Disentangling systematic and idiosyncratic dynamics in panels of volatility measures

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11230%2F14%3A10295822" target="_blank" >RIV/00216208:11230/14:10295822 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.jeconom.2014.05.017" target="_blank" >http://dx.doi.org/10.1016/j.jeconom.2014.05.017</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jeconom.2014.05.017" target="_blank" >10.1016/j.jeconom.2014.05.017</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Disentangling systematic and idiosyncratic dynamics in panels of volatility measures

Popis výsledku v původním jazyce

Realized volatilities observed across several assets show a common secular trend and some idiosyncratic pattern which we accommodate by extending the class of Multiplicative Error Models (MEMs). In our model, the common trend is estimated nonparametrically, while the idiosyncratic dynamics are assumed to follow univariate MEMs. Estimation theory based on seminonparametric methods is developed for this class of models for large cross-sections and large time dimensions. The methodology is illustrated using two panels of realized volatility measures between 2001 and 2008: the SPDR Sectoral Indices of the S&P500 and the constituents of the S&P100. Results show that the shape of the common volatility trend captures the overall level of risk in the market and that the idiosyncratic dynamics have a heterogeneous degree of persistence around the trend. Out-of-sample forecasting shows that the proposed methodology improves volatility prediction over several benchmark specifications.

Název v anglickém jazyce

Disentangling systematic and idiosyncratic dynamics in panels of volatility measures

Popis výsledku anglicky

Realized volatilities observed across several assets show a common secular trend and some idiosyncratic pattern which we accommodate by extending the class of Multiplicative Error Models (MEMs). In our model, the common trend is estimated nonparametrically, while the idiosyncratic dynamics are assumed to follow univariate MEMs. Estimation theory based on seminonparametric methods is developed for this class of models for large cross-sections and large time dimensions. The methodology is illustrated using two panels of realized volatility measures between 2001 and 2008: the SPDR Sectoral Indices of the S&P500 and the constituents of the S&P100. Results show that the shape of the common volatility trend captures the overall level of risk in the market and that the idiosyncratic dynamics have a heterogeneous degree of persistence around the trend. Out-of-sample forecasting shows that the proposed methodology improves volatility prediction over several benchmark specifications.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

AH - Ekonomie

OECD FORD obor

—

Projekt

<a href="/cs/project/GA13-24313S" target="_blank" >GA13-24313S: Vlnková analýza nestacionárních časových řad a časových řad s dlouhou pamětí</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název periodika

Journal of Econometrics

ISSN

0304-4076

e-ISSN

—

Svazek periodika

182

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

21

Strana od-do

364-384

Kód UT WoS článku

000340335400008

EID výsledku v databázi Scopus

2-s2.0-84905020370