SSRN link now expired — download parts I and II here for a limited time:
part III to follow
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.
I implemented AD in production at Danske Bank with my colleagues of the Quantitative Research department. I also teach AD at Copenhagen University in the context of my graduate course on Computational Finance and Machine Learning in Finance. My book gives an exhaustive and pedagogical account of AD, its implementation in C++ and its deployment for the risk management of financial Derivatives.
Parts I and II are necessary pre-requisites to make the most of the critical part III.