Introduction to Computational Finance and Financial Econometrics

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Course Date: 26 August 2014 to 04 November 2014 (10 weeks)

Price: free

Course Summary

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.

Course Instructors

Eric Zivot

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.

Course Description

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, 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
  • 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

Suggested Reading

(The first 4 texts are highly recommended)
Introduction to Computational Finance and Financial Econometrics, Eric Zivot and R. Douglas Martin. 
Manuscript under preparation
Other books for further reference: 

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