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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    50202 - Applied Economics, Econometrics

Result continuities

  • Project

  • 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

  • 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