Monte Carlo Methods and Stochastic Volatility


Dipartimento di Scienze delle Decisioni
Universita Bocconi, Milano
November 23rd to 27th 2009

Course material: PDF FILE WITH 224 SLIDES

Course schedule

Class Day Date Time Room Topic
1st Monday Nov 23 16.30-18.00 N1-7 velodromo Normal dynamic linear models
2nd Tuesday Nov 24 10.30-12.00 SDA 01 via Bocconi Nonnormal, nonlinear dynamic models
3rd Tuesday Nov 24 16.30-18.00 N17 velodromo Stochastic volatility models
4th Wednesday Nov 25 10.30-12.00 4-C via Sarfatti More on SV models
5th Thursday Nov 26 10.30-12.00 4-1 via Sarfatti Sequential Monte Carlo methods
SEMINAR Nov 26 16.30-18.00 Particle Learning for General Mixtures (talk slides)
6th Friday Nov 27 10.30-11.50 N1-2 velodromo Particle learning (PL)
7th Friday Nov 27 12.10-13.30 N1-2 velodromo More on PL

 

R code


Basic references

  • 1. Carlin, Polson and Stoffer (1992) A Monte Carlo approach to nonnormal and nonlinear state space modeling.
    Journal of the American Statistical Association, 87, 493-500.
  • 2. Carvalho, Johannes, Lopes and Polson (2008) Particle Learning and Smoothing.
    Technical Report. The University of Chicago Booth School of Business.
  • 3. Eraker, Johannes and Polson (2003) The Impact of Jumps in Volatility and Returns.
    Journal of Finance, 58, 1269-1300.
  • 4. Gamerman and Lopes (2006) MCMC: Stochastic Simulation for Bayesian Inference.
    Baton Rouge: Chapman & Hall/CRC.
  • 5. Jacquier, Polson and Rossi (1994) Bayesian Analysis of Stochastic Volatility Models.
    Journal of Business and Economic Statistics, 12, 371-89.
  • 6. Johannes and Polson (2009) MCMC methods for Financial Econometrics.
    In Handbook of Financial Econometrics (Eds Y. Ait-Sahalia and L. Hansen). Oxford: Elsevier, 1-72.
  • 7. Johannes, Polson and Stroud (2009) Optimal Filtering of Jump Diffusions: Extracting Latent States from Asset Prices.
    Review of Financial Studies, 22, 2559-2599.
  • 8. Kim, Shephard and Chib (1994) Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models.
    Review of Economic Studies, 65, 361-393.
  • 9. Liu and West (2001) Combined parameters and state estimation in simulation-based filtering.
    In Sequential Monte Carlo Methods in Practice (Eds. A. Doucet, N. de Freitas and N. Gordon).
    New York: Springer-Verlag, 197-223.
  • 10. Gordon, Salmond and Smith (1993) Novel approach to nonlinear/non-Gaussian Bayesian state estimation.
    Radar and Signal Processing, IEE Proceedings F 140, 107-113.
  • 11. Migon, Gamerman, Lopes and Ferreira (2005) Dynamic models.
    In Handbook of Statistics, Volume 25: Bayesian Thinking, Modeling and Computation (Eds. D. Dey and C. R. Rao),
    Amsterdam: Elsevier, 553-588.
  • 12. Petris, Petrone and Campagnoli (2009) Dynamic Linear Models with R.
    New York: Springer.
  • 13. Pitt and Shephard (1999) Filtering via simulation: auxiliary particle filters.
    Journal of the American Statistical Association, 94, 590-599.
  • 14. Polson, Lopes and Carvalho (2009) Bayesian Statistics with a Smile: a Resampling-Sampling Perspective.
    Technical Report. The University of Chicago Booth School of Business.
  • 15. Polson, Stroud and Muller (2008) Practical Filtering with Sequential Parameter Learning.
    Journal of the Royal Statistical Society, Series B, 70, 413-428.
  • 16. Prado and West (2010) Time Series: Modelling, Computation and Inference.
    Baton Rouge: Chapman & Hall/CRC.
  • 17. Storvik (2002) Particle filters in state space models with the presence of unknown static parameters.
    IEEE Transactions of Signal Processing, 50, 281-289.
  • 18. West and Harrison (1997) Bayesian Forecasting and Dynamic Models (2nd edition).
    New York: Springer-Verlag.


Other useful links