Quant Insights
Volatility modeling in financial markets
London & Online, 10th November 2017
3rd Annual Conference
Brought to you by
CQF Institute and Wilmott

QI Conference

Fitch Ratings Auditorium, 30 North Colonnade, Canary Wharf, London, E14 5GN

The CQF Institute and Wilmott present their third annual Quant Insights conference this November. Held in the heart of Canary Wharf, London’s modern financial center, the conference will bring together leading practitioners to explore volatility modeling, looking at the latest strategies and new technologies used to model volatility.

Book Ticket

2 Organizers

Two internationally recognized brands; CQF Institute & Wilmott partner for the most topical quant conference of the year.

10 Talks

Quant Insights will have talks from experts in their field and a panel session discussing volatility in quant finance.

120 Live Tickets

The conference offers an opportunity to network with like-minded professionals in the quant community.

100+ Online Tickets

Delegates can follow and participate in the conference proceedings from anywhere in the world with online tickets.

Speakers and Talks

Paul Wilmott
Keynote: Some Things I Have Learned About Volatility Over the Years | Abstract
Keynote: Some Things I Have Learned About Volatility Over the Years

  • Why does volatility matter?
  • How can you make money from volatility?
  • Is a stable calibration needed to produce stable hedge ratios?
  • What would happen if you hedge with the wrong volatility?
  • What would a good volatility model look like?
  • Dr. Paul Wilmott
    President, CQF Institute

    Paul is the founder of the Certificate in Quantitative Finance and Wilmott.com and he is internationally renowned as a leading expert on quantitative finance. His research work is extensive, with more than 100 articles in leading mathematical and finance journals, as well as several internationally acclaimed books on mathematical modeling and derivatives, including the best-selling Paul Wilmott On Quantitative Finance, published by John Wiley & Sons.

    Laura Ballotta
    Smiles & Smirks: A Tale of Factors | Abstract
    Smiles & Smirks: A Tale of Factors

    We offer a general framework based on time changed Lévy process for modeling the stochastic evolution of stock prices, which includes risk factors of both diffusive and jump nature, and leverage effects. The proposed setting encompasses a large number of the most commonly used stochastic volatility models, allows for the construction of new potential alternative models, and enables a comparative study of their features in terms of volatility, volatility of volatility and correlation processes. We analyse the performance of these models in terms of calibration and fit of the volatility

    Dr. Laura Ballotta
    Senior Lecturer in Financial Mathematics, Cass Business School, City, University of London

    Laura holds a PhD in Mathematical and Computational Methods for Economics and Finance from the Università degli Studi di Bergamo (Italy). Her research interests focus on development of realistic models for asset prices, which also recognize the interdependence in place between them, and their applications to practical problems such as counterparty risk valuation.

    Philippe Henrotte
    Volatility Calibration and Hedging | Abstract
    Volatility Calibration and Hedging
  • Is there a link between calibration quality and back-tested hedging efficiency?
  • Do simpler models lead to better hedging?
  • Is a stable calibration needed to produce stable hedge ratios?
  • How to stabilize the calibrated parameters of a rich model?
  • The hedging role of variance swaps and VIX derivatives.
  • Dr. Philippe Henrotte
    Co-Founder and Partner, ITO33

    Philippe is one of the founding partners of ITO33, which designs sophisticated derivatives pricing software for financial institutions. He is an Affiliate Professor at the Finance Department of HEC Paris. He holds a PhD in Finance from Stanford University. His research interests focus on risk management and the hedging and pricing of derivatives in incomplete markets. He is a director of Equinox Russian Opportunities Fund Limited, a hedge fund targeting the Russian capital markets.

    Peter Tankov
    Pricing and Hedging with Lognormal Rough Volatility | Abstract
    Pricing and Hedging with Lognormal Rough Volatility
    We study stochastic volatility models where the log-volatility follows a Gaussian Volterra process. This includes in particular some of the recently introduced “rough stochastic volatility'' models. We derive explicit hedging strategies and fast Monte Carlo algorithms for computing option prices and hedge ratios in such models.
    Dr. Peter Tankov
    Professor of Quantitative Finance, ENSAE ParisTech

    Peter is a leading expert on jump risk in financial markets, author of the widely-read book Financial Modelling with Jump Processes, and of many publications on subjects ranging from risk management, option pricing and model calibration to stochastic control and commodity prices modeling. His interests include stochastic models and energy finance.

