Jose H. Blanchet

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Let (Xn :n≥ 0) be a sequence of i.i.d. r.v.’s with negative mean. Set S0 = 0 and define Sn =X1 + · · ·+Xn. We propose an importance sampling algorithm to estimate the tail of M =max{Sn :n≥ 0} that is strongly efficient for both light and heavy-tailed increment distributions. Moreover, in the case of heavy-tailed increments and under additional technical(More)
Consider a sequence (Xk : k ≥ 0) of regularly varying independent and identically distributed random variables with mean 0 and finite variance. We develop efficient rare-event simulation methodology associated with large deviation probabilities for the random walk (Sn : n ≥ 0). Our techniques are illustrated by examples, including large deviations for the(More)
We consider the problem of efficient estimation of tail probabilities of sums of correlated lognormals via simulation. This problem is motivated by the tail analysis of portfolios of assets driven by correlated Black-Scholes models. We propose two estimators that can be rigorously shown to be efficient as the tail probability of interest decreases to zero.(More)
We develop rare-event simulation methodology for the analysis of loss events in a manyserver loss system under quality-driven regime, focusing on the steady-state loss probability (i.e. fraction of lost customers over arrivals) and the behavior of the whole system leading to loss events. The analysis of these events requires working with the full(More)
Two of the most popular approximations for the distribution of the steady-state waiting time, W∞, of the M/G/1 queue are the so-called heavytraffic approximation and heavy-tailed asymptotic, respectively. If the traffic intensity, ρ, is close to 1 and the processing times have finite variance, the heavy-traffic approximation states that the distribution of(More)
This paper studies performance-sensitive debt (PSD), the class of debt obligations whose interest payments depend on some measure of the borrowers performance. We demonstrate that the existence of PSD obligations cannot be explained by the trade-off theory of capital structure, as PSD leads to earlier default and lower equity value compared to fixed-rate(More)
A binary contingency table is an m×n array of binary entries with row sums r = (r1, . . . , rm) and column sums c = (c1, . . . , cn). The configuration model generates a contingency table by considering ri tokens of type 1 for each row i and cj tokens of type 2 for each column j, and then taking a uniformly random pairing between type-1 and type-2 tokens.(More)
This paper develops the first class of algorithms that enable unbiased estimation of steady-state expectations for multidimensional reflected Brownian motion. In order to explain our ideas, we first consider the case of compound Poisson (possibly Markov modulated) input. In this case, we analyze the complexity of our procedure as the dimension of the(More)