Introduction to financial modeling; Linköpings universitet

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Introduction to financial modeling

There is a change in plan for the lecture on "Inference problems". Considering the backgrounds of the participants crediting the course, we have decided to skip this lecture. Instead, the participants are advised to make a self study. The links for appropriate materials are provided in the Lectures (slide) section. Additionally, we will present a brief introduction to the Bayesian filtering problems in the class room on that day.

In case you are planning to present a paper as a part of the evaluation, please inform us about your paper selection latest by 2012-11-19.

An online summary of stochastic calculus important results can be found here.

As part of the evaluation, each student should read a paper related to the topics covered during this course and present it on December 3rd. There is a list of proposed papers but if you are interested in an specific topic you can choose your own paper, just send it to us. This is the list of proposed papers.


  • Estimation of stochastic volatility models via Monte Carlo maximum likelihood. Gleb Sandmann and Siem Jan Koopman. Journal of Econometrics 87 (1998) 271-301.
  • Using partial least squares and support vector machines for bankruptcy prediction. Zijiang Yang, Wenjie You, and Guoli Ji. Expert systems with Applications 38 (2011) 8336-8342.
  • On the use of instrumental variables in accounting research. David F. Larcker, Tjomme O.Rusticus. Journal of Accounting and Economics 49 (2010) 186-205.
  • Valuing American Options by Simulation: A Simple Least-Squares Approach. Francis A. Longstaff and Eduardo S. Schwartz. The Review of Financial Studies 14 (2001) 113-147.
  • Stochastic volatility in asset prices. Estimation with simulated maximum likelihood. Jon Danielsson. Journal of Econometrics 64 (1994) 375-400.
  • Parameter Estimation for Discretely Observed Stochastic Volatility Models. Valentine Genon-Catalot, Thierry Jeantheau and Catherine Laredo. International Statistical Institute (ISI) and Bernoulli Society for Mathematical Statistics and Probability 5 (1999) 855-872.
  • Exact simulation of stochastic volatility and other affine jump diffusion processes, Mark Broadie and Ozgur Kaya.
  • Simulation-based sequential analysis of Markov switching stochastic volatility models, Carlos M. Carvalho and Hedibert F. Lopes, Computational Statistics & Data Analysis 51 (2007) 4526 – 4542.
  • Factor stochastic volatility with time varying loadings and Markov switching regimes, Hedibert Freitas Lopes and Carlos M. Carvalho, Journal of Statistical Planning and Inference 137 (2007) 3082 – 3091.
  • Variational Heteroscedastic Gaussian Process Regression, Miguel Lazaro-Gredilla and Michalis K. Titsias, ICML 2011.
  • Inference for Lévy-Driven Stochastic Volatility Models via Adaptive Sequential Monte Carlo, A. Jasra, D. A. Stephens, A. Doucet and T. Tsagaris, Scandinavian Journal of Statistics, Vol. 38: 1–22, 2011.
  • A Comparison of Biased Simulation Schemes for Stochastic Volatility Models, R. Lord, R. Koekkoek and D. van Dijk, TI 2006-046/4, Tinbergen Institute Discussion Paper
  • Sequential Monte Carlo Methods for Option Pricing,A. Jasra and P. Del Moral,Stochastic Analysis and Applications, 29:2, 292-316.
  • Bayesian inference for derivative prices, N.G. Polson and J. R. Stroud, Bayesian Statistics 7, 2003.

  • Informationsansvarig: Escobar J. and Saha S.
    Senast uppdaterad: 2012-12-07