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Bayesian parametric and semiparametric factor models for large realized covariance matrices

Jin, Xin; Maheu, John M.; Yang, Qiao - In: Journal of Applied Econometrics 34 (2019) 5, pp. 641-660

Persistent link: https://www.econbiz.de/10012082833

Can GARCH models capture long-range dependence?

Maheu, John M. (contributor) - In: Studies in nonlinear dynamics and econometrics : SNDE ; … 9 (2005) 4, pp. 1-41

Persistent link: https://www.econbiz.de/10003283982

Bayesian adaptively updated Hamiltonian Monte Carlo with an application to high-dimensional BEKK GARCH models

Burda, Martin; Maheu, John M. - In: Studies in nonlinear dynamics and econometrics : SNDE ; … 17 (2013) 4, pp. 345-372

Persistent link: https://www.econbiz.de/10009787988

Bayesian nonparametric estimation of ex post variance

Griffin, Jim; Liu, Jia; Maheu, John M. - In: Journal of financial econometrics 19 (2021) 5, pp. 823-859

Persistent link: https://www.econbiz.de/10012799051

Nonparametric dynamic conditional beta

Maheu, John M.; Zamenjani, Azam Shamsi - In: Journal of financial econometrics 19 (2021) 4, pp. 583-613

Persistent link: https://www.econbiz.de/10012654975

Oil price shocks and economic growth : the volatility link

Maheu, John M.; Song, Yong; Yang, Qiao - In: International journal of forecasting 36 (2020) 2, pp. 570-587

Persistent link: https://www.econbiz.de/10012415259

An infinite hidden Markov model for short-term interest rates

Maheu, John M.; Yang, Qiao - In: Journal of empirical finance 38 (2016), pp. 202-220

Persistent link: https://www.econbiz.de/10011663269

Bayesian semiparametric modeling of realized covariance matrices

Jin, Xin; Maheu, John M. - In: Journal of econometrics 192 (2016) 1, pp. 19-39

Persistent link: https://www.econbiz.de/10011610652

Modeling covariance breakdowns in multivariate GARCH

Jin, Xin; Maheu, John M. - In: Journal of econometrics 194 (2016) 1, pp. 1-23

Persistent link: https://www.econbiz.de/10011705024

Learning, forecasting and structural breaks

Maheu, John M.; Gordon, Stephen - In: Journal of Applied Econometrics 23 (2008) 5, pp. 553-583

We provide a general methodology for forecasting in the presence of structural breaks induced by unpredictable changes to model parameters. Bayesian methods of learning and model comparison are used to derive a predictive density that takes into account the possibility that a break will occur...

Persistent link: https://www.econbiz.de/10005241860