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Juan Fernández Sánchez

Publications and source records attributed to Juan Fernández Sánchez.

3 recordsLinked to original sources

On families of bivariate copulas and their interrelation with the Hilbert space l2 and the Hilbert cube H

The Markov kernel based metric $D_1$ was introduced in 2011 in order to construct the scale-invariant dependence measure $ζ_1$, which assign each bivariate copula $C$ a dependence value in $[0,1]$, with $0$ exclusively for the case of independence, and $1$ exclusively for complete/functional dependence. In the original paper it has been shown that the resulting metric space $(\mathcal{C},D_1)$ is separable and complete, however, no further topological properties were studied. Considering that $D_1$ has proved useful in a variety of contexts, using tools from infinite-dimensional topology, we here close this gap, show that $(\mathcal{C},D_1)$ is homeomorphic to the Hilbert space $(\ell_2,\Vert \cdot \Vert_2)$, and prove that several subfamilies are either homeomorphic to $(\ell_2,\Vert \cdot \Vert_2)$ or to the Hilbert cube $(\mathcal{H},ρ)$. Moreover, allowing for a better assessment of relative sizes, we show that various subfamilies are so-called $Z$-sets in $(\mathcal{C},D_1)$, implying that they are topologically negligible in the full space.

math.FA↗

On d-stochastic measures with fractal support and uniform (d-1)-marginals, and related results

The family $\mathcal{P}_{d}^{λ_{d-1}}$ of all probability measures on $[0,1]^d$ whose $(d-1)$-dimensional marginals are all equal to the Lebesgue measure $λ_{d-1}$ on $[0,1]^{d-1}$ contains remarkably pathological elements: Working with Iterated Function Systems with Probabi\-lities (IFSPs) we construct measures $μ\in \mathcal{P}_{d}^{λ_{d-1}}$ of the following two types: (i) $μ$ has self-similar fractal support; (ii) $μ$ has self-similar support and models the situation of complete/functional dependence in each direction.As our main results concerning type (i) we prove, firstly, that for every $d\geq 3$ the set $\mathcal{D}_d$ of Hausdorff dimensions of the supports of elements in $\mathcal{P}_{d}^{λ_{d-1}}$ is dense in $[d-1,d]$; and, secondly, that the subset of elements in $\mathcal{P}_{d}^{λ_{d-1}}$ having fractal support is dense in $\mathcal{P}_{d}^{λ_{d-1}}$ with respect to the Wasserstein metric. Moreover, we show the existence of an element in $\mathcal{P}_{3}^{λ_{2}}$ of type (ii) whose support is a Sierpinski tetrahedron and study some generalizations.

math.PR↗

On distributions with fixed marginals maximizing the joint or the prior default probability, estimation, and related results

We study the problem of maximizing the probability that (i) an electric component or financial institution $X$ does not default before another component or institution $Y$ and (ii) that $X$ and $Y$ default jointly within the class of all random variables $X,Y$ with given univariate continuous distribution functions $F$ and $G$, respectively, and show that the maximization problems correspond to finding copulas maximizing the mass of the endograph $Γ^\leq(T)$ and the graph $Γ(T)$ of $T=G \circ F^-$, respectively. After providing simple, copula-based proofs for the existence of copulas attaining the two maxima $\overline{m}_T$ and $\overline{w}_T$ we generalize the obtained results to the case of general (not necessarily monotonic) transformations $T:[0,1] \rightarrow [0,1]$ and derive simple and easily calculable formulas for $\overline{m}_T$ and $\overline{w}_T$ involving the distribution function $F_T$ of $T$ (interpreted as random variable on $[0,1]$). The latter are then used to charac\-terize all non-decreasing transformations $T:[0,1] \rightarrow [0,1]$ for which $\overline{m}_T$ and $\overline{w}_T$ coincide. A strongly consistent estimator for the maximum probability that $X$ does not default before $Y$ is derived and proven to be asymptotically normal under very mild regularity conditions. Several examples and graphics illustrate the main results and falsify some seemingly natural conjectures.

math.PR↗