Math 576: Quantitative Risk Management


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1 Math 576: Quantitative Risk Management Haijun Li Department of Mathematics Washington State University Week 4 Haijun Li Math 576: Quantitative Risk Management Week 4 1 / 22
2 Outline 1 Basics of Multivariate Modelling 2 The Multivariate Normal Distribution Haijun Li Math 576: Quantitative Risk Management Week 4 2 / 22
3 Notation R d = the ddimensional Euclidean space. R d k = the space of all d k matrices. For any a, b R d, the rectangular region [a, b] := d [a i, b i ], a = (a 1,..., a d ), b = (b 1,..., b d ), i=1 is called a multivariate closed interval. The intervals [a, b), (a, b], etc, are defined similarly. For any a = (a 1,..., a d ), b = (b 1,..., b d ), a b a i b i, i = 1,..., d. The inequality a < b is defined similarly componentwise. Haijun Li Math 576: Quantitative Risk Management Week 4 3 / 22
4 Notation (cont d) Random (row) vectors: X = (X 1,..., X d ), etc. The transpose X is a column vector. The multivariate distribution F(x) = P(X 1 x 1,..., X d x d ) = P(X (, x]), x = (x 1,..., x d ) R d. The marginal distribution of X i is given by F i (x i ) = P(X i x i ) = F(,...,, x }{{} i,,..., ), x }{{} i R. i 1 n i Write X = (X 1,..., X k, X k+1,..., X d ). The multivariate marginal distribution of (X 1,..., X k ) is give by F {1,...,k} (x 1,..., x k ) = P(X 1 x 1,..., X k x k ) = F(x 1,..., x k,,..., ). }{{} n k Haijun Li Math 576: Quantitative Risk Management Week 4 4 / 22
5 Notation (cont d) The joint survival function F(x) = P(X 1 > x 1,..., X d > x d ) = P(X (x, )), x = (x 1,..., x d ) R d. The marginal survival distribution of X i is given by F i (x i ) = P(X i > x i ) = F(,...,, x }{{} i,,..., ), x }{{} i R. i 1 n i A random vector X is said to be absolutely continuous if F(x) = x1 xd f (t 1,..., t d ) dt 1 dt }{{ d, x = (x } 1,..., x d ) R d, dt where f (t 1,..., t d ) is known as the joint density at (t 1,..., t d ) R d. The notion of densities is local. In fact, for any Borel subset A R d, P(X A) = f (t)dt. Haijun Li Math 576: Quantitative Risk Management Week 4 5 / 22 A
6 Conditional Distributions X 1 {}}{{}}{ Write X = ( X 1,..., X k, X k+1,..., X d ) X 2 Let X = (X 1, X 2 ) have the joint distribution F(x) with multivariate margins F 1 (x 1 ) and F 2 (x 2 ). Let f (t) denote the density of X having multivariate marginal densities f 1 (t 1 ) and f 2 (t 2 ). The conditional density function of X 2 given that X 1 = t 1 is given by f 2 1 (t 2 t 1 ) := f (t 1, t 2 ) f (t 1 ), t 1 R k, t 2 R n k. The conditional distribution of X 2 given that X 1 = x 1 is given by F 2 1 (x 2 x 1 ) = P(X 2 x 2 X 1 = x 1 ) = f 2 1 (t 2 x 1 )dt 2. (,x 2 ] Haijun Li Math 576: Quantitative Risk Management Week 4 6 / 22
7 Expectations Let g : R d R, and h : R n k R be Borelmeasurable. The expectation: E[g(X)] := g(x)df (x) = g(x)f (x)dx. R d R } d {{} if the density exists If the density exists, the conditional expectation is defined as E[h(X 2 ) X 1 = x 1 ] := h(t 2 )f 2 1 (t 2 x 1 )dt 2. R n k The expectation E[h(X 2 ) X 1 ] is a function of random vector X 1. Haijun Li Math 576: Quantitative Risk Management Week 4 7 / 22
