Newsletter of the Bachelier Finance Society

Volume 9, Number 4, September 2017 


The aim of these postings is to create a forum for the dissemination of information on academic and industrial positions related to mathematical finance, across different disciplines and different geographical regions. Please submit any job advertisements you are aware of to, preferably in plain text and sending the link to the website containing all the information. Updates and new items appear continuously at:

PhD Position
University of Oslo
Deadline: September 20, 2017
Senior Lecturer/Associate Professor Statistics
Australian National University
Deadline: September 24, 2017
Postdoc positions
Deadline: September 30, 2017
Full Professor
TU Munich
Deadline: October 1, 2017
TU Wien
Deadline: October 5, 2017
Ph.D. Position
University of St.Gallen
Deadline: October 15, 2017
Tenure-track position
HEC Montréal
Deadline: October 15, 2017

Assistant/Associate professor position
Worcester Polytechnic Institute
Deadline: October 15, 2017


The Society maintains a list of books, book reviews and journals at: Members that would like to have their books added to the website, should please let us know.

Recently published books

Emilio Barucci, Claudio Fontana
Financial Markets Theory: Equilibrium, Efficiency and Information
Springer (2017), ISBN 978-1-4471-7322-9


Stochastic Volatility Modeling

by Lorenzo Bergomi 

Stochastic Volatility Modeling

Antoine Jacquier, Imperial College London and Baruch College, CUNY

At a time when XVA Quants seem to be taking over the world of quantitative finance, more classical derivatives pricing is seeing its future called in doubt, and the question “Is Derivatives modelling dead?” is becoming a recurring topic at conferences and (mathematical finance related) social gatherings.

Lorenzo Bergomi, Quant of the Year 2009, and armed with 20 years’ worth of quantitative modelling experience at Société Générale, brings here a clear answer: “NO!”

In fact, according to him, even though derivatives pricing has grown outstandingly over the past decades, the underlying risks are still largely misunderstood, and classical “calibration” has to be fully re-thought in terms of realised P&L of actual trading strategies.

This book should be read by practitioners, as it is the only one providing a strong quantitative framework to the (Delta and Vega) hedging of Equity derivatives. It should also be read by academics who will benefit from practical insights. It should finally be read by (motivated) students, who will definitely find areas to dig deeper in, both theoretically and numerically.

This monograph on stochastic volatility modelling is not a review of the state of the art literature on the topic. It maps the route to the new generation of models and how to trade with them. One of the goals here is to show that calibration of the implied volatility surface, though important, is far from enough, and first generation stochastic volatility models (such as Heston or local volatility models) are rarely able to go beyond this step. In fact, Lorenzo Bergomi honestly attempts to push these models further, but has to unfortunately acknowledge his (and other people who tried) failing: local and stochastic volatility models, as used until recently, are too structurally constrained by the initial volatility surface they try to match, and cannot effectively grasp the dynamics of forward variances, in particular when it comes to pricing cliquet options.

This is where Bergomi’s contribution really kicks in. After a careful review of classical models, together with their hedging (in theory and in practice), he introduces and studies what I believe is his deepest contribution to the field: variance curve dynamics. Instead of suggesting dynamics for the instantaneous volatility, he proposed several years ago to model the forward variance curve directly. After motivating properly this new class of models, he shows how they naturally encompass classical stochastic volatility models (Heston in particular), and how they can be efficiently used to price options on realised variance and VIX Futures and options. Being infinite-dimensional objects, variance curves are a delicate beast to tame, especially numerically. To tackle this problem, he joined forces with then Société Générale collaborator Julien Guyon to develop an asymptotic expansion of prices assuming small volatility of volatility, and numerically validates it, thereby providing practitioners with easy-to-implement formulae. The final stretch of the book is to combine this variance curve approach with a local volatility model, in order to be able to capture both the current volatility surface, while having a framework flexible enough to capture various observed dynamics.

This book should be seen as a strong case for the need of a deeper understanding of derivatives’ modelling (and their risks). Lorenzo Bergomi provides us here with new tools (variance curve models, metrics such as the At-The-Money Forward Skew and the Skew Stickiness Ratio) as well as new results on hedging and P&L computations of actual trading strategies, which have been so far too much overlooked in mathematical finance research. Welcome to the new era of Derivatives Modelling!



This list contains conferences related to mathematical finance that take place in the next three months. A full list is available at Please let us know of conferences we are not aware of and include a URL for the event.
Boston University Conference on Financial Econometrics: Derivatives, Volatility, Jumps and Inference
September 16, 2017
Boston MA, USA
Workshop on “Dependence Modeling Tools for Risk Management”
October 2–5, 2017
Montreal, Canada
Jim Gatheral’s 60th Birthday Conference
October 13–15, 2017
New York NY, USA
Swissquote Conference 2017 on FinTech
November 3, 2017
Lausanne, Switzerland
2017 Conference on Derivatives and Volatility
November 9–10, 2017
Chicago IL, USA
QMF 2017
December 12–15, 2017
Sydney, Australia