A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations

By Drew D. Creal, S. J. Koopman, André Lucas. 2011

307 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/6d0e8af5b3ffc3b62f2d60cd1f09ee0b6dc10df8

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Citations in the corpus

Estimation of Affine Term Structure Models with Spanned or Unspanned Stochastic Volatility (2014, methodology): “Nevertheless, we show how the intuition behind the concentrated likelihood for spanned models can be extended to estimate USV models using the EM algorithm of Dempster et al. (1977). The maximization step of the EM algorithm solves a similar problem as optimizing the likelihood function of a spanned”
GENERALIZED AUTOREGRESSIVE SCORE MODELS WITH APPLICATIONS (2013, unknown): “…the GAS(1, 1) specification for the conditional variance leads to the updating equation ft+1 = ω + A1 · ( 1 + 3ν−1 ) · ( (1 + ν−1) (1− 2ν−1)(1 + ν−1y2t /(1− 2ν−1) ft) y2t − ft ) +B1ft, (8) see also Creal, Koopman, and Lucas (2010) for the full multivariate case and further generalizations.”
The effects of a “black swan” event (COVID-19) on herding behavior in cryptocurrency markets (2021, unknown): “The evolution in the time-varying parameter vector ht can be specified as follows (see Creal et al., 2011, 2013 for technical details): htþ1 ¼ jþ Ast þ Bht ð14Þ where j;A and B are matrices that contains coefficients, particularly, jandA control for the level and the persistence of the mean…”
Maximum Likelihood Estimation for Generalized Autoregressive Score Models (2014, unknown): “We present our results for the class of generalized autoregressive score (GAS) models which are developed by Creal et al. (2011, 2013) and Harvey (2013).”
Information Theoretic Optimality of Observation Driven Time Series Models (2014, unknown): “Creal, Koopman, and Lucas (2011) adopt the score framework to specify a multivariate heavy-tailed model for time-varying volatilities and correlations.”
New HEAVY Models for Fat-Tailed Realized Covariances and Returns (2017, methodology): “Unlike Creal, Koopman, and Lucas (2011), Lucas, Schwaab, and Zhang (2014), and Hansen, Janus, and Koopman (2016), our score is a matrix-valued rather than a vector-valued variable.”
Time‐series models with an EGB2 conditional distribution (2014, unknown): “This model belongs to the exponential generalized autoregressive heteroscedasticity (EGARCH) class and is complementary to the Student-t EGARCH model discussed by Creal et al. (2011) and Harvey (2013, Ch. 4).”
Long Memory Dynamics for Multivariate Dependence under Heavy Tails (2014, methodology): “Third, by employing the generalized autoregressive score (GAS) framework of Creal, Koopman, and Lucas (2011), we have shown that the correlation and volatility estimates are more robust to incidental extreme observations when considering Student’s t copula and marginal density functions,…”
Volatility Modeling with a Generalized t Distribution (2017, unknown): “Models in which the dynamics are driven by the conditional score have been developed by Creal, Koopman, and Lucas (2011, 2013), where they are called generalized autoregressive score models, and Harvey (2013), where they are called dynamic conditional score (DCS) models.”
Maximum likelihood estimation for score-driven models (2021, methodology): “To elucidate on how the theory as developed in this paper may be applied to a realistic case, we adopt the leading example of a score-driven time-varying volatility model of Creal et al. (2011, 2013).”
Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models (2016, unknown): “Typical examples are the generalised autoregressive conditional heteroscedasticity (GARCH) model of Engle (1982) and Bollerslev (1987), the autoregressive conditional duration (ACD) model of Engle and Russell (1998), and the multiplicative error models of Engle and Gallo (2006).”
Spillover Dynamics for Systemic Risk Measurement Using Spatial Financial Time Series Models (2016, unknown): “To describe the dynamics of ft, we adopt the autoregressive score framework of Creal et al. (2011, 2013) and Harvey (2013). The score framework for time-varying parameters has been adopted successfully in a range of different empirical settings, including the multivariate volatility model of Creal e”
Time‐Varying Transition Probabilities for Markov Regime Switching Models (2017, methodology): “For example, Creal et al. (2011) and Lucas et al. (2014) study dynamic volatilities and correlations under fat tails and possible skewness; Harvey and Luati (2014) introduce new models for dynamic changes in levels under fat tails; Creal et al. (2014) investigate score-based mixed measurement…”
Time-Varying Systemic Risk: Evidence From a Dynamic Copula Model of CDS Spreads (2018, unknown): “Vt 1 [eit] (2)it = !i + i (2)i;t 1 + ie(2)i;t 1 + ie(2)i;t 11 fei;t 1 > 0g (17)”