8 Independence Let X = (X 1, X 2 ) have the joint distribution F(x 1, x 2 ) with multivariate margins F 1 (x 1 ) and F 2 (x 2 ). X 1 and X 2 are independent, denoted as X 1 X 2, if P(X 2 B X 1 A) = P(X 2 B), Borel sets A R k, B R n k. In terms of distribution functions, X 1 X 2 if and only if F(x 1, x 2 ) = F 1 (x 1 )F 2 (x 2 ), x = (x 1, x 2 ) R k R n k. In the case that the density exists, X 1 X 2 if and only if f (t 1, t 2 ) = f 1 (t 1 )f 2 (t 2 ), t = (t 1, t 2 ) R k R n k. In terms of expectations, X 1 X 2 if and only if E[h(X 2 ) X 1 ] = E[h(X 2 )], h : R n k R. Haijun Li Math 576: Quantitative Risk Management Week 4 8 / 22
9 Moments The mean vector of X = (X 1,..., X d ) is defined as µ = E(X) = (E(X 1 ),..., E(X d )). The covariance matrix of X = (X 1,..., X d ) is defined as Σ = Cov(X) = E[(X µ) (X µ)] R d d. If Σ = (σ ij ) d d R d d, then the covariance of X i and X j is given by σ ij = E[X i X j ] E(X i )E(X j ), 1 i, j d. σ ii = E(X 2 i ) [E(X i )] 2 =: σ 2 i is known as the variance of X i. The correlation of X i and X j is a rescaled covariance: ρ ij := σ ij σii σ jj. The matrix (ρ ij ) d d is known as the correlation matrix. Haijun Li Math 576: Quantitative Risk Management Week 4 9 / 22
10 Remark Higher order moments can be obtained from the moment generating function E(exp{Xt }). For any matrix B R d k, any vector b R k, E(XB + b) = E(X)B + b. Cov(XB + b) = Cov(XB) = B Cov(X)B. Any covariance matrix is positive semidefinite. Haijun Li Math 576: Quantitative Risk Management Week 4 10 / 22
11 Standard Estimators of Mean and Covariance Suppose we have n iid observations, X 1,..., X n, of a ddimensional riskfactor change vector X. The sample mean vector: X := 1 n n X i E(X), as n. i=1 The sample covariance matrix: S := 1 n 1 n (X i X) (X i X) Cov(X), as n. i=1 Both estimators are unbiased. Haijun Li Math 576: Quantitative Risk Management Week 4 11 / 22
12 The Multivariate Normal Distribution Definition 1 Let Z = (Z 1,..., Z k ), where Z 1,..., Z k are iid with standard normal density N(0, 1): ϕ(x) = 1 2π e x2 2, x R. 2 X = (X 1,..., X d ) N d (µ, Σ) has a multivariate normal distribution if X d = µ + ZA, for some matrix A R k d. E(X) = µ, and Cov(X) = Σ = A A (Cholesky decomposition). If Σ is invertible, the normal density is given by 1 ϕ d (x) = (2π) d/2 exp { 12 } Σ 1/2 (x µ)σ 1 (x µ), x R d }{{} ellipsoid contours Haijun Li Math 576: Quantitative Risk Management Week 4 12 / 22
13 Figure : Normal ellipsoids (x µ)σ 1 (x µ) = c with smaller c lead to higher probability mass concentration. Haijun Li Math 576: Quantitative Risk Management Week 4 13 / 22
14 Cholesky Factorization (AndréLouis Cholesky, 1924) If the covariance matrix Σ is positivedefinite, there exists a square matrix A such that Σ = A A. The matrix A can be constructed using the Cholesky Factorization, and can be chosen as an upper triangular matrix. The matrix A can be also written as λ λ2 0 A = P 0 0 λd where λ 1 λ 2 λ d 0 are the eigenvalues of the covariance matrix Σ, and the matrix P is a d d orthogonal matrix; that is, P P = I (indentity matrix). Haijun Li Math 576: Quantitative Risk Management Week 4 14 / 22
