FinM 331/STAT 339 Financial Data Analysis
Winter 2010
Mondays, 6:30-9:30PM, Ryerson 251, Chicago
Mondays, 7:30-10:30PM, UBS, Stamford
Tuesdays, 7:30-10:30PM, Spring, Singapore
Syllabus+ and Webpage
28 December 2009 Edition

Visiting Professor Floyd B. Hanson



Course Outline (tentative)

Applied Statistical Analysis of Financial Data with Computation in MATLAB

  1. Introduction (1 Lecture): Data models and Discretized Diffusion log-returns, Independent observations and their statistics, Additive and Multiplicative models, Law of large numbers and Monte Carlo applications, Central limit theorem and Extreme tail errors.

  2. Exploratory Data Analysis (2.5): Pseudo-Random Number Generators (RNGs: Uniform, Normal, Exponential, Poisson, Compound Poisson), Histograms, Cumulative histograms, Kernel smoothing, Quantile-Quantile (Q-Q) plots, Discretized Jump-diffusions, Confidence Intervals and Value at Risk (VaR), more Normal distribution poor at extremes, short-fall statistics, Profit and Loss (P&L) statments, Cauchy flat tail distribution, Data estimation, Order statistics, Extreme statistics (Fat tails, Pareto distribution, Peaks Over Thresholds (POTs).

  3. Multivariate Statistics (1.5L): Bivariate distribution, Bivariate Kernel smoothing, Conditional expectation, Covariance, Correlation Coefficient, Bivariate 3D-histograms, Hybrid fat and thin tail models, Principal Component Analysis (PCA), Multivariate sample means.

  4. Parametric Regression (1.5L): Ordinary Least Squares (OLS), Multiple linear regression, MATLAB regression functions, Maximum Likelihood Estimation (MLE), MLE for discretized linear diffusions, compound Poisson processes, MLE for discretized linear jump-diffusions.

  5. Non-Parametric Regression (1L): Nnumerical optimization with derivative-free MATLAB fminsearch, Black-Scholes European options, Market calibration and Implied Volatility (IV), Risk-Neutral options and IV, more Kernel smoothing regression.

  6. General Parameter Estimation (1.5L): Maximum Likelihood, Moment Methods and Bayesian.

  7. Hypothesis Testing and Analysis of Variance (1L): Goodness of fit, Confidence Intervals.

Prerequisite Knowledge:


Some Related Resources of the Instructor:


Web Source (temp.): http://www.math.uchicago.edu/~hanson/finm331/

Email Comments or Questions to fhanson at math dot uchicago dot edu )