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Leonardo Rojas-Nandayapa

Publications and source records attributed to Leonardo Rojas-Nandayapa.

7 recordsLinked to original sources

Approximation of Ruin Probabilities via Erlangized Scale Mixtures

In this paper, we extend an existing scheme for numerically calculating the probability of ruin of a classical Cramér--Lundberg reserve process having absolutely continuous but otherwise general claim size distributions. We employ a dense class of distributions that we denominate Erlangized scale mixtures (ESM) and correspond to nonnegative and absolutely continuous distributions which can be written as a Mellin--Stieltjes convolution $Π\star G$ of a nonnegative distribution $Π$ with an Erlang distribution $G$. A distinctive feature of such a class is that it contains heavy-tailed distributions. We suggest a simple methodology for constructing a sequence of distributions having the form $Π\star G$ to approximate the integrated tail distribution of the claim sizes. Then we adapt a recent result which delivers an explicit expression for the probability of ruin in the case that the claim size distribution is modelled as an Erlangized scale mixture. We provide simplified expressions for the approximation of the probability of ruin and construct explicit bounds for the error of approximation. We complement our results with a classical example where the claim sizes are heavy-tailed.

math.PR↗

Asymptotic tail behavior of phase-type scale mixture distributions

We consider phase-type scale mixture distributions which correspond to distributions of a product of two independent random variables: a phase-type random variable $Y$ and a nonnegative but otherwise arbitrary random variable $S$ called the scaling random variable. We investigate conditions for such a class of distributions to be either light- or heavy-tailed, we explore subexponentiality and determine their maximum domains of attraction. Particular focus is given to phase-type scale mixture distributions where the scaling random variable $S$ has discrete support --- such a class of distributions has been recently used in risk applications to approximate heavy-tailed distributions. Our results are complemented with several examples.

math.PR↗

Fitting phase--type scale mixtures to heavy--tailed data and distributions

We consider the fitting of heavy tailed data and distribution with a special attention to distributions with a non--standard shape in the "body" of the distribution. To this end we consider a dense class of heavy tailed distributions introduced recently, employing an EM algorithm for the the maximum likelihood estimates of its parameters. We present methods for fitting to observed data, histograms, censored data, as well as to theoretical distributions. Numerical examples are provided with simulated data and a benchmark reinsurance dataset. We empirically demonstrate that our model can provide excellent fits to heavy--tailed data/distributions with minimal assumptions

math.ST↗

Efficient simulation for dependent rare events with applications to extremes

We consider the general problem of estimating probabilities which arise as a union of dependent events. We propose a flexible series of estimators for such probabilities, and describe variance reduction schemes applied to the proposed estimators. We derive efficiency results of the estimators in rare-event settings, in particular those associated with extremes. Finally, we examine the performance of our estimators in a numerical example.

math.PR↗

Approximating the Laplace transform of the sum of dependent lognormals

Let $(X_1, \dots, X_n)$ be multivariate normal, with mean vector $\boldsymbolμ$ and covariance matrix $\boldsymbolΣ$, and $S_n=\mathrm{e}^{X_1}+\cdots+\mathrm{e}^{X_n}$. The Laplace transform ${\cal L}(θ)=\mathbb{E}\mathrm{e}^{-θS_n} \propto \int \exp\{-h_θ(\boldsymbol{x})\} \,\mathrm{d} \boldsymbol{x}$ is represented as $\tilde{\cal L}(θ)I(θ)$, where $\tilde{\cal L}(θ)$ is given in closed-form and $I(θ)$ is the error factor ($\approx 1$). We obtain $\tilde{\cal L}(θ)$ by replacing $h_θ(\boldsymbol{x})$ with a second order Taylor expansion around its minimiser $\boldsymbol{x}^*$. An algorithm for calculating the asymptotic expansion of $\boldsymbol{x}^*$ is presented, and it is shown that $I(θ)\to 1$ as $θ\to\infty$. A variety of numerical methods for evaluating $I(θ)$ are discussed, including Monte Carlo with importance sampling and quasi-Monte Carlo. Numerical examples (including Laplace transform inversion for the density of $S_n$) are also given.

math.PR↗

Exponential Family Techniques for the Lognormal Left Tail

Let $X$ be lognormal$(μ,σ^2)$ with density $f(x)$, let $θ>0$ and define ${L}(θ)=E e^{-θX}$. We study properties of the exponentially tilted density (Esscher transform) $f_θ(x) =e^{-θx}f(x)/{L}(θ)$, in particular its moments, its asymptotic form as $θ\to\infty$ and asymptotics for the Cramér function; the asymptotic formulas involve the Lambert W function. This is used to provide two different numerical methods for evaluating the left tail probability of lognormal sum $S_n=X_1+\cdots+X_n$: a saddlepoint approximation and an exponential twisting importance sampling estimator. For the latter we demonstrate the asymptotic consistency by proving logarithmic efficiency in terms of the mean square error. Numerical examples for the c.d.f.\ $F_n(x)$ and the p.d.f.\ $f_n(x)$ of $S_n$ are given in a range of values of $σ^2,n,x$ motivated from portfolio Value-at-Risk calculations.

math.PR↗

Non-Existence of Stabilizing Policies for the Critical Push-Pull Network and Generalizations

The push-pull queueing network is a simple example in which servers either serve jobs or generate new arrivals. It was previously conjectured that there is no policy that makes the network positive recurrent (stable) in the critical case. We settle this conjecture and devise a general sufficient condition for non-stabilizability of queueing networks which is based on a linear martingale and further applies to generalizations of the push-pull network.

math.PR↗