Introduction to Computational Finance and Financial Econometrics

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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.

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 …

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Didn't find what you were looking for? See also: Econometrics, Business Finance, International Economics, Balance Sheet, and Economics.

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

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.

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.

About the Instructor(s)

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 Syllabus

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 Readings

(The first 4 texts are highly recommended) Introduction to Computational Finance and Financial Econometrics, Eric Zivot and R. Douglas Martin. Manuscript under preparation Statistics and Data Analysis for Financial Engineering by David Ruppert, Springer-Verlag. Beginner's Guide to R by Alain Zuur, Elena Ieno and Erik Meesters, Springer-Verlag. R Cookbook by Paul Teetor, O'Reilly. Other books for further reference: Introductory Statistics with R, Second Edition (Statistics and Computing, Paperback), by Peter Dalgaard, Springer-Verlag, New York. Modern Portfolio Theory and Investment Analysis, by E.J. Elton et al., Wiley, New York. Financial Modeling, by Simon Benninga. MIT Press. Statistical Analysis of Financial data in S-PLUS, by Rene Carmona, Springer-Verlag, 2004.

Provided by:

University: University of Washington

Instructor(s): Eric Zivot

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