Volatility filtering in estimation of kurtosis (and variance)
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985998%3A_____%2F19%3A00505006" target="_blank" >RIV/67985998:_____/19:00505006 - isvavai.cz</a>
Result on the web
<a href="https://www.degruyter.com/downloadpdf/j/demo.2019.7.issue-1/demo-2019-0001/demo-2019-0001.pdf" target="_blank" >https://www.degruyter.com/downloadpdf/j/demo.2019.7.issue-1/demo-2019-0001/demo-2019-0001.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1515/demo-2019-0001" target="_blank" >10.1515/demo-2019-0001</a>
Alternative languages
Result language
angličtina
Original language name
Volatility filtering in estimation of kurtosis (and variance)
Original language description
The kurtosis of the distribution of financial returns characterized by high volatility persistence and thick tails is notoriously difficult to estimate precisely. We propose a simple but effective procedure of estimating the kurtosis coefficient (and variance) based on volatility filtering that uses a simple GARCH model. In addition to an estimate, the proposed algorithm issues a signal of whether the kurtosis (or variance) is finite or infinite. We also show how to construct confidence intervals around the proposed estimates. Simulations indicate that the proposed estimates are much less median biased than the usual method-of-moments estimates, their confidence intervals having much more precise coverage probabilities. The procedure alsoworks well when the underlying volatility process is not the one the filtering technique is based on. We illustrate how the algorithm works using several actual series of returns.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50202 - Applied Economics, Econometrics
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Dependence Modeling
ISSN
2300-2298
e-ISSN
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Volume of the periodical
7
Issue of the periodical within the volume
1
Country of publishing house
PL - POLAND
Number of pages
23
Pages from-to
1-23
UT code for WoS article
000459213300001
EID of the result in the Scopus database
2-s2.0-85062242090