A latent dynamic factor approach to forecasting multivariate stock market volatility
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Cited by:
- Tobias Hartl & Roland Weigand, 2018.
"Multivariate Fractional Components Analysis,"
Papers
1812.09149, arXiv.org, revised Jan 2019.
- Hartl, Tobias & Weigand, Roland, 2019. "Multivariate Fractional Components Analysis," University of Regensburg Working Papers in Business, Economics and Management Information Systems 38283, University of Regensburg, Department of Economics.
- Roland Weigand, 2014.
"Matrix Box-Cox Models for Multivariate Realized Volatility,"
Working Papers
144, Bavarian Graduate Program in Economics (BGPE).
- Weigand, Roland, 2014. "Matrix Box-Cox Models for Multivariate Realized Volatility," University of Regensburg Working Papers in Business, Economics and Management Information Systems 478, University of Regensburg, Department of Economics.
- Luo, Jiawen & Chen, Langnan, 2020. "Realized volatility forecast with the Bayesian random compressed multivariate HAR model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 781-799.
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More about this item
JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ECM-2014-02-02 (Econometrics)
- NEP-ETS-2014-02-02 (Econometric Time Series)
- NEP-FOR-2014-02-02 (Forecasting)
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