New simulation methodology for risk analysis: rare-event, heavy-tailed simulations using hazard function transformations, with applications to value-at-risk

Authors:
Zhi Huang;Perwez Shahabuddin
Affiliations:
Columbia University, New York, NY;Columbia University, New York, NY
Venue:
Proceedings of the 35th conference on Winter simulation: driving innovation
Year:
2003

Citing 5
Cited 2

Fast simulation of rare events in queueing and reliability models

ACM Transactions on Modeling and Computer Simulation (TOMACS)
Modelling extremal events: for insurance and finance

Modelling extremal events: for insurance and finance
Simulating GI/GI/1 queues and insurance risk processes with subexponential distributions

Proceedings of the 32nd conference on Winter simulation
Simulating heavy tailed processes using delayed hazard rate twisting

ACM Transactions on Modeling and Computer Simulation (TOMACS)
Variance Reduction Techniques for Estimating Value-at-Risk

Management Science

A unified approach for finite-dimensional, rare-event Monte Carlo simulation

WSC '04 Proceedings of the 36th conference on Winter simulation
Perwez Shahabuddin, 1962--2005: A professional appreciation

ACM Transactions on Modeling and Computer Simulation (TOMACS)

Quantified Score

Hi-index	0.00

Visualization

Abstract

We develop an observation that a simulation method introduced recently for heavy-tailed stochastic simulation, namely hazard-rate twisting, is equivalent to doing exponential twisting on a transformed version of the heavy-tailed random-variable; the transforming function is the hazard function. Using this approach, the paper develops efficient methods for computing portfolio value-at-risk (VAR) when changes in the underlying risk factors have the multivariate Laplace distribution.