Financial Models with Levy Processes and Volatility Clustering

FINANCIAL MODELS WITH LéVY PROCESSES AND VOLATILITY CLUSTERING The failure of financial models has been identified by some market observers as a major contributor to the global financial crisis. More specifically, it's been argued that the underlying assumption made in most of these models-that distributions of prices and returns are normally distributed-have been responsible for their undoing. Financial crises and black swan events may not be precisely predictable by models, but improving the reliability and flexibility of those models is essential for both financial practitioners and academics intent on limiting the impact of major market crashes. In Financial Models with Lévy Processes and Volatility Clustering, authors Svetlozar Rachev, Young Shin Kim, Michele Leonardo Bianchi, and Frank Fabozzi focus on the application of non-normal distributions for modeling the behavior of stock price returns. Opening with a brief introduction to the basics of probability distributions, this practical resource quickly moves on to: Address a wide array of methods for the simulation of infinitely divisible distributions and Lévy processes with a view toward option pricing. Discuss two approaches to deal with non-normal multivariate distributions, providing insight into portfolio allocation assuming a multi-tail t distribution and a non-Gaussian multivariate model. Examine discrete time option pricing models with volatility clustering-namely non-Gaussian GARCH models. Provide guidance on pricing American options with Monte Carlo methods. If you want to gain a better understanding of how financial models can be used to capture the dynamics of economic and financial variables, Financial Models with Lévy Processes and Volatility Clustering is the best place to start.