Statistical Consequences of Fat Tails
Statistical Consequences of Fat Tails
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Statistical Consequences of Fat Tails by Nassim Nicholas Taleb
The book investigates the misapplication of conventional statistical techniques to fat tailed distributions and looks for remedies, when possible. Switching from thin tailed to fat tailed distributions requires more than changing the color of the dress. Traditional asymptotics deal mainly with either n=1 or n=∞, and the real world is in between, under the laws of the medium numbers-which vary widely across specific distributions. Both the law of large numbers and the generalized central limit mechanisms operate in highly idiosyncratic ways outside the standard Gaussian or Levy-Stable basins of convergence. A few examples: - The sample mean is rarely in line with the population mean, with effect on na ve empiricism, but can be sometimes be estimated via parametric methods. - The empirical distribution is rarely empirical. - Parameter uncertainty has compounding effects on statistical metrics. - Dimension reduction (principal components) fails. - Inequality estimators (Gini or quantile contributions) are not additive and produce wrong results. - Many biases found in psychology become entirely rational under more sophisticated probability distributions. - Most of the failures of financial economics, econometrics, and behavioral economics can be attributed to using the wrong distributions. This book, the first volume of the Technical Incerto, weaves a narrative around published journal articles.| SKU | Unavailable |
| ISBN 13 | 9781544508054 |
| ISBN 10 | 1544508050 |
| Title | Statistical Consequences of Fat Tails |
| Author | Nassim Nicholas Taleb |
| Series | Technical Incerto |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | STEM Academic Press |
| Year published | 2020-06-30 |
| Number of pages | 446 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |