Innovative modeling techniques that overcome fundamental flaws in Black-Scholes and other option pricing models "An intriguing, in-depth look at what really constitutes option pricing theory. Plus, some interesting thoughts on projecting and assessing volatility. A must read for those who are looking for more advanced option modeling techniques." --Lawrence G. McMillan, author of Options as a Strategic Investment and McMillan on Options "Refreshingly grounded in empirical data and a spirit of numerical experimentation, this book also succeeds at explaining the arcane mathematical concepts of options pricing in straightforward, clear language. The reader will come away not just with the accepted wisdom of the field, but also with a good sense of how to test that wisdom against real data." --William H. Press, senior author of the Numerical Recipes book and software series "Katz and McCormick's rigorous data studies and their experiences as traders lead to useful solutions for option modeling. In addition, their ability to write in clear English should be studied by all financial authors." --Howard L. Simons, president, Rosewood Trading, Inc., and contributing editor, Futures magazine Popular option pricing models, from Black-Scholes to Cox-Ross-Rubinstein, have been shown to consistently break down under certain market conditions. Advanced Option Pricing Models outlines well-researched and tested alternatives to these models. It also shows traders how to design and implement models that are consistent with the distributional quirks of the underlying markets, while providing more accurate pricing estimates under a wider range of market conditions. "In thisbook, we have analyzed standard option pricing models, discovered their flaws, and investigated better estimators of volatility and other model inputs. We have also explored nonstandard, rather innovative ways to achieve more accurate appraisals of option value. It is our sincere hope that this will give you the edge you need in the tough options game." --From the Introduction Option trading is a fiercely competitive pursuit in which pricing models are essential for evaluating various strategies and estimating the payoff of each under different market scenarios. Unfortunately, standard option pricing models from Black-Scholes to Cox-Ross-Rubinstein can provide inaccurate conclusions under numerous market conditions, leading traders to arrive at erroneous pricing estimates and initiate unprofitable trades. Advanced Option Pricing Models takes an objective, scientific look at this problem, detailing specific conditions under which standard option pricing models fail to provide accurate price estimates. The book then shows how to construct nonstandard models that effectively compensate for those flaws and provide a profitable competitive edge over traders still using older models. This solidly researched book explores: Factors that influence fair value, along with mathematical concepts required when developing pricing models Moments of returns, and how differences in their levels under a variety of conditions affect both the fair value and real-market price of options Models that combine two or more variables to provide distinctly better estimations of future volatility How nonlinear models such as neural networks and polynomial regressions can be fit to data derived fromactual stock and option prices, and outperform Black-Scholes on such data How to build models designed to identify and exploit gross option mispricings that can quickly appear--and, just as instantaneously, disappear A thorough understanding of the factors that influence option prices is essential for the development of an effective pricing model. Advanced Option Pricing Models analyzes standard pricing models, revealing their strengths as well as their fundamental flaws, then introduces nonstandard approaches that option analysts and traders can use to reach more consistently accurate, and profitable, appraisals of option value.
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Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.