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mt_christo
mt_christo
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There's been a huge amount of literature written on the subject, Google is full of it. Why heavy tails are not mainstream in basic modeling? That's not because thethese models are "too complex", but mostly because they are not obvious. And risk management and portfolio techniques need an obvious (linear etc.) model to build on that, otherwise very little can be done for the dollar spent on the analysis. "Heavy tailed" models are a huuuge family, I would compare their difference vs. Gaussian to the difference between processes with memory andvs. Markov chains - the two families simply cannot be compared. The first type has thousands of concepts of the second type in it.

Please approach the Stable family with caution - from my experience, it doesn't precisely model real series (but, of course, better than Gauss). A composition (like different laws for the middle term and the tail - like multiplied by some exponential decay, different power laws, etc.) often works better. Of course, you can't toss these distributions around anymore, like you do with Gaussian.

There's been a huge amount of literature written on the subject, Google is full of it. Why heavy tails are not mainstream in basic modeling? That's not because the models are "too complex", but mostly because they are not obvious. And risk management and portfolio techniques need an obvious (linear etc.) model to build on that, otherwise very little can be done for the dollar spent on the analysis. "Heavy tailed" models are a huuuge family, I would compare their difference vs. Gaussian to the difference between processes with memory and Markov chains - the two families simply cannot be compared. The first type has thousands of concepts of the second type in it.

Please approach the Stable family with caution - from my experience, it doesn't precisely model real series (but, of course, better than Gauss). A composition (like different laws for the middle term and the tail - like multiplied by some exponential decay, different power laws, etc.) often works better. Of course, you can't toss these distributions around anymore, like you do with Gaussian.

There's been a huge amount of literature written on the subject, Google is full of it. Why heavy tails are not mainstream in basic modeling? That's not because these models are "too complex", but mostly because they are not obvious. And risk management and portfolio techniques need an obvious (linear etc.) model to build on that, otherwise very little can be done for the dollar spent on the analysis. "Heavy tailed" models are a huuuge family, I would compare their difference vs. Gaussian to the difference between processes with memory vs. Markov chains - the two families simply cannot be compared. The first type has thousands of concepts of the second type in it.

Please approach the Stable family with caution - from my experience, it doesn't precisely model real series (but, of course, better than Gauss). A composition (like different laws for the middle term and the tail - like multiplied by some exponential decay, different power laws, etc.) often works better. Of course, you can't toss these distributions around anymore, like you do with Gaussian.

Source Link
mt_christo
mt_christo
  • 93
  • 1
  • 6

There's been a huge amount of literature written on the subject, Google is full of it. Why heavy tails are not mainstream in basic modeling? That's not because the models are "too complex", but mostly because they are not obvious. And risk management and portfolio techniques need an obvious (linear etc.) model to build on that, otherwise very little can be done for the dollar spent on the analysis. "Heavy tailed" models are a huuuge family, I would compare their difference vs. Gaussian to the difference between processes with memory and Markov chains - the two families simply cannot be compared. The first type has thousands of concepts of the second type in it.

Please approach the Stable family with caution - from my experience, it doesn't precisely model real series (but, of course, better than Gauss). A composition (like different laws for the middle term and the tail - like multiplied by some exponential decay, different power laws, etc.) often works better. Of course, you can't toss these distributions around anymore, like you do with Gaussian.