Introduction to Computational Finance and Financial Econometrics
This course is offered through Coursera — you can add it to your Accredible profile to organize your learning, find others learning the same thing and to showcase evidence of your learning on your CV with Accredible's export features.
Course Date: 26 August 2014 to 04 November 2014 (10 weeks)
Learn mathematical and statistical tools and techniques used in quantitative and computational finance. Use the open source R statistical programming language to analyze financial data, estimate statistical models, and construct optimized portfolios. Analyze real world data and solve real world problems.
Eric Zivot is the Robert Richards Chaired Professor in the Economics Department, Adjunct Professor of Statistics, Adjunct Professor of Finance, and Adjunct Professor of Applied Mathematics. He is co-director of the Master of Science Program in Computational Finance and Risk Management in the Department of Applied Mathematics at UW. He is also a risk management consultant to BlackRock Alternative Advisors. He is co-author of Modeling Financial Time Series with S-PLUS and co-developer of S+FinMetrics, and has consulted on the use of S-PLUS and R in the finance industry. He regularly teaches courses on econometric theory, financial econometrics and time series econometrics, and is the recipient of the Henry T. Buechel Award for Outstanding Teaching. His current research focuses on the econometric analysis of high frequency financial data and the measurement of financial risk. He has published extensively in the leading econometrics and empirical finance journals. He holds the Ph.D. in Economics from Yale University, and the BS in Economics and Statistics from the University of California Berkeley.
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.
You'll do the R assignments for this course on DataCamp.com, an online interactive learning platform that offers free R tutorials through learning-by-doing.
The platform provides you with hints and instant feedback on how to perform even better. Every week, new labs will be posted.
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
Covariance, correlation, autocorrelation, linear combinations of random variables
Time Series concepts
Covariance stationarity, autocorrelations, MA(1) and AR(1) models
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
Euler’s theorem, asset contributions to volatility, beta as a measure of portfolio risk
The Single Index Model
Estimation using simple linear regression
(The first 4 texts are highly recommended)
Introduction to Computational Finance and Financial Econometrics, Eric Zivot and R. Douglas Martin. Manuscript under preparation