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# 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....

https://www.coursera.org/course/compfinance

Introduction to Computational Finance and Financial Econometrics

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
Eulerâs theorem, asset contributions to volatility, beta as a measure of portfolio risk
The Single Index Model
Estimation using simple linear regression

2. ### kandlekid

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. ### zedDoubleNaught

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:

https://www.coursera.org/course/compinvesting1
Computational Investing, Part I

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

ET IS FREE BECAUSE OF THE FINANCIAL SUPPORT FROM THESE COMPANIES: