Generalised Wishart processes

Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011

Volumn , Issue , 2011, Pages 736-744

  Wilson, Andrew Gordon ^a   Ghahramani, Zoubin ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COVARIANCE MATRIX; RANDOM PROCESSES; STOCHASTIC SYSTEMS;

ARBITRARY INPUTS; COVARIANCE MATRICES; FINANCIAL DATA; INPUT VARIABLES; MISSING DATA; MULTIVARIATE GARCH; RANDOM MATRICES; TIME VARYING;

ARTIFICIAL INTELLIGENCE;

EID: 80053163503     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (33)

1. Andersen, T. G. and Bollerslev, T. (1997). Intraday periodicity and volatility persistence in financial markets. Journal of Empirical Finance, 4(2-3):115-158. (Pubitemid 127175391)
2. Bae, K., Karolyi, G., and Stulz, R. (2003). A new approach to measuring financial contagion. Review of Financial Studies, 16(3):717.
3. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3):307-327.
4. Bollerslev, T. (1990). Modeling the coherence in shortterm nominal exchange rates: A multivariate generalized arch approach. Review of Economics and Statistics, 72:498-505.
5. Bollerslev, T., Engle, R. F., and Wooldridge, J. M. (1988). A capital asset pricing model with timevarying covariances. The Journal of Political Economy, 96(1):116-131.
6. Bonilla, E., Chai, K., and Williams, C. (2008). Multitask Gaussian process prediction. In NIPS.
7. Boyle, P. and Frean, M. (2004). Dependent Gaussian processes. In NIPS.
8. Brooks, C., Burke, S., and Persand, G. (2001). Benchmarks and the accuracy of GARCH model estimation. International Journal of Forecasting, 17:45-56. (Pubitemid 33623413)
9. Brownlees, C. T., Engle, R. F., and Kelly, B. T. (2009). A practical guide to volatility forecasting through calm and storm. Available at SSRN: http://ssrn.com/abstract=1502915.
10. Bru, M. (1991). Wishart processes. Journal of Theoretical Probability, 4(4):725-751.
11. Chen, Y. and Welling, M. (2010). Dynamical products of experts for modeling financial time series. In ICML, pages 207-214.
12. Engle, R. and Kroner, K. (1995). Multivariate simultaneous generalized ARCH. Econometric theory, 11(01):122-150.
13. Engle, R., Nelson, D., and Bollerslev, T. (1994). Arch models. Handbook of Econometrics, 4:2959-3038.
14. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom ination. Econometrica, 50(4):987-1007.
15. Gelfand, A., Schmidt, A., Banerjee, S., and Sirmans, C. (2004). Nonstationary multivariate process modeling through spatially varying coregionalization. Test, 13(2):263-312. (Pubitemid 40217244)
16. Geman, S. and Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(2):721-741. (Pubitemid 15453722)
17. Gourifieroux, C. (1997). ARCH models and financial applications. Springer Verlag.
18. Gourifieroux, C., Jasiak, J., and Sufana, R. (2009). The Wishart autoregressive process of multivariate stochastic volatility. Journal of Econometrics, 150(2):167-181.
19. Green, P. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82(4):711.
20. Hansen, P. R. and Lunde, A. (2005). A forecast comparison of volatility models: Does anything beat a GARCH(1, 1). Journal of Applied Econometrics, 20(7):873-889. (Pubitemid 43097870)
21. Harvey, A., Ruiz, E., and Shephard, N. (1994). Multivariate stochastic variance models. The Review of Economic Studies, 61(2):247-264.
22. McCullough, B. and Renfro, C. (1998). Benchmarks and software standards: A case study of GARCH procedures. Journal of Economic and Social Measurement, 25:59-71. (Pubitemid 128637218)
23. Murray, I., Adams, R. P., and MacKay, D. J. (2010). Elliptical Slice Sampling. JMLR: W&CP, 9:541-548.
24. Philipov, A. and Glickman, M. (2006). Multivariate stochastic volatility via Wishart processes. Journal of Business and Economic Statistics, 24(3):313-328. (Pubitemid 44116423)
25. Poon, S.-H. and Granger, C. W. (2005). Practical issues in forecasting volatility. Financial Analysts Journal, 61(1):45-56. (Pubitemid 40251487)
26. Rasmussen, C. E. and Williams, C. K. (2006). Gaussian processes for Machine Learning. The MIT Press.
27. Robert, C. and Casella, G. (2004). Monte Carlo statistical methods. Springer Verlag.
28. Sharpe, W. (1966). Mutual fund performance. Journal of business, 39(1):119-138.
29. Silvennoinen, A. and Teräsvirta, T. (2009). Multivariate GARCH models. Handbook of Financial Time Series, pages 201-229.
30. Teh, Y., Seeger, M., and Jordan, M. (2005). Semiparametric latent factor models. In Workshop on Artificial Intelligence and Statistics, volume 10.
31. Wilson, A. G. and Ghahramani, Z. (2010). Copula processes. In NIPS.
32. Wilson, A. G. and Ghahramani, Z. (2011a). Generalised Wishart Processes Supplementary Material. mlg.eng.cam.ac.uk/andrew/gwpsupp.pdf.
33. Wilson, A. G. and Ghahramani, Z. (2011b). In preparation.