This book will enable the reader to model, design and implement a range of financial models for derivatives pricing and asset allocation. The book will provide practitioners with the complete financial modeling workflow, from model choice, deriving (semi-) analytic approximate prices and Greeks even for exotic options. Such methods can be used for calibration to market data. Furthermore, Monte Carlo simulation techniques are covered which can be applied to multi-dimensional and path dependent options or some asset allocation problems. Equity/Equity-Interest Rate Hybrid models, Interest Rate models and Asset Allocation are used as examples showing specific models with analysis of their features. The authors then go on to show how to price simple options and how to calibrate the models to real life market data and finally they discuss the pricing of exotic options. At the end of these sections the reader will be able to use the techniques discussed for equity derivatives and interest rate models in other areas of finance such as foreign exchange and inflation. The models discussed for derivatives pricing are: Heston / Bates Model Local/Stochastic Volatility Models (DD, CEV, DDHeston) Lévy Models (Variance-Gamma, Normal Inverse Gaussian) Heston — Hull — White Model Libor Market Model SABR Model Lévy Models with Stochastic Volatility The methods which are discusses Direct Integration methods+ Methods based on Fourier Transform Monte Carlo Simulation Local and Global Optimization The models discussed for asset allocation are: Markowitz Model Black-Litterman Model Copula Models CVaR numerical optimization Source code for all the examples is provided with implementation in Matlab.
Book Details:
- Author: Joerg Kienitz
- ISBN: 9780470744895
- Year Published: 2012
- Pages: 734
- BISAC: BUS027000, BUSINESS & ECONOMICS/Finance
About the Book and Topic:
This book will enable the reader to model, design and implement a range of financial models for derivatives pricing and asset allocation. The book will provide practitioners with the complete financial modeling workflow, from model choice, deriving (semi-) analytic approximate prices and Greeks even for exotic options. Such methods can be used for calibration to market data. Furthermore, Monte Carlo simulation techniques are covered which can be applied to multi-dimensional and path dependent options or some asset allocation problems. Equity/Equity-Interest Rate Hybrid models, Interest Rate models and Asset Allocation are used as examples showing specific models with analysis of their features. The authors then go on to show how to price simple options and how to calibrate the models to real life market data and finally they discuss the pricing of exotic options. At the end of these sections the reader will be able to use the techniques discussed for equity derivatives and interest rate models in other areas of finance such as foreign exchange and inflation. The models discussed for derivatives pricing are: Heston / Bates Model Local/Stochastic Volatility Models (DD, CEV, DDHeston) Lévy Models (Variance-Gamma, Normal Inverse Gaussian) Heston — Hull — White Model Libor Market Model SABR Model Lévy Models with Stochastic Volatility The methods which are discusses Direct Integration methods+ Methods based on Fourier Transform Monte Carlo Simulation Local and Global Optimization The models discussed for asset allocation are: Markowitz Model Black-Litterman Model Copula Models CVaR numerical optimization Source code for all the examples is provided with implementation in Matlab.
A financial model is designed to represent in mathematical terms the relationships among the variables of a financial problem so that it can be used to answer “what if” questions or make projections. The central aim of financial modeling is to present the major derivatives pricing models not only from a theoretical viewpoint but how they are implemented and used. It will also ideally support probabilistic, or stochastic risk modeling. Fast semi analytic methods as well as Monte Carlo simulation have become essential tools in the pricing of derivative securities and in risk management. While a great deal of financial modeling can be carried out using numerical methods, Monte Carlo simulations can be used with greater efficiency when there are (for example), multi-dimensions to a derivatives payoff valuation. The main benefit is that all the methods we apply are implemented as ready to use scripts or functions in Matlab. That means no care has to be taken of compiler issues.
AUTHOR REPUTATION Kienitz is a well known speaker at major financial conferences (Global Derivatives/WBS) and publishes regularly on financial mathematical topics. Together with Wetterau this brings a wealth of knowledge from hands on quantitative analysis making this a really strong author team. PRACTICAL GUIDE — looks not only at the theory of financial modeling, but at actual practical concerns such as calibration and implementation. There is a dearth of practical information on this area. DOWNLOAD SECTION / NO CD-ROM — features source code for all examples featured in the book, enabling readers to apply techniques learned to other areas.
About the Author
Jörg Kientz, (Bonn, Germany) is currently Head of Quantitative Analytics in the Chief Operating Office of Deutsche Postbank AG. Before joining Deutsche Postbank AG he worked as a financial consultant for Reuters PLC, and as an IT professional for Deutsche Postbank Systems AG. He is the author of several articles on mathematical methods applied to finance and the co-author of the book “Monte Carlo Frameworks” (John Wiley and Sons). Jörg lectures at major conferences including Global Derivatives or WBS Fixed Income. He works as a consultant for Increase Upside SEC his brand for delivering software, education and consulting. Furthermore, he is a visiting lecturer to the Universities of Oxford, Bonn or Duisburg. He holds a PhD in Stochastic Analysis. Daniel Wetterau, Bonn Germany is a quantitative analyst at Deutsche Postbank AG. He joined the Quantitative Analysis group in 2008 after preparing his Masters Thesis on stochastic volatility models. Before working as a Quantitative Analyst he studied mathematics and economics at the University of Wuppertal. He trains professionals on financial mathematics and on applications of the Monte Carlo method. He received the “Barmenia Förderpreis for Mathematics” in 2007. Daniel works across all asset classes with the main focus on interest rate derivatives and quantitative asset allocation techniques.