Modern Computational Finance - Antoine Savine

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Computational Finance

       Scripting for Derivatives and xVA

      ANTOINE SAVINE AND JESPER ANDREASEN

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      Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

      Published simultaneously in Canada.

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      Library of Congress Cataloging-in-Publication Data

      Names: Antoine Savine, author. | Jesper Andreasen, 1970– author.

      Title: Modern computational finance : scripting for derivatives and XVA / Antoine Savine and Jesper Andreasen.

      Description: Hoboken, New Jersey : John Wiley & Sons, Inc., [2022] | Includes bibliographical references.

      Identifiers: LCCN 2021036994 (print) | LCCN 2021036995 (ebook) | ISBN 9781119540786 (hardback) | ISBN 9781119540816 (adobe pdf) | ISBN 9781119540793 (epub)

      Subjects: LCSH: Finance—Mathematical models. | Finance—Computer simulation. | Finance—Data processing.

      Classification: LCC HG106 .A556 2022 (print) | LCC HG106 (ebook) | DDC 332.01/5195—dc23

      LC record available at https://lccn.loc.gov/2021036994

      LC ebook record available at https://lccn.loc.gov/2021036995

      Cover Design: Wiley

      Cover Image: © kentarcajuan/Getty Images

      Antoine came to General Re Financial Products in London in 1998 with a lot of youthful spirit and many refreshing ideas. One of them was a financial payoff language called SynTech (Syntactic interpreter Technology). I am not sure that I was immediately convinced but when he connected it to a real model and priced some structures that we made up on the fly, I was hooked. I learned SynTech in hours but it took me months to figure out how it was put together, a process that forced me to learn structured programming in general and C++ in particular.

      As always, it was a general struggle to keep up with the financial innovation, and constant re‐coding of new payoffs was a painful and error prone process. We had been toying with a cocktail of Visual Basic for scripting of the payoffs and scenarios of future prices generated by C programs. However, the implementation was slow, model specific, hard to use, and generally more elephant than elegant.

      SynTech, however, was easy to use with a readable simple Basic‐like syntax and thoroughly built for speed and versatility. Scripts were pre‐processed to cache and index static information for maximum pricing speed, and its API allowed a seamless hook‐up with any dynamic model in our library. In fact, it took Antoine very little time to hook up SynTech to our then newly developed Libor Market Model.

      Further, it was clear that SynTech could be extended to perform various life cycle tasks such as fixings and payments to limit the manual burden on the back office in the handling of exotic trades. SynTech implemented a clear separation between instrument and model.

      From then on, I have only used scripting languages as the interfaces for all the models I have developed. And when I need to price something I rarely use anything else than naked scripting. The scripting languages that I have used and developed have all had a syntax very close to the original SynTech. The developments that I have done have mainly been underneath. Remarkably, SynTech's original 50 keywords still cover the ground.

      Paribas's GRFN developed by Guillaume Amblard and Societe Generale's OptIt by Jean‐Marc Eber were as far as we know the first scripting languages that were used on an industrial scale. Jean‐Marc Eber went on to set up the company Lexifi, which to this date supplies the industry with scripting technology.

      GRFN and OptIt emerged in the mid‐1990s. There were, for sure, other earlier attempts to develop scripting languages, most notably various uses of the LEX‐YACC suite of tools for creating languages. Emmanuel Derman mentions such efforts in passing in My Life as a Quant and I have heard of similar experiments at JP Morgan. However, to my knowledge, none of these made it to large‐scale production.

      In 2001, I went to Bank of America in London, where I ganged up with James Pfeffer and developed Thor. The main innovation of Thor was the use of the visitor pattern technology that later was instrumental in using scripting as the backbone for xVA and regulatory calculations. The main idea of the visitor pattern is to have visitors that visit the cash flows for different purposes: pre‐processing, valuation, dependency graph generation, compression, decoration, and so on. The diverse use of visitors shows that development of your own scripting language is necessary. Python or C# can not be visited.

      In 2002–2005 I worked for Nordea in Copenhagen where we developed a scripting language called Loke. The main innovation during this period was the integration of American Monte‐Carlo techniques with Loke, including upper‐bound techniques implemented by Ove Scavenius.

      In 2005–2008 I was back with Bank of America, where I found the Thor language to be heavily used by the structured interest rate and equity desks. Often they used a macro language called Sif, developed by Mohan Namasivayam, to generate Thor scripts.

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