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Determining the optimal dimensionality of multivariate volatility models with tools from random matrix theory

Rosenow, Bernd - In: Journal of economic dynamics & control 32 (2008) 1, pp. 279-302

Persistent link: https://www.econbiz.de/10003622775

Dynamic principal component CAW models for high-dimensional realized covariance matrices

Gribisch, Bastian; Stollenwerk, Michael - In: Quantitative finance 20 (2020) 5, pp. 799-821

Persistent link: https://www.econbiz.de/10012262622

Forecasting large covariance matrix with high-frequency data using factor approach for the correlation matrix

Dong, Yingjie; Tse, Yiu Kuen - In: Economics letters 195 (2020), pp. 1-4

Persistent link: https://www.econbiz.de/10012509995

Determinants of asymmetric return comovements of gold and other financial assets

Poshakwale, Sunil S.; Mandal, Anandadeep - In: International review of financial analysis 47 (2016), pp. 229-242

Persistent link: https://www.econbiz.de/10011624131

Multivariate FIAPARCH modelling of financial markets with dynamic correlations in times of crisis

Karanasos, Menelaos; Yfanti, Stavroula; Karoglou, Michail - In: International review of financial analysis 45 (2016), pp. 332-349

Persistent link: https://www.econbiz.de/10011583871

Dynamic stock-bond return correlations and financial market uncertainty

Chiang, Thomas C.; Li, Jiandong; Yang, Sheng-Yung - In: Review of quantitative finance and accounting 45 (2015) 1, pp. 59-88

Persistent link: https://www.econbiz.de/10011333137

Potential PCA interpretation problems for volatility smile dynamics

Reiswich, Dimitri; Tompkins, Robert G. - 2009

Principal Component Analysis (PCA) is a common procedure for the analysis of financial market data, such as implied volatility smiles or interest rate curves. Recently, Pelsser and Lord [11] raised the question whether PCA results may not be 'facts but artefacts'. We extend this line of research...

Persistent link: https://www.econbiz.de/10011293917