没钱的兄弟可以在下面跟帖索要，我一到两周集中发送一下

A number of methodologies have been employed to provide decision making solutions to a whole assortment of financial problems in today's globalized markets. Hidden Markov Models in Finance by Mamon and Elliott will be the first systematic application of these methods to some special kinds of financial problems; namely, pricing options and variance swaps, valuation of life insurance policies, interest rate theory, credit risk modeling, risk management, analysis of future demand and inventory level, testing foreign exchange rate hypothesis, and early warning systems for currency crises. This book provides researchers and practitioners with analyses that allow them to sort through the random "noise" of financial markets (i.e., turbulence, volatility, emotion, chaotic events, etc.) and analyze the fundamental components of economic markets. Hence, Hidden Markov Models in Finance provides decision makers with a clear, accurate picture of core financial components by filtering out the random noise in financial markets.

[此贴子已经被作者于2008-2-2 23:46:34编辑过]

Publisher: Springer; 1 edition (April 24, 2007)

Language: English

ISBN-10: 0387710817

ISBN-13: 978-0387710815

Product Dimensions: 9.2 x 6 x 0.7 inches

Shipping Weight: 15.2 ounces 

Average Customer Review:＜script type="text/javascript"＞＜/script＞＜script type="text/javascript"＞＜/script＞

Hidden Markov Models have come into vogue in recent years in various fields. Notably automatic speech recognition. An HMM is useful in a Bayesian context, where you have to work back from some observations to discern an underlying probability model that is supposedly generating those observations. Often in the presence of noise. Well, it turns out that this general description can also be applied to financial models, which is the book's subject.

Various specific models are tackled. Including the seminal Black-Scholes, where the security market is modelled as a Markov modulated Brownian. Typically, the maths in the book uses sophisticated probabilistic analysis and often assuming Markov processes. As an aside, if your field is electrical engineering or information theory, where you might have used Markov processes, then your background should suffice if you want to migrate to finance. It's not that different, at a certain conceptual level.

这应该属于统计方面，故发在这里，如果不行的话移动到金融区，谢谢

太黑了