GO-GARCH: a multivariate generalized orthogonal GARCH model

By R. Weide. 2002

458 citations recorded by the source corpus.

Subfields: Finance, Statistics and Probability

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Citations in the corpus

High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes (2019, methodology): “Deep learning-based sequence models have been successfully applied to audio signals [33], language modeling [13, 30], and general density estimation of univariate sequences [22, 21].”
Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion (2024, unknown): “…± 0.000 0.830 ± 0.006 0.039 ± 0.001 0.290 ± 0.001 - - VAR-Lasso [44] 0.012 ± 0.000 0.510 ± 0.006 0.025 ± 0.000 0.150 ± 0.002 - 3.100 ± 0.004 GARCH [61] 0.023 ± 0.000 0.880 ± 0.002 0.190 ± 0.001 0.370 ± 0.001 - - DeepAR [54] - 0.336 ± 0.014 0.023 ± 0.001 0.055 ± 0.003 - 0.127 ± 0.042 LSTM-Copula…”
COINTEGRATION RANK TESTING UNDER CONDITIONAL HETEROSKEDASTICITY (2010, unknown): “In the case where the individual components follow a standard GARCH(1,1) process (as is the case with Models A and B, which follow), van der Weide (2002) refers to εt as a GO-GARCH(1,1) process.”
A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations (2011, methodology): “A similar decomposition for parameterizing a set of orthonomal matrices has been adopted in the GO-GARCH model of van der Weide (2002).”
Analytical quasi maximum likelihood inference in multivariate volatility models (2003, methodology): “Many other multivariate GARCH variants are special cases of the BEKK specification, for example the factor model of Engle, Ng and Rothschild (1990), the orthogonal GARCH model of Alexander (2001) and the GO-GARCH model of van der Weide (2002).”
Estimating multivariate volatility models equation by equation (2016, methodology): “…to alleviate the dimensionality curse rely on either constraining the structure of the model to reduce the number of parameters (see for example Engle et al. (1990), van der Weide (2002) and Lanne and Saikkonen (2007)), or using an alternative estimation criterion (see Engle et al. (2008)).”
Copula-Based Models for Financial Time Series (2009, methodology): “Two popular approaches to solve that problem are factor-based ARCH models and extensions, see Alexander and Chibumba (1998) and van der Weide (2002) for example, and the DCC model of Engle (2002) and its extensions, see Cappiello, et al. (2006) for example.”
Hedging emerging market stock prices with oil, gold, VIX, and bonds: A comparison between DCC, ADCC and GO-GARCH ☆ (2015, unknown): “The first version of GO-GARCH, van der Weide (2002), appeared around the same time as DCC and used the maximum likelihood approach in estimation.”
Glossary to ARCH (GARCH) (2008, unknown): “GO-GARCH (Generalized Orthogonal GARCH) The multivariate GO-GARCH model of van der Weide (2002) assumes that the temporal variation in the N × N conditional covariance matrix may be expressed in terms of N conditionally uncorrelated components, where X denotes a N × N matrix, and D t is diagonal…”
Generalized dynamic factor models and volatilities: recovering the market volatility shocks (2016, methodology): “…and then assuming a GARCH model for the latent factors, as, for example, Ng et al. (1992), Harvey et al. (1992), Diebold and Nerlove (1989), Van der Weide (2002), Connor et al. (2006), and Sentana et al. (2008), among others; see Jurado et al. (2013) for an application to macroeconomic data.”