Algorithmic Differentiation in Finance Explained

Discusses Algorithmic Differentiation specifically applied to finance

Provides guidance on theory and the practical application to financial markets

Offers working code for testing and analysis

Discusses Algorithmic Differentiation specifically applied to finance Provides guidance on theory and the practical application to financial markets Offers working code for testing and analysis

Autorentext

Marc Henrard is Head of Quantitative Research and Advisory Partner at OpenGamma, a provider of derivatives risk analytics solutions. Marc is also an Visiting Professor at University College London. He has over 15 years' experience in finance, including senior positions in risk management, trading, and quantitative analysis. Prior to joining OpenGamma, Marc was in charge of researching and implementing interest rate models as the Head of Interest Rate Modelling for the Dexia Group. Previously he held various management positions at the Bank for International Settlements as Deputy Head of Treasury Risk, Deputy Head of Interest Rate Trading and Head of Quantitative Research. Marc holds a PhD in Mathematics from the University of Louvain, Belgium. Prior to his career in finance he was a research scientist and university lecturer for 8 years.

Marc's research focuses on interest rate modelling, risk management and market infrastructure. He publishes on a regular basis in international finance journals and is a regular speaker at practitioner and academic conferences.

Inhalt

Chapter1 Introduction.- Chapter2 The Principles of Algorithmic Differentiation.- Chapter3 Applications to Finance.- Chapter4 Automated Algorithmic differentiation.- Chapter5 Derivatives to Non-inputs and Non-derivatives to Inputs.- Chapter 6 Calibration.