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Home / Computer Science / Problem 1 Covariance Matrix Decomposition Download historical data for 5 years and at least 100
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Problem 1: Covariance Matrix Decomposition: Download historical data for 5 years and at least 100 companies or ETFs. In this problem, we will look at the covariance matrix for these assets and its properties. 1. Clean the data so that your input pricing matrix is as full as possible. Fill in any gaps using a reasonable method of your choice. Justify your choice. 2. Generate a sequence of daily returns for each underlying asset for each date. 3. Calculate the covariance matrix of daily returns and perform an eigenvalue decomposition on the matrix. How many positive eigenvalues are there? How many were negative? We know from theory that there should be no negative eigenvalues. If any are negative, what happened? 4. How many eigenvalues are required to account for 50% of the variance? How about 90%? Does this make sense to you?

Problem 1 Covariance Matrix Decomposition Download historical data for 5 years and at least 100

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(4 customer reviews)

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SKU: 4987 Category: Computer Science Tags: Calculate the covariance matrix of daily returns and perform an eigenvalue decomposition on the matrix, Clean the data so that your input pricing matrix is as full as possible, Download historical data for 5 years and at least 100 companies or ETFs, Fill in any gaps using a reasonable method of your choice, Generate a sequence of daily returns for each underlying asset for each date, How many eigenvalues are required to account for 50 of the variance How about 90 Does this make sense to you, How many positive eigenvalues are there How many were negative We know from theory that there should be no negative eigenvalues, In this problem we will look at the covariance matrix for these assets and its properties
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Problem 1: Covariance Matrix Decomposition:

Download historical data for 5 years and at least 100 companies or ETFs. In this problem, we will look at the covariance matrix for these assets and its properties.

1. Clean the data so that your input pricing matrix is as full as possible. Fill in any gaps using a reasonable method of your choice. Justify your choice.

2. Generate a sequence of daily returns for each underlying asset for each date.

3. Calculate the covariance matrix of daily returns and perform an eigenvalue decomposition on the matrix. How many positive eigenvalues are there? How many were negative? We know from theory that there should be no negative eigenvalues. If any are negative, what happened?

4. How many eigenvalues are required to account for 50% of the variance? How about 90%? Does this make sense to you?

4 reviews for Problem 1 Covariance Matrix Decomposition Download historical data for 5 years and at least 100

  1. Rated 5 out of 5

    Ty Miyahara – October 26, 2023

    Thanks a lots, I really appreciate your effort it is an excellent report.

  2. Rated 5 out of 5

    Jeff Minyard – October 27, 2023

    Excellent, thank you! Got solution extremely fast, within minutes.

  3. Rated 5 out of 5

    Jp Grillo – October 27, 2023

    great work as usual thanks

  4. Rated 5 out of 5

    Gordon Benton – October 28, 2023

    Second time coming to this website for a paper and it worked out great.

Only logged in customers who have purchased this product may leave a review.

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Problem 1: Covariance Matrix Decomposition: Download historical data for 5 years and at least 100 companies or ETFs. In this problem, we will look at the covariance matrix for these assets and its properties. 1. Clean the data so that your input pricing matrix is as full as possible. Fill in any gaps using a reasonable method of your choice. Justify your choice. 2. Generate a sequence of daily returns for each underlying asset for each date. 3. Calculate the covariance matrix of daily returns and perform an eigenvalue decomposition on the matrix. How many positive eigenvalues are there? How many were negative? We know from theory that there should be no negative eigenvalues. If any are negative, what happened? 4. How many eigenvalues are required to account for 50% of the variance? How about 90%? Does this make sense to you?
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