Volatility filtering in estimation of kurtosis (and variance)

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Volatility filtering in estimation of kurtosis (and variance)

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Volatility filtering in estimation of kurtosis (and variance)

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50202 - Applied Economics, Econometrics

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Dependence Modeling

ISSN

2300-2298

e-ISSN

—

Svazek periodika

7

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

PL - Polská republika

Počet stran výsledku

23

Strana od-do

1-23

Kód UT WoS článku

000459213300001

EID výsledku v databázi Scopus

2-s2.0-85062242090