financial trading and market micro-structure mgt 4850 spring 2011 university of lethbridge
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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– Column vector
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
• 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
• 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
• A linear combination of the matrices A1,A2, . . . , An is an expression of the form c1A1 + c2A2 + ・ ・ ・ + cnAn
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)
• (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 basic calculations
• Two-Asset examples– Correlation and Covariance– Trend line
• Portfolio Means and Variances
• Matrix Notation
• Efficient Portfolios
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
• Sum
• Scalar 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 product AB is the m-by-p matrix (m rows, p columns).
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:
• (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
• Linear combinations
• Single-index model
• Multi-index model
• Stochastic Dominance
Return
• The expected return of a portfolio is a weighted average of the expected returns of the components:
1
1
( ) ( )
where proportion of portfolio
invested in security and
1
n
p i ii
i
n
ii
E R x E R
x
i
x
Two-Security Case
• For a two-security portfolio containing Stock A and Stock B, the variance is:
2 2 2 2 2 2p A A B B A B AB A Bx x x x
portfolio variance
• For an n-security portfolio, the portfolio variance is:
2
1 1
where proportion of total investment in Security
correlation coefficient between
Security and Security
n n
p i j ij i ji j
i
ij
x x
x i
i j
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)
• For a two-security minimum variance portfolio, the proportions invested in stocks A and B are:
2
2 2 2
1
B A B ABA
A B A B AB
B A
x
x x
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
• 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
• 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)
• The general form of a multi-index model:
1 1 2 2 ...
where constant
return on the market index
return on an industry index
Security 's beta for industry index
Security 's market beta
retur
i i im m i i in n
i
m
j
ij
im
i
R a I I I I
a
I
I
i j
i
R
n on Security i
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)