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Stochastic Modeling in Economics and Finance (Applied Optimization), by Jitka Dupacova, J. Hurt, J. Stepan
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In Part I, the fundamentals of financial thinking and elementary mathematical methods of finance are presented. The method of presentation is simple enough to bridge the elements of financial arithmetic and complex models of financial math developed in the later parts. It covers characteristics of cash flows, yield curves, and valuation of securities.
Part II is devoted to the allocation of funds and risk management: classics (Markowitz theory of portfolio), capital asset pricing model, arbitrage pricing theory, asset & liability management, value at risk. The method explanation takes into account the computational aspects.
Part III explains modeling aspects of multistage stochastic programming on a relatively accessible level. It includes a survey of existing software, links to parametric, multiobjective and dynamic programming, and to probability and statistics. It focuses on scenario-based problems with the problems of scenario generation and output analysis discussed in detail and illustrated within a case study.
- Sales Rank: #3694955 in eBooks
- Published on: 2002-08-31
- Released on: 2002-08-31
- Format: Kindle eBook
Review
From the reviews:
"The monograph presents a complete overview on stochastic modeling in finance and economics. … the mathematical approach is accessible to a wide audience. … A comprehensive bibliography and index complete the book. The volume can be used in introductory graduate courses, and as a reference text for researchers in probability, statistics and operations research … ." (Emilia Di Lorenzo, Zentralblatt MATH, Vol. 1094 (20), 2006)
Most helpful customer reviews
1 of 1 people found the following review helpful.
Another excellent textbook
By Jaroslav Sklenar
A very useful textbook for both undergraduate and advanced courses on stochastic models in finance. The first part is a comprehensive but readable and well understandable introduction to Financial Mathematics. The second part on discrete time stochastic models is based on some background knowledge of Stochastic Programming. The theory is well balanced with many practical examples from various areas. The chapter is concluded by a review of numerical techniques of solving stochastic programs. The third part describes the continuous time financial models based on the theory of Stochastic Processes.
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