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A Note on Tractable State-Space Model for Symmetric Positive-Definite Matrices

Author

Listed:
  • Roberto Casarin

    (Department of Economics, University of Venice C� Foscari)

Abstract

This article discusses Windle and Carvalho's (2014) state-space model for observations and latent variables in the space of positive symmetric matrices. The present discussion focuses on the model specification and on the contribution to the positive-value time series literature. I apply the proposed model to financial data with a view to shedding light on some modeling issues.

Suggested Citation

  • Roberto Casarin, 2014. "A Note on Tractable State-Space Model for Symmetric Positive-Definite Matrices," Working Papers 2014:23, Department of Economics, University of Venice "Ca' Foscari".
  • Handle: RePEc:ven:wpaper:2014:23
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    References listed on IDEAS

    as
    1. Kapetanios, G. & Mitchell, J. & Price, S. & Fawcett, N., 2015. "Generalised density forecast combinations," Journal of Econometrics, Elsevier, vol. 188(1), pages 150-165.
    2. Geweke, John & Amisano, Gianni, 2010. "Comparing and evaluating Bayesian predictive distributions of asset returns," International Journal of Forecasting, Elsevier, vol. 26(2), pages 216-230, April.
    3. Andrew J. Patton & Allan Timmermann, 2011. "Forecast Rationality Tests Based on Multi-Horizon Bounds," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 1-17, June.
    4. Soyer, Refik & Tanyeri, Kadir, 2006. "Bayesian portfolio selection with multi-variate random variance models," European Journal of Operational Research, Elsevier, vol. 171(3), pages 977-990, June.
    5. K. Triantafyllopoulos, 2012. "Multi‐variate stochastic volatility modelling using Wishart autoregressive processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 33(1), pages 48-60, January.
    6. Todd E. Clark, 2011. "Real-Time Density Forecasts From Bayesian Vector Autoregressions With Stochastic Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(3), pages 327-341, July.
    7. Hao Wang & Mike West, 2009. "Bayesian analysis of matrix normal graphical models," Biometrika, Biometrika Trust, vol. 96(4), pages 821-834.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Exponential Smoothing; Positive-Valued Processes; State-Space Models; Stochastic Volatility.;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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