Using HMM Approach for Assessing Quality of Value at Risk Estimation: Evidence from PSE Listed Company

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F17%3A43912765" target="_blank" >RIV/62156489:43110/17:43912765 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.11118/actaun201765051687" target="_blank" >https://doi.org/10.11118/actaun201765051687</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.11118/actaun201765051687" target="_blank" >10.11118/actaun201765051687</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Using HMM Approach for Assessing Quality of Value at Risk Estimation: Evidence from PSE Listed Company

Popis výsledku v původním jazyce

The article points out the possibilities of using Hidden Markov model (abbrev. HMM) for estimation of Value at Risk metrics (abbrev. VaR) in sample. For the illustration we use data of the company listed on Prague Stock Exchange in range from January 2011 to June 2016. HMM approach allows us to classify time series into different states based on their development characteristic. Due to a deeper shortage of existing domestic results or comparison studies with advanced volatility governed VaR forecasts we tested HMM with univariate ARMA-GARCH model based VaR estimates. The common testing via Kupiec and Christoffersen procedures offer generalization that HMM model performs better that volatility based VaR estimation technique in terms of accuracy, even with the simpler HMM with normal-mixture distribution against previously used GARCH with many types of non-normal innovations.

Název v anglickém jazyce

Using HMM Approach for Assessing Quality of Value at Risk Estimation: Evidence from PSE Listed Company

Popis výsledku anglicky

The article points out the possibilities of using Hidden Markov model (abbrev. HMM) for estimation of Value at Risk metrics (abbrev. VaR) in sample. For the illustration we use data of the company listed on Prague Stock Exchange in range from January 2011 to June 2016. HMM approach allows us to classify time series into different states based on their development characteristic. Due to a deeper shortage of existing domestic results or comparison studies with advanced volatility governed VaR forecasts we tested HMM with univariate ARMA-GARCH model based VaR estimates. The common testing via Kupiec and Christoffersen procedures offer generalization that HMM model performs better that volatility based VaR estimation technique in terms of accuracy, even with the simpler HMM with normal-mixture distribution against previously used GARCH with many types of non-normal innovations.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis

ISSN

1211-8516

e-ISSN

—

Svazek periodika

65

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

8

Strana od-do

1687-1694

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85042848371