Arbeitspapier
A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations
We propose a new class of observation-driven time-varying parameter models for dynamic volatilities and correlations to handle time series from heavy-tailed distributions. The model adopts generalized autoregressive score dynamics to obtain a time-varying covariance matrix of the multivariate Student's t distribution. The key novelty of our proposed model concerns the weighting of lagged squared innovations for the estimation of future correlations and volatilities. When we account for heavy tails of distributions, we obtain estimates that are more robust to large innovations. The model also admits a representation as a time-varying heavy-tailed copula which is particularly useful if the interest focuses on dependence structures. We provide an empirical illustration for a panel of daily global equity returns.
- Sprache
-
Englisch
- Erschienen in
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Series: Tinbergen Institute Discussion Paper ; No. 10-032/2
- Klassifikation
-
Wirtschaft
Econometric and Statistical Methods and Methodology: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Model Construction and Estimation
- Thema
-
dynamic dependence
multivariate Student's t distribution
copula
Statistische Verteilung
Kopula (Mathematik)
Zeitreihenanalyse
Theorie
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Creal, Drew
Koopman, Siem Jan
Lucas, André
- Ereignis
-
Veröffentlichung
- (wer)
-
Tinbergen Institute
- (wo)
-
Amsterdam and Rotterdam
- (wann)
-
2010
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Creal, Drew
- Koopman, Siem Jan
- Lucas, André
- Tinbergen Institute
Entstanden
- 2010