Henrik Hult

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Within-die process variations arise during integrated circuit (IC) fabrication in the sub-100nm regime. These variations are of paramount concern as they deviate the performance of ICs from their designers' original intent. These deviations reduce the parametric yield and revenues from integrated circuit fabrication. In this paper we provide a complete(More)
In this paper we study the asymptotic decay of finite time ruin probabilities for an insurance company that faces heavy-tailed claims, uses predictable investment strategies and makes investments in risky assets whose prices evolve according to quite general semimartingales. We show that the ruin problem corresponds to determining hitting probabilities for(More)
State-dependent importance sampling algorithms based on mixtures are considered. The algorithms are designed to compute tail probabilities of a heavy-tailed random walk. The increments of the random walk are assumed to have a regularly varying distribution. Sufficient conditions for obtaining bounded relative error are presented for rather general mixture(More)
In some recent papers (Elliott and van der Hoek, 2003; Hu and Øksendal, 2003) a fractional Black-Scholes model have been proposed as an improvement of the classical Black-Scholes model (see also Benth, 2003; Biagini et al., 2002; Biagini and Øksendal, 2004). Common to these fractional Black-Scholes models, is that the driving Brownian motion is replaced by(More)
2013. Rare-event simulation for stochastic recurrence equations with heavy-tailed innovations. One shortcoming of utilizing the conditional mixture algorithm is the necessity to sample from the conditional distribution. A natural way to get around that is to utilize a scaling based approach introduced in [Hult and Svensson 2012]. When simulating B j+1 given(More)
In this article, rare-event simulation for stochastic recurrence equations of the form <i>X</i><sub><i>n</i>&plus;1</sub>=<i>A</i><sub><i>n</i>&plus;1</sub><i>X</i><sub><i>n</i></sub>&plus;<i>B</i><sub><i>n</i>&plus;1</sub>, <i>X</i><sub>0</sub>=0 is studied, where &lcub;<i>A</i><sub>n</sub>;<i>n</i>&#8805; 1&rcub; and(More)
Importance sampling in the setting of heavy tailed random variables has generally focused on models with additive noise terms. In this work we extend this concept by considering importance sampling for the estimation of rare events in Markov chains of the form X<sub>n+1</sub> = A<sub>n+1</sub>X<sub>n</sub>+B<sub>n+1</sub>; X<sub>0</sub> = 0; where the(More)
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