Free PDF A Linear Algebra Primer for Financial Engineering: Covariance Matrices, Eigenvectors, OLS, and more (Financial Engineering Advanced Backgro
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A Linear Algebra Primer for Financial Engineering: Covariance Matrices, Eigenvectors, OLS, and more (Financial Engineering Advanced Backgro
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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.
- Sales Rank: #454288 in Books
- Published on: 2014-07-07
- Original language: English
- Dimensions: 9.00" h x .77" w x 6.00" l,
- Binding: Paperback
- 340 pages
About the Author
Dan Stefanica has been the Director of the Baruch MFE Program since its inception in 2002, and is the author of the best-selling A Primer For The Mathematics Of Financial Engineering and A Linear Algebra Primer for Financial Engineering: Covariance Matrices, Eigenvectors, OLS, and more, and co-author of 150 Most Frequently Asked Questions on Quant Interviews. He teaches graduate courses on numerical methods for financial engineering, as well as pre-program courses on advanced calculus and numerical linear algebra with financial applications. His research spans numerical analysis, graph theory, and geophysical fluid dynamics. He has a PhD in mathematics from New York University and taught previously at the Massachusetts Institute of Technology.
Most helpful customer reviews
12 of 15 people found the following review helpful.
This book doesn't teach Linear Algebra but shows financial engineering applicatons of it.
By 2 cents worth
This book is not A Linear Algebra Primer for Financial Engineering...
It is a Primer on Applying Basic Linear Algebra in Financial Engineering...
That is the book doesn't teach much Linear Algebra...what it does is show how to apply
linear algebra to Financial Engineering...but you have to know Linear Algebra ( at the college undergrad linear algebra level)
to make much use of the book. But if you are comfortable with Linear Algebra there are some useful applications.
6 of 8 people found the following review helpful.
Comprehensive survey of linear algebra for finance professionals
By Ijon Tichy
This book, modestly titled as a "linear algebra primer", is, in fact, a well written comprehensive introduction to the uses of linear algebra techniques in finance. The emphasis lies on concrete, computer software implementable linear algebra with a wide range of specific applications to portfolio construction, market data analysis, and derivatives pricing. It will be a valuable resource not only for its targeted audience, namely graduate students in financial engineering, but also, more broadly, for business professionals whose work relies of quantitative techniques.
2 of 2 people found the following review helpful.
Unarguably the best Linear Algebra primer out there for applications to finance
By Amazon Customer
Unarguably the best Linear Algebra primer out there for applications to finance. Broad range of topics covered with great depth. I often use it as a quick reference guide.
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