This text has grown out of a two-semester course sequence in the Carnegie Mellon Master's program in Computational Finance. It contains numerous examples, exercises, and references. It assumes the reader is familiar with differential and integral calculus and basic concepts from calculus-based probability. It does not assume familiarity with measure-theoretic probability, but rather informally develops the necessary tools from this subject within the text.

Developed for the professional Master's program in Computational Finance at Carnegie Mellon, the leading financial engineering program in the U.S. Tested in the classroom and revised over a period of several years

Autorentext

Steven E. Shreve is Co-Founder of the Carnegie Mellon MS Program in Computational Finance and winner of the Carnegie Mellon Doherty Prize for sustained contributions to education.

Inhalt
1 General Probability Theory.- 2 Information and Conditioning.- 3 Brownian Motion.- 4 Stochastic Calculus.- 5 Risk-Neutral Pricing.- 6 Connections with Partial Differential Equations.- 7 Exotic Options.- 8 American Derivative Securities.- 9 Change of Numéraire.- 10 Term-Structure Models.- 11 Introduction to Jump Processes.- A Advanced Topics in Probability Theory.- A.1 Countable Additivity.- A.3 Random Variable with Neither Density nor Probability Mass Function.- B Existence of Conditional Expectations.- C Completion of the Proof of the Second Fundamental Theorem of Asset Pricing.- References.