Introduction to Computational Finance and Financial Econometrics

Discussion in 'Educational Resources' started by slacker, Aug 23, 2012.

  1. slacker


    Only 9 days left till school starts!!! Enrol now, free education....

    Introduction to Computational Finance and Financial Econometrics

    About the Course
    Learn mathematical, programming and statistical tools used in the real world analysis and modeling of financial data. Apply these tools to model asset returns, measure risk, and construct optimized portfolios using the open source R programming language and Microsoft Excel. Learn how to build probability models for asset returns, to apply statistical techniques to evaluate if asset returns are normally distributed, to use Monte Carlo simulation and bootstrapping techniques to evaluate statistical models, and to use optimization methods to construct efficient portfolios.

    Topics covered include:

    Computing asset returns
    Univariate random variables and distributions
    Characteristics of distributions, the normal distribution, linear function of random variables, quantiles of a distribution, Value-at-Risk
    Bivariate distributions
    Covariance, correlation, autocorrelation, linear combinations of random variables
    Time Series concepts
    Covariance stationarity, autocorrelations, MA(1) and AR(1) models
    Matrix algebra
    Descriptive statistics
    histograms, sample means, variances, covariances and autocorrelations
    The constant expected return model
    Monte Carlo simulation, standard errors of estimates, confidence intervals, bootstrapping standard errors and confidence intervals, hypothesis testing , Maximum likelihood estimation, review of unconstrained optimization methods
    Introduction to portfolio theory
    Portfolio theory with matrix algebra
    Review of constrained optimization methods, Markowitz algorithm, Markowitz Algorithm using the solver and matrix algebra
    Statistical Analysis of Efficient Portfolios
    Risk budgeting
    Euler’s theorem, asset contributions to volatility, beta as a measure of portfolio risk
    The Single Index Model
    Estimation using simple linear regression
  2. Hi Slacker,

    Probably over my head, but what the heck. Took the Machine Learning course (Stanford) the first time it was offered (last October). A great experience. Very well done. There's really no downside to this type of thing.
  3. Thanks for the link. I'm signed up, I wonder how much of the texts I need to read before the course. I hope I can follow the lectures well enough.

    I'm signed up for this one:
    Computational Investing, Part I

    but it's always TBA, they don't have a start date.