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Robust inference in time-varying structural VAR models : the DC-cholesky multivariate stochastic volatility model

Hartwig, Benny - 2020

Cholesky multivariate stochastic volatility model. It establishes that systematically different dynamic restrictions are … divergent when volatility clusters idiosyncratically. It is illustrated that this property is important for empirical … indicate that conclusions may critically hinge on a selected ordering of variables. The dynamic correlation Cholesky …

Persistent link: https://www.econbiz.de/10012424283

Stochastic multivariate mixture covariance model

So, Mike Ka-pui; Li, Raymond W. M.; Asai, Manabu; Jiang, Yue - In: Journal of forecasting 36 (2017) 2, pp. 139-155

Persistent link: https://www.econbiz.de/10011729126

Model-based estimation of high frequency jump diffusions with microstructure noise and stochastic volatility

Bos, Charles S. - 2008

Persistent link: https://www.econbiz.de/10003645209

Do precious metal prices help in forecasting South African inflation?

Balcilar, Mehmet; Katzke, Nico; Gupta, Rangan - In: The North American journal of economics and finance : a … 40 (2017), pp. 63-72

Persistent link: https://www.econbiz.de/10011878788

Volatility spillovers across financial markets : the role of oil price uncertainty

Lee, Seojin; Kim, Young Min - In: Applied economics letters 30 (2023) 17, pp. 2342-2347

Persistent link: https://www.econbiz.de/10014365776

Bayesian modelling of VAR precision matrices using stochastic block networks

Huber, Florian; Koop, Gary; Marcellino, Massimiliano; … - 2024

Commonly used priors for Vector Autoregressions (VARs) induce shrinkage on the autoregressive coefficients. Introducing shrinkage on the error covariance matrix is sometimes done but, in the vast majority of cases, without considering the network structure of the shocks and by placing the prior...

Persistent link: https://www.econbiz.de/10015395756