υp-strictly stable laws and estimation of their parameters

Klebanov, Lev B.; Melamed, J. A.; Manija, C. M.

doi:10.1007/bfb0072707

Search citation statements

Order By: Relevance

Paper Sections

Select...

Fifth Argument1

Introduction and Definitions1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

1998

2023

Publication Types

Select...

Other3

Article2

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 5 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The number of stocks in a portfolio is also a random variable. Notice that the analogues of normal and stable random variables were implemented and studied in [15], [16], [17], [12]. These distributions differ from the classic stable distributions, but have the property of stability (in which instead of the sum of random variables is used the sum of a random number of components).…”

Section: Fifth Argumentmentioning

confidence: 99%

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

Klebanov,

Volchenkova

2015

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Fifth Argumentmentioning

confidence: 99%

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

Klebanov,

Volchenkova

2015

Preprint

View full text Add to dashboard Cite

show abstract

“…On the other hand, there exist a large literature on financial applications of stable and geometric stable distributions in Finance (see, for example, [5]). Geometric stable distributions were introduced in [6] and are analogues of classical stable distributions for the case of sums of a random number of random variables, where the number of summands has geometric distribution. The case of general distribution of the number of summands was studied in [7] (see also [8]).…”

Section: Introduction and Definitionsmentioning

confidence: 99%