A Linear Algebra Primer for Financial Engineering: Covariance Matrices, Eigenvectors, OLS, and more (Financial Engineering Advanced Background Series)

A Linear Algebra Primer for Financial Engineering: Covariance Matrices, Eigenvectors, OLS, and more (Financial Engineering Advanced Background Series) cover

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Author: Dan Stefanica

Pages: 340

Size: 3.578,36 Kb

Publication Date: July 7,2014

Category: Business Mathematics



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This publication covers linear algebra options for economic engineering applications from a numerical perspective.

Financial Applications

• The Arrow—Debreu one period marketplace model

• One period index choices arbitrage

• Covariance and correlation matrix estimation from period series data

• Regular least squares for implied volatility computation

• Minimum amount variance portfolios and optimum come back portfolios

• Worth at Risk and portfolio VaR

Linear Algebra Topics

• LU and Cholesky decompositions and linear solvers

• Optimal solvers for tridiagonal symmetric positive matrices

• Common least squares and linear regression

• Linear Transformation Property or home

• Efficient cubic spline interpolation

• Multivariate regular random variables

The reserve is written in an identical spirit as the very best offering ``A Primer for the Mathematics of Financial Engineering” by the same writer, and really should accordingly be beneficial to a similarly huge target audience:

• Prospective students for monetary engineering or mathematical financing programs can self-study material that may prove very important within their future research

• Finance practitioners will see mathematical underpinnings for most methods found in practice, furthering the capability to expand upon these procedures

• Academics teaching economic engineering courses can use this reserve as textbook, or as reference publication for numerical linear algebra strategies with economic applications. The reserve consists of many such applications, in addition to pseudocodes, numerical good examples, and questions frequently asked in interviews for quantitative positions.



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