Dietmar Maringer – författare

This book describes computational finance tools. It covers fundamental numerical analysis and computational techniques, such as option pricing, and gives special attention to simulation and optimization. Many chapters are organized as case studies around portfolio insurance and risk estimation problems. In particular, several chapters explain optimization heuristics and how to use them for portfolio selection and in calibration of estimation and option pricing models. Such practical examples allow readers to learn the steps for solving specific problems and apply these steps to others. At the same time, the applications are relevant enough to make the book a useful reference. Matlab and R sample code is provided in the text and can be downloaded from the book's website.

Computationally-intensive tools play an increasingly important role in financial decisions. Many financial problems-ranging from asset allocation to risk management and from option pricing to model calibration-can be efficiently handled using modern computational techniques. Numerical Methods and Optimization in Finance presents such computational techniques, with an emphasis on simulation and optimization, particularly so-called heuristics. This book treats quantitative analysis as an essentially computational discipline in which applications are put into software form and tested empirically.

This revised edition includes two new chapters, a self-contained tutorial on implementing and using heuristics, and an explanation of software used for testing portfolio-selection models. Postgraduate students, researchers in programs on quantitative and computational finance, and practitioners in banks and other financial companies can benefit from this second edition of Numerical Methods and Optimization in Finance.

This book follows on from Natural Computing in Computational Finance  Volumes I, II and III.   As in the previous volumes of this series, the  book consists of a series of  chapters each of 

which was selected following a rigorous, peer-reviewed, selection process.  The chapters illustrate the application of a range of cutting-edge natural  computing and agent-based methodologies in computational finance and economics. 

The applications explored include  option model calibration, financial trend reversal detection, enhanced indexation, algorithmic trading,  corporate payout determination and agent-based modeling of liquidity costs, and trade strategy adaptation.  While describing cutting edge applications, the chapters are 

written so that they are accessible to a wide audience. Hence, they should be of interest  to academics, students and practitioners in the fields of computational finance and  economics.  

which was selected following a rigorous, peer-reviewed, selection process.  The chapters illustrate the application of a range of cutting-edge natural  computing and agent-based methodologies in computational finance and economics. 

The applications explored include  option model calibration, financial trend reversal detection, enhanced indexation, algorithmic trading, corporate payout determination and agent-based modeling of liquidity costs, and trade strategy adaptation.  While describing cutting edge applications, the chapters are 

written so that they are accessible to a wide audience. Hence, they should be of interest  to academics, students and practitioners in the fields of computational finance and  economics.  

The applications explored include  option model calibration, financial trend reversal detection, enhanced indexation, algorithmic trading,  corporate payout determination and agent-based modeling of liquidity costs, and trade strategy adaptation.  While describing cutting edge applications, the chapters are 

written so that they are accessible to a wide audience. Hence, they should be of interest  to academics, students and practitioners in the fields of computational finance and  economics.  

written so that they are accessible to a wide audience. Hence, they should be of interest  to academics, students and practitioners in the fields of computational finance and  economics.