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High-dimensional problems frequently arise in the pricing of derivative securities – for example, in pricing options on multiple underlying assets and in pricing term structure derivatives. American versions of these options, ie, where the owner has the right to exercise early, are particularly challenging to price. We introduce a stochastic mesh method for… (More)

Simulation has proved to be a valuable tool for estimating security prices for which simple closed form solutions do not exist. In this paper we present two direct methods, a pathwise method and a likelihood ratio method, for estimating derivatives of security prices using simulation. With the direct methods, the information from a single simulation can be… (More)

- JESSICA A. WACHTER, Robert Barro, +13 authors Pietro Veronesi
- 2008

Why is the equity premium so high, and why are stocks so volatile? Why are stock returns in excess of government bill rates predictable? This paper proposes an answer to these questions based on a time-varying probability of a consumption disaster. In the model, aggregate consumption follows a normal distribution with low volatility most of the time, but… (More)

- Paul Glasserman, Jingyi Li
- Management Science
- 2005

M onte Carlo simulation is widely used to measure the credit risk in portfolios of loans, corporate bonds, and other instruments subject to possible default. The accurate measurement of credit risk is often a rare-event simulation problem because default probabilities are low for highly rated obligors and because risk management is particularly concerned… (More)

The payoff of a barrier option depends on whether or not a specified asset price, index, or rate reaches a specified level during the life of the option. Most models for pricing barrier options assume continuous monitoring of the barrier; under this assumption, the option can often be priced in closed form. Many (if not most) real contracts with barrier… (More)

This paper develops a variance reduction technique for Monte Carlo simulations of path-dependent options driven by high-dimensional Gaussian vectors. The method combines importance sampling based on a change of drift with stratified sampling along a small number of key dimensions. The change of drift is selected through a large deviations analysis and is… (More)

The views expressed here are those of the author(s) and not necessarily those of the Federal Deposit Insurance Corporation Abstract We consider the problem of decomposing the credit risk in a portfolio into a sum of risk contributions associated with individual obligors or transactions. For some standard measures of risk — including value-at-risk and… (More)

- Paul Glasserman, Philip Heidelberger, Perwez Shahabuddin, Tim Zajic
- Operations Research
- 1999

1 Motivation The estimation of rare event probabilities poses some of the of the most diicult computational challenges for Monte Carlo simulation and, at the same time, some of the greatest opportunities for eeciency improvement through the use of variance reduction techniques. Current i n terest in rare events stems primarily from developments in computer… (More)

- Paul Glasserman, Steven Kou
- ACM Trans. Model. Comput. Simul.
- 1995

We analyze the performance of an importance sampling estimator for a rare-event probability in tandem Jackson networks. The rare event we consider corresponds to the network population reaching <italic>K</italic> before returning to ø, starting from ø, with <italic>K</italic> large. The estimator we study is based on interchanging the arrival rate… (More)

- Paul Glasserman, Sridhar R. Tayur
- Operations Research
- 1994

Most models of multilevel production and distribution systems assume unlimited production capacity at each site. When capacity limits are introduced, an ineffective policy may lead to increasingly large order backlogs: The stability of the system becomes an issue. In this paper, we examine the stability of a multi-echelon system in which each node has… (More)