books by Paul Glasserman (probability engineering derivatives)

books by Paul Glasserman

books:

Monte Carlo Methods in Financial Engineering (Stochastic Modelling and Applied Probability)

20 reviews
Paul Glasserman

Springer, 2003

Very well written
I must admit it is magnificently written book. Not just the main thematical issues but also appendix, where, compared to other similar publications, Girsanov theorem is thoroughly explained. All in all it's a very useful book for everyone who has some basic information about stochastic processes and wants to learn something more about it or deepen their's knowledges. Well done.

Monotone Str in Discrete-Event Systems (Wiley Series in Probability and Statistics)
Paul Glasserman, David D. Yao

Wiley-Interscience, 1994

Information previously available only in journal articles and research papers has been brought together in this outstanding text. Uses the unifying theme of monotone structure to transcend the two-perspective approach to DES--one stressing logical/qualitative issues and the other temporal/quantitative analysis--to encompass elements from both. Features notes and references at the end of each chapter.

Gradient Estimation Via Perturbation Analysis (The Springer International Series in Engineering and Computer ...
Paul Glasserman

Springer, 1990

Monte Carlo Methods in Financial Engineering
Paul Glasserman

Springer Verlag, 2003

Hedging With Trees Advances in Pricing and Risk Managing Derivatives

Risk Books, 1998

*An edited compilation of the best technical articles published in RISK magazine from 1995-98 *Developments in exotic options, interest rate modelling, and computational techniques *Advances in market risk, VAR and capital management *Original introductory overviews by Mark Broadie and Paul Glasserman

Stochastic Networks (Lecture Notes in Statistics)

Springer, 1996

Two of the most exciting topics of current research in stochastic networks are the complementary subjects of stability and rare events. Both are classical topics that have experienced renewed interest motivated by new applications to emerging technologies. For example, new stability issues arise in the scheduling of multiple classes in semiconductor manufacturing, the so-called "re-entrant lines," and a prominent need for studying rare events is ...