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University of Washington

Introduction to Computational Finance and Financial Econometrics

University of Washington via Coursera

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Overview

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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.  

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

Taught by

Eric Zivot

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Reviews

3.3 rating, based on 10 Class Central reviews

Start your review of Introduction to Computational Finance and Financial Econometrics

  • Anonymous
    Information packed class. Professor Zivot has a great deal of knowledge in this field. Unfortunately, video quality if horrible. Slides often unreadable. I think it's a general unwillingness of UW to provide a high quality free online classes. It could be a great class, but not at the current production.
  • Anonymous
    This course is really good for introductory econometric. If you listen to the lectures and work the problems it gives a basic understanding and knowledge. Prof is very knowledgeable . The lecture video is of poor quality and unreadable slides. Looks…
  • Anonymous
    A well done introduction to econometrics. I learned a lot. The lectures were well done and on time. One problem was that the problem sets were just too easy, especially the labs. Since the labs were preprogrammed, we merely had to press run and answer the questions. It would have been more instructive to actually have to some programming in R to answer the questions. An initial skeleton of the program which we would have to fill in would have worked much better.
  • Profile image for Wichaiditsornpon@gmail.com
    [email protected]
    this is great course great knowledge great professor but video quality is bad. you can put 5 star if you don't mind about that
  • Ramón Martínez Martínez
  • Kuronosuke
  • John Walsh
  • Profile image for Michael A. Alcorn
    Michael A. Alcorn
  • Macemers
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