15 Sampling Algorithm with Geometric Interpretation 1 Simulate z 1, z 2,..., z d independently from N(0, 1). 2 Stretch and then rotate stretch { }} { λ1 0 (z 1,..., z d ) λd 3 Translate P = ( λ1 z 1,..., λ d z d ) P } {{ } rotation Cholesky factor A { }} { traslation {}}{ λ1 0 (u 1,..., u d ) = (µ 1,..., µ d )+(z 1,..., z d )..... P. 0 λd Haijun Li Math 576: Quantitative Risk Management Week 4 15 / 22
16 Figure : Starting at standard normal, stretch, rotation, and translation yield an ellipsoid. Haijun Li Math 576: Quantitative Risk Management Week 4 16 / 22
17 Properties of Normal Distributions Let X = (X 1,..., X d ) N d (µ, Σ), where Σ = (σ ij ) d d. The moment generating function is E(e Xt ) = e µt tσt. Any affine transform XB + b N k (µb + b, B ΣB), B R d k, b R k. X N d (µ, Σ) if and only if ax = d a i X i N 1 (aµ, aσa ), a = (a 1,..., a d ) R d. i=1 If Y = (Y 1,..., Y d ) N d (µ, Σ ) and X Y, then the convolution X + Y N d (µ + µ, Σ + Σ ). Haijun Li Math 576: Quantitative Risk Management Week 4 17 / 22
18 Properties of Normal Distributions (cont d) X 1 X 2 {}}{{}}{ Write X = ( X 1,..., X k, X k+1,..., X d ) N d (µ, Σ) with block matrices: ( ) Σ11 Σ µ = (µ 1, µ 2 ), Σ = 12 Σ 21 Σ 22 Then the multivariate margins X 1 N k (µ 1, Σ 11 ), X 2 N n k (µ 2, Σ 22 ). Haijun Li Math 576: Quantitative Risk Management Week 4 18 / 22
19 Properties of Normal Distributions (cont d) X 1 X 2 {}}{{}}{ Write X = ( X 1,..., X k, X k+1,..., X d ) N d (µ, Σ) with block matrices: ( ) Σ11 Σ µ = (µ 1, µ 2 ), Σ = 12 Σ 21 Σ 22 Then [X 2 X 1 = x 1 ] N n k (µ 2 1, Σ 22 1 ), where µ 2 1 = µ 2 + (x 1 µ 1 )Σ 1 11 Σ 12, Σ 22 1 = Σ 22 Σ 21 Σ 1 11 Σ 12. Haijun Li Math 576: Quantitative Risk Management Week 4 19 / 22
20 Properties of Normal Distributions (cont d) Let X = (X 1,..., X d ) N d (µ, Σ), where Σ is positivedefinite. Then the squared Mahalanobis distance from the mean vector µ: (X µ)σ 1 (X µ) χ 2 d, a chisquared distribution with d degrees of freedom (with density cx d 2 1 e x 2, where c is the normalizing constant). Figure : The squared Mahalanobis distance decays exponentially fast! Haijun Li Math 576: Quantitative Risk Management Week 4 20 / 22
21 Example: Daily returns of the Disney share price There are many numerical tests of normality. A QQplot: Ordered observations are plotted against quantiles of standard normal distribution. A lack of linearity shows evidence against the hypothesized reference normal distribution. Figure : QQplot of daily returns of the Disney share price from 1993 to 2000 against a normal reference distribution Haijun Li Math 576: Quantitative Risk Management Week 4 21 / 22
22 Defects of Multivariate Normal Distribution The tails of its univariate marginal distributions are too thin; they do not assign enough weight to extreme events. The joint tails of the distribution do not assign enough weight to joint extreme outcomes. The distribution has a strong form of symmetry, known as elliptical symmetry. Haijun Li Math 576: Quantitative Risk Management Week 4 22 / 22
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