A Linear Algebra Primer for Financial Engineering: Covariance Matrices, Eigenvectors, OLS, and more  - medicalbooks.filipinodoctors.org

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A Linear Algebra Primer for Financial Engineering: Covariance Matrices, Eigenvectors, OLS, and more

Manufacturer: FE Press, LLC
ISBN 0979757657
EAN: 9780979757655
Category: Paperback (Business Mathematics)
Price: $65.00  (Customer Reviews)
Dimension: 9.00 x 6.00 x 0.77 inches
Shipping Wt: 1.28 pounds. FREE Shipping (Details)
Availability: In Stock
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Product Description

This book covers linear algebra methods for financial engineering applications from a numerical point of view. The book contains many such applications, as well as pseudocodes, numerical examples, and questions often asked in interviews for quantitative positions.

Financial Applications

• The Arrow—Debreu one period market model

• One period index options arbitrage

• Covariance and correlation matrix estimation from time series data

• Ordinary least squares for implied volatility computation

• Minimum variance portfolios and maximum return portfolios

• Value at Risk and portfolio VaR

Linear Algebra Topics

• LU and Cholesky decompositions and linear solvers

• Optimal solvers for tridiagonal symmetric positive matrices

• Ordinary least squares and linear regression

• Linear Transformation Property

• Efficient cubic spline interpolation

• Multivariate normal random variables

The book is written in a similar spirit as the best selling ``A Primer for the Mathematics of Financial Engineering" by the same author, and should accordingly be useful to a similarly large audience:

• Prospective students for financial engineering or mathematical finance programs will be able to self-study material that will prove very important in their future studies

• Finance practitioners will find mathematical underpinnings for many methods used in practice, furthering the ability to expand upon these methods

• Academics teaching financial engineering courses will be able to use this book as textbook, or as reference book for numerical linear algebra methods with financial applications.


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