Erik Schlogl

Quantitative Finance (eBook, ePUB)

An Object-Oriented Approach in C++

• Format: ePub

Produktbeschreibung

This text provides readers with a foundation in the key methods and models of quantitative finance. Through an approach based on C++ classes and templates, the text highlights the basic principles common to various methods and models while the algorithmic implementation guides readers to a more thorough, hands-on understanding. It also presents recipes and extensible code building blocks for some of the most widespread methods in risk management and option pricing. C++ code and other materials are available on the author's website.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 354
• Erscheinungstermin: 3. September 2018
• Englisch
• ISBN-13: 9781315359854
• Artikelnr.: 54789092

Autorenporträt

Erik Schlögl currently is Professor and Director of the Quantitative Finance Research Centre at the University of Technology, Sydney (UTS), Australia. Erik received his doctorate in Economics from the University of Bonn, Germany, for work on term structure models and the pricing of fixed income derivatives and has gained broad-based experience in computational financial engineering. He has consulted for financial institutions and software developers in Europe, Australia and in the US. His research interests cover a broad area of quantitative finance, in particular model calibration, interest rate term structure modelling, credit risk and the integration of multiple sources of risk. He has published articles in a number of international journals, including Finance & Stochastics, Quantitative Finance, Risk and the Journal of Economic Dynamics and Control. In addition to UTS, he held positions at the University of New South Wales, Australia, and the University of Bonn, Germany.

Inhaltsangabe

A Brief Review of the C++ Programming Language. Basic Building Blocks. Lattice Models for Option Pricing. The Black
Scholes World. Finite Difference Methods. Implied Volatility and Volatility Smiles. Monte Carlo Simulation. The Heath
Jarrow
Morton Model. Appendices. References. Index.