mandelbrotian randomness in finance, examples of practical uses?

[DcifrThs]

i just ordered a few books on this but want to see if anybody has seen more empirical (though not necessarily precise) practical uses of scalable distributions for the purposes of trading, investing, portfolio construction, etc.

any clarifying questions, please ask.

thanks,
Barron

[hawk59]

dcfr,

what type of trading do you do?

[DcifrThs]

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dcfr,

what type of trading do you do?

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no particular type, i get my kicks learning.

to be more specific: i just want to learn about how to apply varying tools to the imprecise art + science of financial market research. this one seems key and after reading 95% of the black swan and reading some philosophical works by benoit mandelbrot, i think this will soon be the next wave of financial market analysis. if not, it will still give me a better idea than my competition into varying methodologies available to the astute mind.

so i'd love eto hear from anybody who knows of or could link/recommend some real time type experiments or any type of application of this stuff.

thanks,

Barron

[eastbay]

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dcfr,

what type of trading do you do?

[/ QUOTE ]

no particular type, i get my kicks learning.

to be more specific: i just want to learn about how to apply varying tools to the imprecise art + science of financial market research. this one seems key

[/ QUOTE ]

Based on what?

eastbay

[DcifrThs]

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[ QUOTE ]
dcfr,

what type of trading do you do?

[/ QUOTE ]

no particular type, i get my kicks learning.

to be more specific: i just want to learn about how to apply varying tools to the imprecise art + science of financial market research. this one seems key

[/ QUOTE ]

Based on what?

eastbay

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i dont' really know what you're asking here. are you asking what my "this is key" assumption is based on as it relates to using varying tools to analyze financial markets?

or are you asking about the "key" assumption as it relates to the actual tools i'm looking to learn.

i'll assume from here on out that it is the latter b/c the former seems fairly obvious.

first there seem to be more books popping up on the subject. the academic literature seems to be prodding slowly in that direction. further, we are experiencing an utterly widespread misuse (though not disillusionment) of financial models that apply notions from the more normally distrubuted physical sciences to those of the jumpy financial markets. note that while the misuse offers returns, irrespective of large blowups, they'll probably continue to be used. we aren't sold portfolio insurance anymore, but we are still taught the options pricing that was its brainchild.

i remember moving through my math-fin courses thinking, "hey, this mumbo jumbo seems to work, except when it doesn't...but that can't be too big a deal, can it?"

specifically, think about brownian motions. this is a type of randomness generating process that is "the" underpinning of scholes et. al.'s elegant world of options pricing. it is the basis on how the prices of risky assets are assumed to move in time. it is ubiquitous in interest rate models & term structure of volatility models.

but, it has it's initial home in the physical sciences (IIRC it has to do with how particles dissipate in different environments). the resulting (mis)use of it leads to audacious statements like those that came out of LTCM's shareholder letters (predicting with 100% precision the probability of losing $X/mo/year/5year periods).

it was (and still is) used with complete faith regarding its ability to mimic moves of risky assets& thus price them based on that voaltility. i was shown nice time period graphs of how this normally-esque distributed randomness produced return streams that looked like those of risky assets.

it seems now to be becomming more accepted (at a VERY VERY early stage) that our current mathematical models aren't robust to reality.

MBAs will still teach this stuff (for those who elect to take it) & MS Fin students will need to understand it. PhDs are going to publish paper after paper applying it as it has been applied (or coming up w/ new and itneresting applications)....but not 10-20yrs from now (if i had to guess).

thus, i want to learn as much about it as possible.

i'm not throwing out the old methodologies, i'm just becomming more aware of their limitations and would like to add more tools to my arsenal of understanding.

Barron

[RicoTubbs]

[ QUOTE ]
but, it has it's initial home in the physical sciences (IIRC it has to do with how particles dissipate in different environments). the resulting (mis)use of it leads to audacious statements like those that came out of LTCM's shareholder letters (predicting with 100% precision the probability of losing $X/mo/year/5year periods).

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Barron,
What makes you so sure that the probabilities were wrong or that the LTCM guys were not capable of calculating them?

[eastbay]

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dcfr,

what type of trading do you do?

[/ QUOTE ]

no particular type, i get my kicks learning.

to be more specific: i just want to learn about how to apply varying tools to the imprecise art + science of financial market research. this one seems key

[/ QUOTE ]

Based on what?

eastbay

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i dont' really know what you're asking here.


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You said "mandelbrotian randomness" in finance seems key. I'm asking why you think that.

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first there seem to be more books popping up on the subject.


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In the early 90's there was a rash of books about aliens at Area 51. Did that mean it was more likely that aliens were there then? (only partially tongue in cheek.)

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the academic literature seems to be prodding slowly in that direction.


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for example?

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further, we are experiencing an utterly widespread misuse (though not disillusionment) of financial models that apply notions from the more normally distrubuted physical sciences to those of the jumpy financial markets.


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It is not a new idea that distributions in finance are "fat tailed" and not Gaussian.

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specifically, think about brownian motions. this is a type of randomness generating process that is "the" underpinning of scholes et. al.'s elegant world of options pricing. it is the basis on how the prices of risky assets are assumed to move in time. it is ubiquitous in interest rate models & term structure of volatility models.


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It's called a model. All models are approximate and based on idealized assumptions. This is not a revelation.

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it was (and still is) used with complete faith regarding its ability to mimic moves of risky assets& thus price them based on that voaltility.


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Anybody who uses any model with "complete faith" is an idiot and not worth talking about.

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it seems now to be becomming more accepted (at a VERY VERY early stage) that our current mathematical models aren't robust to reality.


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I don't know what "robust to reality" means, but I don't think it's news to anyone that normal distributions are not often a good model in finance.

Maybe I should start with a simpler question: What do you mean by "Mandelbrotian randomness"?

eastbay

[hawk59]

ok maybe i should ask then do you really think all this stuff is out there because academics want to sound smart or because there is a practical way to make money off of it. you know it's possible to just buy undervalued stocks with a minimum of complication.

[DcifrThs]

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ok maybe i should ask then do you really think all this stuff is out there because academics want to sound smart or because there is a practical way to make money off of it. you know it's possible to just buy undervalued stocks with a minimum of complication.

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if i were to want to manage my own portfolio then i'd look more into value investing.

however, i'm looking to work at some of the most competitive places out there where equities are a wee tiny part of the overall portfolio simply b/c you run out of undervalued stocks to buy (unless you're private equity) w/ $30billion.

the techniques used are very quant heavy adn i'd like to be more than prepared should anything come up w/ an interview. one question would be like "so what do you find interesting?"

also, the more i know about the structure of the marketplace, the more i'll feel comfortable analyzing it.

hope that makes sense,
Thanks,
Barron

[DcifrThs]

Ok, this thread got WAAAYYY off track.

lets bring it back.

the books i ordered are the following:

1) the (mis) behavior or markets: a fractal view of risk, ruin & reward

2) Fractals & scaling in finance by benoit mandelbrot

the first is like a 15 page essay and the 2nd is a textbook w/ both mathematical & non-mathematical treatments on the subject.

the journal of finance had not one reference of "fractal" "non-scalable" "scale invariant" etc. they had about ten million references for "volatility" though.

i've read tons of stuff on NNT's homepage and a lot of good info comes from this article:

Page 1

Page 2

enjoy!

lets get some more thoughts or sources! please!!!

thanks,
Barron

PS- if there are any other Qs about anything then just bump the "ask dcifrths" thread from a while ago