Appendix B: Continuous Probability Distributions Commonly Used in Financial Econometrics (original) (raw)

Heavy-Tailed Distributions: Data, Diagnostics, and New Developments

SSRN Electronic Journal, 2000

This monograph is written for the numerate nonspecialist, and hopes to serve three purposes. First it gathers mathematical material from diverse but related fields of order statistics, records, extreme value theory, majorization, regular variation and subexponentiality. All of these are relevant for understanding fat tails, but they are not, to our knowledge, brought together in a single source for the target readership. Proofs that give insight are included, but for fussy calculations the reader is referred to the excellent sources referenced in the text. Multivariate extremes are not treated. This allows us to present material spread over hundreds of pages in specialist texts in twenty pages. Chapter 5 develops new material on heavy tail diagnostics and gives more mathematical detail.

Heavy Tailed Distributions in Finance: Reality or Myth? Amateurs Viewpoint

The purpose of this paper is to show that the use of heavy-tailed distributions in Financial problems is theoretically baseless and can lead to significant misunderstandings. The reason for this the authors see in an incorrect interpretation of the concept of the distributional tail. In accordance with this, in applications it is necessary to use instead of the distributional tail the "smearing" of its central part. keywords: heavy tailed distributions, exponential tails, pre-limit theorems, Financial indexes

Measuring heavy-tailedness of distributions

Different questions related with analysis of extreme values and outliers arise frequently in practice. To exclude extremal observations and outliers is not a good decision, because they contain important information about the observed distribution. The difficulties with their usage are usually related with the estimation of the tail index in case it exists. There are many measures for the center of the distribution, e.g. mean, mode, median. There are many measures for the variance, asymmetry and kurtosis, but there is no easy characteristic for heavy-tailedness of the observed distribution. Here we propose such a measure, give some examples and explore some of its properties. This allows us to introduce classification of the distributions, with respect to their heavy-tailedness. The idea is to help and navigate practitioners for accurate and easier work in the field of probability distributions. Using the properties of the defined characteristics some distribution sensitive extremal index estimators are proposed and their properties are partially investigated.

A useful family of fat-tailed distributions

2022

It is argued that there is a need for fat-tailed distributions that become thin in the extreme tail. A 3-parameter distribution is introduced that visually resembles the t-distribution and interpolates between the normal distribution and the Cauchy distribution. It is fat-tailed, but has all moments finite, and the moment-generating function exists. It would be useful as an alternative to the t-distribution for a sensitivity analysis to check the robustness of results or for computations where finite moments are needed, such as in option-pricing. It can be motivated probabilistically in at least two ways, either as the random thinning of a long-tailed distribution, or as random variation of the variance of a normal distribution. Its properties are described, algorithms for random-number generation are provided, and examples of its use in data-fitting given. Some related distributions are also discussed, including asymmetric and multivariate distributions.

A new class of models for heavy tailed distributions in finance and insurance risk

Insurance: Mathematics and Economics, 2012

Many insurance loss data are known to be heavy-tailed. In this article we study the class of Log phase-type (LogPH) distributions as a parametric alternative in fitting heavy tailed data. Transformed from the popular phase-type distribution class, the LogPH introduced by Ramaswami exhibits several advantages over other parametric alternatives. We analytically derive its tail related quantities including the conditional tail moments and the mean excess function, and also discuss its tail thickness in the context of extreme value theory. Because of its denseness proved herein, we argue that the LogPH can offer a rich class of heavy-tailed loss distributions without separate modeling for the tail side, which is the case for the generalized Pareto distribution (GPD). As a numerical example we use the well-known Danish fire data to calibrate the LogPH model and compare the result with that of the GPD. We also present fitting results for a set of insurance guarantee loss data.

Fat Tail Statistics and Beyond

Based on data from three different systems, namely, turbulence, financial market and surface roughness we discuss methods to analyze their complexities. Scaling analysis and fat tail statistics in the context of Lévy distributions are compared with a stochastic method, for which a Fokker-Planck equation can be estimated from data. We show that the last method provides a more detailed characterization of complexity.

How Much Data Do You Need? An Operational Metric for Fat-tailedness

— This note presents an operational measure of fat-tailedness for univariate probability distributions, in [0, 1] where 0 is maximally thin-tailed (Gaussian) and 1 is maximally fat-tailed. It is based on "how much data one needs to make meaningful statements about a given dataset?" Applications: Among others, it

Robust measures of tail weight

Computational Statistics & Data Analysis, 2006

The kurtosis coefficient is often regarded as a measure of the tail heaviness of a distribution relative to that of the normal distribution. However, it also measures the peakedness of a distribution, hence there is no agreement on what kurtosis really estimates. Another disadvantage of the kurtosis is that its interpretation and consequently its use is restricted to symmetric distributions. Moreover, the kurtosis coefficient is very sensitive to outliers in the data. To overcome these problems, several measures of left and right tail weight for univariate continuous distributions are proposed. They can be applied to symmetric as well as asymmetric distributions that do not need to have finite moments. Their interpretation is clear and they are robust against outlying values. The breakdown value and the influence functions of these measures and the resulting asymptotic variances are discussed and used to construct goodness-of-fit tests. Simulated as well as real data are employed for further comparison of the proposed measures.