching people how to magically mathify finance. This is infinitely less risky than investing.

      Analogously: if you want to make money at a casino, get a job as a dealer.

  9. Why should they? by Colin+Smith · · Score: 3, Insightful

    It's your money they are paying themselves with, not their own. Until YOU sit up and take notice, then actually DO something you're going to continue to get robbed. But hey, I'm making money off you as well, so don't worry about it "nothing to see here, move along".
     

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    Deleted
  10. Theocracy of Quants by Baldrson · · Score: 4, Insightful
    Wilmott suffers from the same thing that plagues all social scientists: They can't run controlled experiments to extract causation, yet they influence public policy as though they could.

    In another time, this would have been called what it is: theocracy, rule by theory.

    Oh sure, they can try to be inductive, but there is always that old "correlation doesn't imply causation" gotcha isn't there?

    The real solution to this problem with the social sciences was almost addressed by the Protestant culture that founded the US -- the Laboratory of the States -- but the incorporation of the slave states in the 1700s, with the resulting Amendment from Hell, the 14th, in the 1800s killed off that option entirely when "social science" sunk its fangs into the body politc in the 1900s.

    "The Union" means everyone is a slave to the theocrats posing as theoreticians.

    So now we're running uncontrolled experiments on nonconsenting human subjects in the guise of "public policy" of "liberal democracy" -- tyranny of the majority limited only by a vague laundry list of selectively enforced human rights.

  11. the formula which distroyed wall street by e**(i+pi)-1 · · Score: 3, Interesting

    Mathematical models always only work in a certain range As Newtonian mechanics well for smaller velocities and macroscopic bodies it has to be replaced for large velocities or in smaller scales. Exponential growth laws have to be replaced by logistic growth. etc Models are especially popular in probability theory. The text mentions Gaussian Copula function, the "rocket fuel" for collateralized debt obligation, which is cited as one of the reasons for the finance disaster. See "The formula that killed Wall street".

  12. Picking up nickels in front of a bulldozer by turing_m · · Score: 4, Interesting

    The reason why the quants ignore Black Swan events is that they are not financially impacted by them to any real extent. They make their living from making small amounts of money using lots and lots of leverage. But I prefer Buffett's metaphor for this sort of practice: picking up nickels in front of a bulldozer.

    As long as "quants" can pick up "nickels" in front of a bulldozer for a few years, they can retire and never have to work again, even if their parent companies (and the companies they borrow from) go bankrupt. Those "nickels" are many millions, their percentage of those "nickels" are still high enough to retire on. Of course, they risk billions in the process.

    I suspect the only way to really curb the practice would be to either limit amounts of leverage or cause complete bankruptcy/imprisonment/physical harm somehow to those responsible when the bulldozer (the black swan) eventually comes along. Of course, these laws can't really be applied to those responsible for the GFC. Laws can and probably will be created, and then after a few generations those laws will be repealed as the creation of a few old fuddy duddies who didn't understand whatever "new economy" comes along, and the cycle will repeat.

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    If I have seen further it is by stealing the Intellectual Property of giants.
  13. Wall Street by hairyfeet · · Score: 3, Interesting

    Wall Street Is just Las Vegas with better clothes. All the day traders and other 'quick money" guys have rendered the idea of having an actual investment in a company because you believe they are going to do well in the future and desiring to be a part of that passe. They have also trained corporations to "damn everything but the quarterly reports!" causing long term damage and even failure to a company in return for short term profits.

    We need to get the day traders out, and the investors back in. perhaps by setting up a tax than penalizes anyone who buys stocks for very quick turnovers and rewards those that hold onto a stock for a set period. Because real long term growth of a company takes investment. Investment and the building of infrastructure, training of employees, construction of new buildings, etc and all of these things cost. In the current Wall Street model such investments show up on the quarterly report and torpedo the stock. We should legalize gambling for those that want to take a shot at the quick cash and leave investing in the growth of businesses to investors that are willing to look at the long term picture, not simply the quarterly report.

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    ACs don't waste your time replying, your posts are never seen by me.
  14. what quants know by Erastus · · Score: 3, Interesting

    I think there is a misperception that quants just run wild with models with catastrophic results and that they are naive when it comes to practical matters. However, quants are also taught about "model risk" to include things like: wrong assumptions, poor estimation of parameters, errors in discretization, etc. Let's also not forget the positive social value of financial innovation. It helps you borrow at lower rates, pay less for insurance, etc. There were a few problems that led to this financial crisis and I think quants played a relatiely minor role.