arXiv subjects

Jeffrey F. Collamore

Publications and source records attributed to Jeffrey F. Collamore.

6 recordsLinked to original sources

arXiv2025

Sharp Large Deviations and Gibbs Conditioning for Threshold Models in Portfolio Credit Risk

We obtain sharp large deviation estimates for exceedance probabilities in dependent triangular array threshold models with a diverging number of latent factors. The prefactors quantify how latent-factor dependence and tail geometry enter at leading order, yielding three regimes: Gaussian or exponential-power tails produce polylogarithmic refinements of the Bahadur-Rao $n^{-1/2}$ law; regularly varying tails yield index-driven polynomial scaling; and bounded-support (endpoint) cases lead to an $n^{-3/2}$ prefactor. We derive these results through Laplace-Olver asymptotics for exponential integrals and conditional Bahadur-Rao estimates for the triangular arrays. Using these estimates, we establish a Gibbs conditioning principle in total variation: conditioned on a large exceedance event, the default indicators become asymptotically i.i.d., and the loss-given-default distribution is exponentially tilted (with the boundary case handled by an endpoint analysis). As illustrations, we obtain second-order approximations for Value-at-Risk and Expected Shortfall, clarifying when portfolios operate in the genuine large-deviation regime. The results provide a transferable set of techniques-localization, curvature, and tilt identification-for sharp rare-event analysis in dependent threshold systems.

math.PR↗

arXiv2016

Large excursions and conditioned laws for recursive sequences generated by random matrices

We determine the large exceedance probabilities and large exceedance paths for the matrix recursive sequence $V_n = M_n V_{n-1} + Q_n, \: n=1,2,\ldots,$ where $\{M_n\}$ is an i.i.d. sequence of $d \times d$ random matrices and $\{ Q_n\}$ is an i.i.d. sequence of random vectors, both with nonnegative entries. Early work on this problem dates to Kesten's (1973) seminal paper, motivated by an application to multi-type branching processes. Other applications arise in financial time series modeling (connected to the study of the GARCH($p,q$) processes) and in physics, and this recursive sequence has also been the focus of extensive work in the recent probability literature. In this work, we characterize the distribution of the first passage time $T_u^A := \inf \{n: V_n \in u A \}$, where $A$ is a subset of the nonnegative quadrant in ${\mathbb R}^d$, showing that $T_u^A/u^α$ converges to an exponential law. In the process, we also revisit and refine Kesten's classical estimate, showing that if $V$ has the stationary distribution of $\{ V_n \}$, then ${\mathbb P} \left( V \in uA \right) \sim C_A u^{-α}$ as $u \to \infty$, providing, most importantly, a new characterization of the constant $C_A$. Finally, we describe the large exceedance paths via two conditioned limit laws. In the first, we show that conditioned on a large exceedance, the process $\{ V_n\}$ follows an exponentially-shifted Markov random walk, which we identify, thereby generalizing results for classical random walk to matrix recursive sequences. In the second, we characterize the empirical distribution of $\{ \log |V_n| - \log |V_{n-1}| \}$ prior to a large exceedance, showing that this distribution converges to the stationary law of the exponentially-shifted Markov random walk.

math.PR↗

arXiv2014

Large deviation estimates for exceedance times of perpetuity sequences and their dual processes

