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Intro to Computational Finance with R

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7 Hours87 Exercises19,281 Learners7700 XP

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Course Description

In this course, you'll make use of R to analyze financial data, estimate statistical models, and construct optimized portfolios. You will learn how to build probability models for assets returns, the way you should apply statistical techniques to evaluate if asset returns are normally distributed, methods to evaluate statistical models, and portfolio optimization techniques.
The material in this course was originally developed as a complement to Prof. Eric Zivot's Coursera lectures. Having a good mathematical basis, and an interest in financial markets is recommended.
  1. 1

    Return calculations

    Free

    Learn how to calculate, analyze and plot simple and continuously compounded returns in R.

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    Load the monthly Starbucks return data
    100 xp
    Get a feel for the data
    100 xp
    Extract the price data
    100 xp
    Find indices associated with the dates 3/1/1994 and 3/1/1995
    100 xp
    Subset directly on dates
    100 xp
    Plot the price data
    100 xp
    Calculate simple returns
    100 xp
    Add dates to simple return vector
    100 xp
    Compute continuously compounded 1-month returns
    100 xp
    Compare simple and continuously compounded returns
    100 xp
    Graphically compare the simple and continuously compounded returns
    100 xp
    Calculate growth of $1 invested in SBUX
    100 xp
    Compute one simple Starbucks return
    50 xp
    Compute one continuously compounded Starbucks return
    50 xp
    Monthly compounding
    50 xp
    Simple annual Starbucks return
    50 xp
    Annual continuously compounded return
    50 xp
  2. 6

    Constant expected return model

    Free

    Estimate parameters of the constant expected return (CER) model, compute standard errors and confidence intervals and test various hypotheses about the parameters and assumptions of the model. Perform bootstrapping of CER model estimates.

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  3. 7

    Introduction to portfolio theory

    Free

    Compute portfolios that consist of Boeing and Microsoft, T-bills and Boeing, T-bills and Microsoft and T-bills and combinations of Boeing and Microsoft. Use R functions to compute the global minimum variance portfolio and the tangency portfolio.

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  4. 8

    Computing efficient portfolios using matrix algebra

    Free

    Using the monthly closing price data on four Northwest stocks, you will estimate expected returns, variances and covariances to be used as inputs to the Markowitz algorithm. You will compute the global minimum variance portfolio, efficient portfolios, and the tangency portfolio for short-sales allowed and for short-sales not allowed.

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