Modern Computational Finance

Preface by Leif Andersen

It is now 2018, and the global quant community is realizing that size does matter: big data, big models, big computing grids, big computations – and a big regulatory rulebook to go along with it all. Not to speak of the big headaches that all this has induced across Wall Street.

The era of “big finance” has been creeping up on the banking industry gradually since the late 1990s, and got a boost when the Financial Crisis of 2007–2009 exposed a variety of deep complexities in the workings of financial markets, especially in periods of stress. Not only did this lead to more complicated models and richer market data with an explosion of basis adjustments, it also emphasized the need for more sophisticated governance as well as quoting and risk managements practices. One poster child for these developments is the new market practice of incorporating portfolio-level funding, margin, liquidity, capital, and credit effects (collectively known as “XVAs”) into the prices of even the simplest of options, turning the previously trivial exercise of pricing, say, a plain-vanilla swap into a cross-asset high-dimensional modeling problem that requires PhD-level expertise in computing and model building. Regulators have contributed to the trend as well, with credit capital calculation requirements under Basel 3 rules that are at the same level of complexity as XVA calculations, and with the transparency requirements of MiFID II requiring banks to collect and disclose ...