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I would be much more interested if this product were "choose probability density function centric." Then, the Monte Carlo engine would gain much more interest.

by aj7 11y ago

I would be much more interested if this product were "choose probability density function centric." Then, the Monte Carlo engine would gain much more interest. Being able to choose or specify arbitrary distributions, and then run simulations, would be valuable. Of special interest are non-continuous distributions. How often have normal distribution reasoning failed in finance? Put another way, a user should be able to model a distribution himself.

ozgooen 11y ago
Very good to know. Right now you can choose between normal, uniform, and a few very simple discreet distributions, but not others. When I built this, my first goal was to make any distribution run quickly. At this point I believe adding other distribution types will be quite doable, expect them shortly.
- thanatropism 11y ago
  Two words: beta distribution. It has finite range and an arbitrary mode, and has the uniform as a special case.
blablabla123 11y ago
Statistical reasoning in general has failed in finance, especially 2008+... I don't think statistics or normal distributions are to blame. Rather the mindset that the risk scenarios are something that is avoidable with certainty. That's almost religious...
stdbrouw 11y ago
Guesstimate is napkin math. It makes no sense to spend an inordinate amount of time fine-tuning the distribution when both the distribution and its parameters are just best guesses. The important part is the propagation of uncertainty across many dependent variables, and the normal distribution is often good enough for that purpose. Whenever it isn't, for me that'd be a sign to use a proper statistical model instead. MCMC was invented to do inference on models of arbitrary complexity, with however much or little data you might have.
perlgeek 11y ago
Why would you need Monte Carlo? Can't you combine probability density functions through convolutions (or other tricks with integrals, like Fourier or Laplace transforming and then using straight arithmetic)?

by aj7 11y ago

ozgooen 11y ago

Very good to know. Right now you can choose between normal, uniform, and a few very simple discreet distributions, but not others. When I built this, my first goal was to make any distribution run quickly. At this point I believe adding other distribution types will be quite doable, expect them shortly.

thanatropism 11y ago
Two words: beta distribution. It has finite range and an arbitrary mode, and has the uniform as a special case.

blablabla123 11y ago

Statistical reasoning in general has failed in finance, especially 2008+... I don't think statistics or normal distributions are to blame. Rather the mindset that the risk scenarios are something that is avoidable with certainty. That's almost religious...

stdbrouw 11y ago

Guesstimate is napkin math. It makes no sense to spend an inordinate amount of time fine-tuning the distribution when both the distribution and its parameters are just best guesses. The important part is the propagation of uncertainty across many dependent variables, and the normal distribution is often good enough for that purpose. Whenever it isn't, for me that'd be a sign to use a proper statistical model instead. MCMC was invented to do inference on models of arbitrary complexity, with however much or little data you might have.

perlgeek 11y ago

Why would you need Monte Carlo? Can't you combine probability density functions through convolutions (or other tricks with integrals, like Fourier or Laplace transforming and then using straight arithmetic)?