Modeling heavy tail property of financial asset returns with skewed generalized t– distribution
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F18%3A00326716" target="_blank" >RIV/68407700:21340/18:00326716 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Modeling heavy tail property of financial asset returns with skewed generalized t– distribution
Original language description
The heavy tail property of financial asset returns is well known and it can be modeled by alpha stable distributions or some distribution from the generalized hyperbolic distribution family. However, the first group may often have too heavy tails and the second group exhibits too light ends at times as they are sometime called semi-heavy tail. In this paper, we examine the ability of several distributions from the skewed generalized t-distribution class to capture the fat tail feature of a group of stock market indices. The chosen distributions form the group are skewed generalized t-distribution, and its special cases: generalized t-distribution, skewed t-distribution and t-distribution. In our analysis, first we use maximum likelihood estimation method to estimate parameters of each distribution from data.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
36th International Conference Mathemtical Methods in Economics: Conference Proceedings
ISBN
978-80-7378-371-6
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
282-287
Publisher name
MATFYZPRESS, vydavatelství Matematicko-fyzikální fakulty UK
Place of publication
Praha
Event location
Jindřichův Hradec
Event date
Sep 12, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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