In a variety of problems in pure and applied probability, it is of relevant to study the large exceedance probabilities of the perpetuity sequence $Y_n := B_1 + A_1 B_2 + \cdots + (A_1 \cdots A_{n-1}) B_n$, where $(A_i,B_i) \subset (0,\infty) \times {\mathbb R}$. Estimates for the stationary tail distribution of $\{ Y_n \}$ have been developed in the seminal papers of Kesten (1973) and Goldie (1991). Specifically, it is well-known that if $M := \sup_n Y_n$, then ${\mathbb P} \left\{ M > u \right\} \sim {\cal C}_M u^{-ξ}$ as $u \to \infty$. While much attention has been focused on extending this estimate, and related estimates, to more general processes, little work has been devoted to understanding the path behavior of these processes. In this paper, we derive sharp asymptotic estimates for the large exceedance times of $\{ Y_n \}$. Letting $T_u := (\log\, u)^{-1} \inf\{n: Y_n > u \}$ denote the normalized first passage time, we study ${\mathbb P} \left\{ T_u \in G \right\}$ as $u \to \infty$ for sets $G \subset [0,\infty)$. We show, first, that the scaled sequence $\{ T_u \}$ converges in probability to a certain constant $ρ> 0$. Moreover, if $G \cap [0,ρ] \not= \emptyset$, then ${\mathbb P} \left\{ T_u \in G \right\} u^{I(G)} \to C(G)$ as $u \to \infty$ for some "rate function" $I$ and constant $C(G)$. On the other hand, if $G \cap [0,ρ] = \emptyset$, then we show that the tail behavior is actually quite complex, and different asymptotic regimes are possible. We conclude by extending our results to the corresponding forward process, understood in the sense of Letac (1986), namely, the reflected process $M_n^\ast := \max\{ A_n M_{n-1}^\ast + B_n, 0 \}$ for $n \in {\mathbb N}$, where $M_0^\ast=0$.

math.PR↗

arXiv2014

Rare event simulation for processes generated via stochastic fixed point equations

In a number of applications, particularly in financial and actuarial mathematics, it is of interest to characterize the tail distribution of a random variable $V$ satisfying the distributional equation $V\stackrel{\mathcal{D}}{=}f(V)$, where $f(v)=A\max\{v,D\}+B$ for $(A,B,D)\in(0,\infty)\times {\mathbb{R}}^2$. This paper is concerned with computational methods for evaluating these tail probabilities. We introduce a novel importance sampling algorithm, involving an exponential shift over a random time interval, for estimating these rare event probabilities. We prove that the proposed estimator is: (i) consistent, (ii) strongly efficient and (iii) optimal within a wide class of dynamic importance sampling estimators. Moreover, using extensions of ideas from nonlinear renewal theory, we provide a precise description of the running time of the algorithm. To establish these results, we develop new techniques concerning the convergence of moments of stopped perpetuity sequences, and the first entrance and last exit times of associated Markov chains on $\mathbb{R}$. We illustrate our methods with a variety of numerical examples which demonstrate the ease and scope of the implementation.

math.PR↗

arXiv2011

Tail estimates for stochastic fixed point equations via nonlinear renewal theory

This paper presents precise large deviation estimates for solutions to stochastic fixed point equations of the type V =_d f(V), where f(v) = Av + g(v) for a random function g(v) = o(v) a.s. as v tends to infinity. Specifically, we provide an explicit characterization of the pair (C,r) in the tail estimate P(V > u) ~ C u^-r as u tends to infinity, and also present a Lundberg-type upper bound of the form P(V > u) <= D(u) u^-r. To this end, we introduce a novel dual change of measure on a random time interval and analyze the path properties, using nonlinear renewal theory, of the Markov chain resulting from the forward iteration of the given stochastic fixed point equation. In the process, we establish several new results in the realm of nonlinear renewal theory for these processes. As a consequence of our techniques, we also establish a new characterization of the extremal index. Finally, we provide some extensions of our methods to Markov-driven sequences.

math.PR↗

arXiv2009

Random recurrence equations and ruin in a Markov-dependent stochastic economic environment

We develop sharp large deviation asymptotics for the probability of ruin in a Markov-dependent stochastic economic environment and study the extremes for some related Markovian processes which arise in financial and insurance mathematics, related to perpetuities and the $\operatorname {ARCH}(1)$ and $\operatorname {GARCH}(1,1)$ time series models. Our results build upon work of Goldie [Ann. Appl. Probab. 1 (1991) 126--166], who has developed tail asymptotics applicable for independent sequences of random variables subject to a random recurrence equation. In contrast, we adopt a general approach based on the theory of Harris recurrent Markov chains and the associated theory of nonnegative operators, and meanwhile develop certain recurrence properties for these operators under a nonstandard "Gärtner--Ellis" assumption on the driving process.

math.PR↗