Quantitative Finance

Risk management is addressed by studying extreme values, adjusting distributions with heavy tails, calculating values ​​at risk (VaR) and other risk measures. Principal component analysis (PCA), smoothing and regression techniques are applied to the construction of yield and advance curves. Time series analysis is applied to the study of temperature options and nonparametric estimation. Non-linear filtering applied to Monte Carlo simulations, option pricing and earnings forecasting. This course is sprinkled with practical examples, using market data. Practical examples are solved in the computing environment R. They illustrate the problems that occur in the commodities, energy and climate markets, as well as in the fixed income, equity and credit markets. The examples, experiments and problem sets are based on the Rsafd library.

Basic Information

60 hours
Probability Theory, Statistical Inference


  • Carmona, René. Statistical Analysis of Financial Data in R. 2014
  • Abu-Moustafa, Y.S., Magdon-Ismail, M., and Lin H-S. Learning from data. AMLBook.com. 2012.
  • Hastie, T., Tibshirani, R., and Friedman, J. The Elements of Statistical Learning: DataMining, Inference, and Prediction. Springer. 2009


  • Duda, R. O., Hart, P. E. and Stork, D. G. Pattern Classification (2nd Edition). Wiley-Interscience, 2000.
  • Murphy, K. P.. Machine Learning, A Probabilistic Perspective. MIT Press, 2012
  • Bishop, C. M.. Pattern Recognition and Machine Learning. Springer, 2006.
  • Morris DeGroot, Mark Schervish. Probability and Statistics. Fourth Edition, 2012.
  • Versani, John. Using R for Introductory Statistics. Chapman & Hall, 2005 (online version at http://cran.r-project.org/doc/contrib/Verzani-SimpleR.pdf)
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