Second‐Order Noncausality in Multivariate GARCH Processes

By Fabienne Comte, Offer Lieberman. 2000

60 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/d96e056c97f5c609a66c1e544994e2a4519fb2e8

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Citations in the corpus

A Nonparametric Test for Granger Causality in Distribution With Application to Financial Contagion (2014, methodology): “A unified treatment of Granger-causality in the mean and the variance is formalized by Comte and Lieberman (2000). More recently, some contributions focus on the concept of Granger-causality in quantiles, an issue which is important for non-Gaussian distributions that exhibit asymmetry, fat-tail cha”
Granger-causality in quantiles between financial markets: Using copula approach ☆ (2014, methodology): “In financial markets, a growing interest in volatility spill-over promotes the development of Grangercausality tests in volatility. cf. Granger, Robins, and Engle (1986), Lin, Engle, and Ito (1994), Cheung and Ng (1996), Comte and Lieberman (2000).”
ASYMPTOTIC THEORY FOR A FACTOR GARCH MODEL (2009, background): “Comte and Lieberman (2003) show that this matrix is positive definite a.”
MGARCH models: Trade-off between feasibility and flexibility (2018, background): “First, empirical evidence on volatility feedbacks is plentiful; see Granger et al. (1986), Engle et al. (1990), Comte and Lieberman (2000), Hafner and Herwartz (2006, 2008b), Bai and Chen (2008), Diebold and Yilmaz (2009), Nakatani and Teräsvirta (2009), Conrad and Karanasos (2010), Beirne et al.…”
Asymptotic theory for multivariate GARCH processes (2003, background): “See for instance, Bollerslev [5], pp. 306–307, or Comte and Lieberman [11].”
Exchange Rates and Commodity Prices : Measuring Causality at Multiple Horizons (2015, methodology): “For that purpose, we will use here the approach developed in Dufour and Taamouti (2010) and Dufour et al. (2012). Fourth, it is well known that Granger causality is generally not invariant to aggregation: highfrequency data may reveal patterns which are aggregated away in low-frequency data, and cau”
Temporal aggregation of univariate and multivariate time series models: a survey (2008, unknown): “Causality in volatility is studied by Comte and Lieberman (2000).”
Temporal aggregation of multivariate GARCH processes (2004, methodology): “noting that εt is a martingale difference sequence and applying a multivariate central limit theorem as in Davidson (1994, Section 25.3). Q.E.D. Proof of Theorem 3: First, εt,1 LICV = εt,2 is equivalent to [Σu]13 = 0, by Proposition 2.3 of Lütkepohl (1993, p.40). Now [Σu]13 = E[ε(2)t,1ε 2 t,2]−E[ht,”