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Bayesian Value-at-Risk and Expected Shortfall for a Large Portfolio (Multi- and Univariate Approaches)

100%

Pajor A. , Osiewalski J.

nr 2B

B-101-B-109

Bayesian assessments of value-at-risk and expected shortfall for a given portfolio of dimension n can be based either on the n-variate predictive distribution of future returns of individual assets, or on the univariate model for portfolio volatility. In both cases, the Bayesian VaR and ES fully take into account parameter uncertainty and non-linear relationship between ordinary and logarithmic returns. We use the n-variate type I MSF-SBEKK(1,1) volatility model proposed specially to cope with large n. We compare empirical results obtained using this (more demanding) multivariate approach and the much simpler univariate approach based on modelling volatility of the whole portfolio (of a given structure).

Regularization of star bodies by random hyperplane cut off

100%

Milman V. , Pajor A.

tom 159

nr 2

247-261

We present a general result on regularization of an arbitrary convex body (and more generally a star body), which gives and extends global forms of a number of well known local facts, like the low M*-estimates, large Euclidean sections of finite volume-ratio spaces and others.

On the limiting empirical measure of eigenvalues of the sum of rank one matrices with log-concave distribution

100%

Pajor A. , Pastur L.

Studia Mathematica

2009

tom 195

nr 1

11-29

We consider n × n real symmetric and hermitian random matrices Hₙ that are sums of a non-random matrix $Hₙ^{(0)}$ and of mₙ rank-one matrices determined by i.i.d. isotropic random vectors with log-concave probability law and real amplitudes. This is an analog of the setting of Marchenko and Pastur [Mat. Sb. 72 (1967)]. We prove that if mₙ/n → c ∈ [0,∞) as n → ∞, and the distribution of eigenvalues of $Hₙ^{(0)}$ and the distribution of amplitudes converge weakly, then the distribution of eigenvalues of Hₙ converges weakly in probability to the non-random limit, found by Marchenko and Pastur.

Random ε-nets and embeddings in $ℓ^{N}_{∞}$

80%

Gordon Y. , Litvak A. , Pajor A. , Tomczak-Jaegermann N.

Studia Mathematica

2007

tom 178

nr 1

91-98

We show that, given an n-dimensional normed space X, a sequence of $N = (8/ε)^{2n}$ independent random vectors $(X_{i})_{i=1}^{N}$, uniformly distributed in the unit ball of X*, with high probability forms an ε-net for this unit ball. Thus the random linear map $Γ: ℝ → ℝ^{N}$ defined by $Γx = (⟨x,X_{i}⟩)_{i=1}^{N}$ embeds X in $ℓ^{N}_{∞}$ with at most 1 + ε norm distortion. In the case X = ℓ₂ⁿ we obtain a random 1+ε-embedding into $ℓ_{∞}^{N}$ with asymptotically best possible relation between N, n, and ε.