Applied Quantitative Finance for Equity Derivatives, Third Edition PDF Download

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Applied Quantitative Finance for Equity Derivatives, Third Edition

Applied Quantitative Finance for Equity Derivatives, Third Edition PDF Author: Jherek Healy
Publisher: Lulu.com
ISBN: 9781716190391
Category : Business & Economics
Languages : en
Pages : 536

Book Description
In its third edition, this book presents the most significant equitya derivatives models used these days. It is not a book around esoteric or cutting-edge models, but rather a book on relatively simple and standard models, viewed from the angle of a practitioner. A few key subjects explained in this book are: cash dividends for European, American, or exotic options; issues of the Dupire local volatility model and possible fixes; finite difference techniques for American options and exotics; Non-parametric regression for American options in Monte-Carlo, randomized simulations; the particle method for stochastic-local-volatility model with quasi-random numbers; numerical methods for the variance and volatility swaps; quadratures for options under stochastic volatility models; VIX options and dividend derivatives; backward/forward representation of exotics. The January 2021 third edition adds significant details around the physical exercise feature, how to imply the Black-Scholes volatility, the projected successive over-relaxation as well as the recent policy iteration method for the pricing of American options (particularly relevant in the case of negative interest rates), the Andersen-Lake algorithm as fast pricing routine for the case of vanilla American options under the Black-Scholes model, random number generation, antithetic variates, the vectorization of the Monte-Carlo simulation, RBF interpolation of implied volatilities, the Cos method for European option under stochastic volatility models, the Vega in stochastic volatility models. The new text also includes important corrections around the pricing of forward starting and knock-in options with finite difference methods.