Antoine Savine is a French mathematician, academic and a leading derivatives research professional with Danske Bank in Copenhagen. Antoine also teaches Volatility and Computational Finance at Copenhagen University. He is the author of Modern Computational Finance with John Wiley and Sons.
Antoine holds a PhD in Mathematics, and is best known for his work on volatility and interest rate models. He was influential in the development of cashflow scripting, the application of generalized derivatives to volatility, and the wide adoption of AAD in financial systems.
The first two parts (out of three) of my book Modern Computational Finance (Wiley, 2018) are complimentarily available on SSRN only in December 2020.
Part I teaches necessary C++ foundations with a focus on parallel computing. Part II summarizes the theory of financial Derivatives and develops serial and parallel Monte-Carlo pricing and risk management engines.
Part III, not included in this preview, discusses and develops the critical adjoint differentiation (AD) technology, its professional implementation in C++ and its deployment in risk management platforms.
In recent years, AD revolutionized the field of quantitative finance. It brought us real-time risk reports and instantaneous calibration. It also enabled extremely promising new directions of research. For instance, the Risk article Differential Machine Learning by Brian Huge and I (also available on arXiv and SSRN) leverages pathwise gradients computed with AD to train a novel breed of deep learning models to effectively approximate pricing and risk functions of arbitrary financial products.
We count on the suggestions and comments left by advanced readers in the linkedIn thread to quickly complete this work with the quality expected from the Modern Computational Finance series.
The draft, co-authored by Jesper Andreasen and Antoine Savine, covers cash-flow scripting, a critical technology in modern Derivatives pricing and risk management, not covered in any alternative literature. Mathematically, scripting turns arbitrary descriptions of Derivatives cash-flows into a functional of the path of the market state so the prices and risks of arbitrary transactions may be computed in arbitrary stochastic models. Computationally, scripting produces a computation graph for the cash-flows of arbitrary transactions, which is then optimized and executed for the computation of prices and risks, in a similar manner to machine learning systems like TensorFlow.
Cash-flow scripting has been around for twenty-five years. It was invented in Societe Generale and Banque Paribas for the purpose of structuring exotic transactions and computing prices and risks in real-time. Since then, scripting has considerably evolved into a central piece in all Derivatives pricing, risk management and regulatory calculation platforms.
Jesper Andreasen and I have been working with cash-flow scripting since the mid 1990s, bringing this technology to production at BNP-Paribas, General Re Financial Products, BAML, Nordea, Danske Bank and Saxobank, among other places. We put in this book the sum of our experience, along with our views for the future of this technology. The book comes with an implementation in C++ available on GitHub. Scripting has strong ties to modern technologies like smart contracts or computation graphs for machine learning, although this is a chapter yet to be written.
We have just presented our latest Risk paper Differential Machine Learning at QuantMinds International 2020. The presentation includes a live demo of how differential ML is implemented in production, and combined with cash-flow scripting to provide truly general means of learning the pricing and risk function of arbitrary financial instruments. See the 5min demonstration below: