Matrix exponential GARCH

By Hiroyuki Kawakatsu. 2006

96 citations recorded by the source corpus.

Subfields: Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/dc87539afbe57bfab0d772ab4bfa8a4b840b0747

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Citations in the corpus

Multivariate stochastic volatility (2009, background): “Lemma 1 of Chiu, Leonard, and Tsui (1996), Kawakatsu (2006)).”
Multivariate GARCH models (2008, background): “Since the model contains a large number of parameters, Kawakatsu (2006) discusses a number of more parsimonious specifications.”
Glossary to ARCH (GARCH) (2008, methodology): “Matrix EGARCH The multivariate matrix exponential GARCH model of Kawakatsu (2006) (see also EGARCH and MGARCH) specifies the second moment dynamics in terms of the”
Sequential Conditional Correlations: Inference and Evaluation (2009, unknown): “and Tauchen (2002) and Kawakatsu (2003) , feasible estimation can be an issue, even”
Biofuel-related price transmission literature: A review (2013, methodology): “Some popular specifications include the multivariate extension of the model by Glosten et al. (1993) proposed by McAleer et al. (2009), the matrix-exponential GARCH (Kawakatsu, 2006), or the asymmetric Baba–Engle–Kraft–Kroner (BEKK) model (Engle and Kroner, 1995) proposed by Kroner and Ng (1998).”
MGARCH models: Trade-off between feasibility and flexibility (2018, background): “…in the system can vary from being rather small to extremely large; see, for example, Bollerslev et al. (1988), Kavussanos and Visvikis (2004), Kawakatsu (2006) and Beirne et al. (2013) for small systems and Cappiello et al. (2006), Diebold and Yilmaz (2009), Santos et al. (2012), Santos et…”
Multivariate Stochastic Volatility: A Review (2006, methodology): “The model may be regarded as an SV generalization of the matrix exponential GARCH model of Kawakatsu (2005).”
Break detection in the covariance structure of multivariate time series models (2009, methodology): “The model in [35] is given by first assuming that the random vectors (yj : j ∈ Z) follow the equations yj = H 1/2 j εj , j ∈ Z, (4.”
A One Line Derivation of EGARCH (2014, background): “Kawakatsu [11] examined a matrix exponential GARCH model and its estimation under alternative multivariate distributions, but did not provide any asymptotic theory.”
Forecasting multivariate realized stock market volatility (2011, methodology): “Our estimated matrix is positive 2 The matrix logarithm has been used for estimators of latent volatility by Chiu et al. (1996) and Kawakatsu (2006) and was also suggested in Asai et al. (2006).”
A New Parametrization of Correlation Matrices (2020, methodology): “In GARCH and stochastic volatility models it was used in Kawakatsu (2006), Ishihara et al. (2016), and Asai and So (2015), and Bauer and Vorkink (2011) used the matrix logarithm for modeling and forecasting of realized covariance matrices.”
Temporal aggregation of univariate and multivariate time series models: a survey (2008, unknown): “The multivariate GARCH literature is extensive, including, among others, Bollerslev et al. (1988), Engle et al. (1990), Engle and Kroner (1995), Kroner and Ng (1998), Hafner and Herwartz (1998), van der Weide (2002), Tse and Tsui (2002), Engle (2002) and Kawakatsu (2006).”