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FINANCIAL TRADING AND MARKET MICRO-STRUCTURE. MGT 4850 Spring 2011 University of Lethbridge. Topics. The power of Numbers Quantitative Finance Risk and Return Asset Pricing Risk Management and Hedging Volatility Models Matrix Algebra. MATRIX ALGEBRA. Definition Row vector

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FINANCIAL TRADING AND MARKET MICRO-STRUCTURE

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Financial trading and market micro structure

FINANCIAL TRADING AND MARKET MICRO-STRUCTURE

MGT 4850

Spring 2011

University of Lethbridge


Topics

Topics

  • The power of Numbers

  • Quantitative Finance

  • Risk and Return

  • Asset Pricing

  • Risk Management and Hedging

  • Volatility Models

  • Matrix Algebra


Matrix algebra

MATRIX ALGEBRA

  • Definition

    • Row vector

    • Column vector


Matrix addition and scalar multiplication

Matrix Addition and Scalar Multiplication

  • Definition: Two matrices A = [aij] and B = [bij ] are said to be equal if Equality of

    these matrices have the same size, and for each index pair (i, j), aij = bij , Matrices

    that is, corresponding entries of A and B are equal.


Matrix addition and subtraction

Matrix Addition and Subtraction

  • Let A = [aij] and B = [bij] be m × n matrices. Then the sum of the matrices, denoted by A + B, is the m × n matrix defined by the formula A + B = [aij + bij ] .

  • The negative of the matrix A, denoted by −A, is defined by the formula −A = [−aij ] .

  • The difference of A and B, denoted by A−B, is defined by the formula A − B = [aij − bij ] .


Scalar multiplication

Scalar Multiplication

  • Let A = [aij] be an m × n matrix and c a scalar. Then the product of the scalar c with the matrix A, denoted by cA, is defined by the formula Scalar cA = [caij ] .


Linear combinations

Linear Combinations

  • A linear combination of the matrices A1,A2, . . . , An is an expression of the form c1A1 + c2A2 + ・ ・ ・ + cnAn


Laws of arithmetic

Laws of Arithmetic

  • Let A,B,C be matrices of the same size m × n, 0 the m × n zero

  • matrix, and c and d scalars.

  • (1) (Closure Law) A + B is an m × n matrix.

  • (2) (Associative Law) (A + B) + C = A + (B + C)

  • (3) (Commutative Law) A + B = B + A

  • (4) (Identity Law) A + 0 = A

  • (5) (Inverse Law) A + (−A) = 0

  • (6) (Closure Law) cA is an m × n matrix.


Laws of arithmetic ii

Laws of Arithmetic (II)

  • (7) (Associative Law) c(dA) = (cd)A

  • (8) (Distributive Law) (c + d)A = cA + dA

  • (9) (Distributive Law) c(A + B) = cA + cB

  • (10) (Monoidal Law) 1A = A


Portfolio models

Portfolio Models

  • Portfolio basic calculations

  • Two-Asset examples

    • Correlation and Covariance

    • Trend line

  • Portfolio Means and Variances

  • Matrix Notation

  • Efficient Portfolios


Review of matrices

Review of Matrices

  • a matrix (plural matrices) is a rectangular table of numbers, consisting of abstract quantities that can be added and multiplied.


Adding and multiplying matrices

Adding and multiplying matrices

  • Sum

  • Scalar multiplication


Matrix multiplication

Matrix multiplication

  • Well-defined only if the number of columns of the left matrix is the same as the number of rows of the right matrix. If A is an m-by-n matrix and B is an n-by-p matrix, then their matrix productAB is the m-by-p matrix (m rows, p columns).


Matrix multiplication1

Matrix multiplication

  • Note that the number of of columns of the left matrix is the same as the number of rows of the right matrix , e. g. A*B →A(3x4) and B(4x6) then product C(3x6).

  • Row*Column if A(1x8); B(8*1) →scalar

  • Column*Row if A(6x1); B(1x5) →C(6x5)


Matrix multiplication properties

Matrix multiplication properties:

  • (AB)C = A(BC) for all k-by-m matrices A, m-by-n matrices B and n-by-p matrices C ("associativity").

  • (A + B)C = AC + BC for all m-by-n matrices A and B and n-by-k matrices C ("right distributivity").

  • C(A + B) = CA + CB for all m-by-n matrices A and B and k-by-m matrices C ("left distributivity").


The mathematics of diversification

The Mathematics of Diversification

  • Linear combinations

  • Single-index model

  • Multi-index model

  • Stochastic Dominance


Return

Return

  • The expected return of a portfolio is a weighted average of the expected returns of the components:


Two security case

Two-Security Case

  • For a two-security portfolio containing Stock A and Stock B, the variance is:


Portfolio variance

portfolio variance

  • For an n-security portfolio, the portfolio variance is:


Minimum variance portfolio

Minimum Variance Portfolio

  • The minimum variance portfolio is the particular combination of securities that will result in the least possible variance

  • Solving for the minimum variance portfolio requires basic calculus


Minimum variance portfolio cont d

Minimum Variance Portfolio (cont’d)

  • For a two-security minimum variance portfolio, the proportions invested in stocks A and B are:


The n security case cont d

The n-Security Case (cont’d)

  • A covariance matrix is a tabular presentation of the pairwise combinations of all portfolio components

    • The required number of covariances to compute a portfolio variance is (n2 – n)/2

    • Any portfolio construction technique using the full covariance matrix is called a Markowitz model


Computational advantages

Computational Advantages

  • The single-index model compares all securities to a single benchmark

    • An alternative to comparing a security to each of the others

    • By observing how two independent securities behave relative to a third value, we learn something about how the securities are likely to behave relative to each other


Multi index model

Multi-Index Model

  • A multi-index model considers independent variables other than the performance of an overall market index

    • Of particular interest are industry effects

      • Factors associated with a particular line of business

      • E.g., the performance of grocery stores vs. steel companies in a recession


Multi index model cont d

Multi-Index Model (cont’d)

  • The general form of a multi-index model:


Portfolio mean and variance

Portfolio Mean and Variance

  • Matrix notation; column vector Γ for the weights transpose is a row vector ΓT

  • Expected return on each asset as a column vector or E its transpose ET

  • Expected return on the portfolio is a scalar

    (row*column)

    Portfolio variance ΓTS Γ (S var/cov matrix)


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