Another view on time-varying correlations: The case of stocks and bonds

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F13%3A10158999" target="_blank" >RIV/00216208:11320/13:10158999 - isvavai.cz</a>

Výsledek na webu

<a href="https://mme2013.vspj.cz/about-conference/conference-proceedings" target="_blank" >https://mme2013.vspj.cz/about-conference/conference-proceedings</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Another view on time-varying correlations: The case of stocks and bonds

Popis výsledku v původním jazyce

The aim of the contribution is to introduce an innovative approach to conditional covariance and correlation modelling. This can be obviously useful in multivariate financial time series analysis, e.g. in the multivariate GARCH context. The proposed method consists of two steps. The first one is based on the LDL factorization of the conditional covariance matrix, state space modelling and associated Kalman recursions. Moreover, it is able to deliver a dynamic orthogonal transformation of given stochastic vector data. The second step of the suggested technique analyses conditional covariances of transformed time series which is indeed simplified due to its simultaneously uncorrelated components. In the paper, performance of the introduced procedure is tested in an empirical financial framework. Namely, the daily correlation links between logarithmic returns on stocks and bonds are investigated and compared with other estimated dynamic correlations gained by several common methods, e.g.

Název v anglickém jazyce

Another view on time-varying correlations: The case of stocks and bonds

Popis výsledku anglicky

The aim of the contribution is to introduce an innovative approach to conditional covariance and correlation modelling. This can be obviously useful in multivariate financial time series analysis, e.g. in the multivariate GARCH context. The proposed method consists of two steps. The first one is based on the LDL factorization of the conditional covariance matrix, state space modelling and associated Kalman recursions. Moreover, it is able to deliver a dynamic orthogonal transformation of given stochastic vector data. The second step of the suggested technique analyses conditional covariances of transformed time series which is indeed simplified due to its simultaneously uncorrelated components. In the paper, performance of the introduced procedure is tested in an empirical financial framework. Namely, the daily correlation links between logarithmic returns on stocks and bonds are investigated and compared with other estimated dynamic correlations gained by several common methods, e.g.

Druh

D - Stať ve sborníku

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

—

Projekt

<a href="/cs/project/GBP402%2F12%2FG097" target="_blank" >GBP402/12/G097: DYME-Dynamické modely v ekonomii</a><br>

Návaznosti

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

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 31st International Conference Mathematical Methods in Economics 2013

ISBN

978-80-87035-76-4

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

249-254

Název nakladatele

College of Polytechnics Jihlava

Místo vydání

Jihlava

Místo konání akce

Jihlava

Datum konání akce

11. 9. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—