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EMPIRICAL LIKELIHOOD FOR GARCH MODELS
Econometric Theory, 2006Summary: This paper develops an empirical likelihood approach for regular generalized autoregressive conditional heteroskedasticity (GARCH) models and GARCH models with unit roots. For regular GARCH models, it is shown that the log empirical likelihood ratio statistic asymptotically follows a \(\chi^2\) distribution.
Chan, NH, Ling, SQ
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Bayesian Clustering of Many GARCH Models [PDF]
Econometric Reviews, 2005 We consider the estimation of a large number of GARCH models, of the order of several hundreds. To achieve parsimony, we classify the series in a small number of groups. Within a cluster, the series share the same model and the same parameters. Each cluster contains therefore similar series. We do not know a priori which series belongs to which cluster.
BAUWENS, Luc, ROMBOUTS, Jeroen
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GO‐GARCH: a multivariate generalized orthogonal GARCH model
Journal of Applied Econometrics, 2002AbstractMultivariate GARCH specifications are typically determined by means of practical considerations such as the ease of estimation, which often results in a serious loss of generality. A new type of multivariate GARCH model is proposed, in which potentially large covariance matrices can be parameterized with a fairly large degree of freedom while ...
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Adaptive Filtering for GARCH Models
2002The volatility of a speculative asset is a fundamental ingredient of many financial pricing algorithms, therefore, accurate forecasts of volatility are essential to financial practioners. Autoregressive Conditional Heteroscekdastic models and their generalisations (GARCH) have been shown to provide reasonable forecasts of volatility with relatively few
Paul E. Lynch, Nigel M. Allinson
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Varying Coefficient GARCH Models
2009This paper offers a new method for estimation and forecasting of the volatility of financial time series when the stationarity assumption is violated. We consider varying–coefficient parametric models, such as ARCH and GARCH, whose coefficients may arbitrarily vary with time.
Cizek, P., Spokoiny, V.
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Dynamic Factor Multivariate GARCH Model
SSRN Electronic Journal, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
André Alves Portela Santos +1 more
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Comparison of Specification Tests for GARCH Models
SSRN Electronic Journal, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kilani Ghoudi, Bruno N. Rémillard
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Comparison of BEKK GARCH and DCC GARCH Models: An Empirical Study
2010Modeling volatility and co-volatility of a few zero-coupon bonds is a fundamental element in the field of fix-income risk evaluation. Multivariate GARCH model (MGARCH), an extension of the well-known univariate GARCH, is one of the most useful tools in modeling the co-movement of multivariate time series with time-varying covariance matrix. Grounded on
Yiyu Huang, Wenjing Su, Xiang Li 0033
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2006
A GARCH-type model for non-leading financial market returns is considered.The innovation consists in assuming the returns to depend on the sign of the leading financial market in the world. Under standard assumption, the conditional distribution of the returns turns out to be a Skew-t random variate.
DE LUCA, GIOVANNI, LOPERFIDO N.
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A GARCH-type model for non-leading financial market returns is considered.The innovation consists in assuming the returns to depend on the sign of the leading financial market in the world. Under standard assumption, the conditional distribution of the returns turns out to be a Skew-t random variate.
DE LUCA, GIOVANNI, LOPERFIDO N.
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