Dynamic Asset Pricing Theory - Info and Reading Options
By Darrell Duffie

"Dynamic Asset Pricing Theory" was published by Princeton University Press in 2001 - S. l and it has 328 pages.
“Dynamic Asset Pricing Theory” Metadata:
- Title: Dynamic Asset Pricing Theory
- Author: Darrell Duffie
- Number of Pages: 328
- Publisher: Princeton University Press
- Publish Date: 2001
- Publish Location: S. l
“Dynamic Asset Pricing Theory” Subjects and Themes:
- Subjects: ➤ Capital assets pricing model - Uncertainty - Portfolio management - Pricing - Modèle de fixation du prix des actifs - Gestion de portefeuille - Incertitude - Portfolio-theorie - Kapitaalgoederen - Modellen - Finance - capital - general & miscellaneous - Securities - general & miscellaneous - Investing - strategies
Edition Specifications:
- Pagination: 328 p.
Edition Identifiers:
- The Open Library ID: OL15505336M - OL2946847W
- Online Computer Library Center (OCLC) ID: 45890428 - 32779773
- Library of Congress Control Number (LCCN): 95032122 - 2001021235
- ISBN-10: 0691021252 - 069109022X
- All ISBNs: 0691021252 - 069109022X
AI-generated Review of “Dynamic Asset Pricing Theory”:
"Dynamic Asset Pricing Theory" Description:
The Open Library:
Dynamic Asset Pricing Theory is a textbook for doctoral students and researchers on the theory of asset pricing and portfolio selection in multiperiod settings under uncertainty. The asset pricing results are based on the three increasingly restrictive assumptions: absence of arbitrage, single-agent optimality, and equilibrium. These results are unified with two key concepts, state prices and martingales. Technicalities are given relatively little emphasis so as to draw connections between these concepts and to make plain the similarities between discrete and continuous-time models. For simplicity, all continuous-time models are based on Brownian motion. Applications include term structure models, derivative valuation and hedging methods, and dynamic programming algorithms for portfolio choice and optimal exercise of American options. Numerical methods covered include Monte Carlo simulation and finite-difference solvers for partial differential equations.
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