    Avik Sengupta
    Automatic Differentiation - Calibrating Volatility Models and Calculating Greeks Accurately and Efficiently in Julia | Abstract
    Automatic Differentiation - Calibrating Volatility Models and Calculating Greeks Accurately and Efficiently in Julia

    The calibration of volatility models, in common with many other operations in computational finance such as the computation of Greeks, depend on the efficient and accurate estimation of large number of gradients. Automatic differentiation (AD) is a powerful method for computing gradients and higher-order derivatives of numerical programs, which are both numerically exact, yet incur very little computational overhead. AD has already been explored previously in the quantitative finance literature, but its adoption has been hampered by the difficulty of its use.

    In this talk, we will see how Julia's design make it feasible to use AD with minimal effort on the programmers part, while also being much faster and more accurate than finite differencing methods. As a demonstration of how AD exemplifies Julia's promise of productivity and performance in large and complex codebases, we will demonstrate how it can be used for both computing Greeks, as well as efficiently fitting stochastic volatility models such as SABR or Heston models.

    Avik Sengupta
    Vice President of Engineering, Julia Computing

    Avik has worked on risk and trading systems in investment banking of many years, mostly using Java interspersed with snippets of the exotic R and K languages. This left him wondering whether there were better things out there. Avik’s quest concluded with the appearance of Julia in 2012. He has been coding in Julia and contributing to it ever since. He is a core contributor to the language, maintains many of its packages and is the author of a book about high performance Julia.

    David Ardia
    Forecasting Performance of Markov-Switching GARCH Models: A Large-Scale Empirical Study | Abstract
    Forecasting Performance of Markov-Switching GARCH Models: A Large-Scale Empirical Study

    Among the various building-blocks of any risk management system, forecasting volatility is key, especially for short-term horizons. Research on modeling volatility using time series models has been active since the creation of the GARCH model.

    Recent academic studies show that structural breaks in the volatility dynamics can strongly affect estimates of GARCH-type models, which translates into poor risk forecasts. A way to address the switch of model's behavior is provided by Markov-switching GARCH models (MSGARCH) whose parameters can change over time according to a discrete latent variable.

    Through a large-scale forecasting performance analysis, we investigate if MSGARCH have any practical value for risk managers compared with single-regime models. We find that MSGARCH models report better Value-at-Risk and left-tail distribution forecasts than their single-regime counterpart. Also, our results indicate that accounting for parameter uncertainty helps for left-tail predictions, independently of the inclusion of the Markov-switching mechanism.

    Dr. David Ardia (Presented by UnRisk)
    Assistant Professor of Finance and Head of the Master of Science in Finance, University of Neuchâtel

    David is Assistant Professor of Finance and Head of the Master of Science in Finance at the University of Neuchâtel, Switzerland, and Visiting Professor of Finance at Laval University, Québec City, Canada. He spent several years in the financial industry. He was senior analyst at aeris CAPITAL AG and head of research at Tolomeo Capital AG. In 2008, he received the Chorafas prize for his book Financial Risk Management with Bayesian Estimation of GARCH Models published by Springer. He is the author of several scientific articles and statistical packages. He holds an MSc in Applied Mathematics, a MAS in Quantitative Finance and a PhD in Financial (Bayesian) Econometrics.

    Stefan Fink
    The Different Truths of IR Volatility Modeling: About Normality and Black’s Immortality | Abstract
    The Different Truths of IR Volatility Modeling: About Normality and Black’s Immortality

    Supplying the "right" prices for Interest Rate Derivatives once was an issue for a compact, single chapter in a Derivatives textbook. Non- negativity of Rates as the condition sine qua non, leading to a world of (at least Plain Vanilla) Derivatives in a Black Lognormal universe.

    Things changed, and did so in a massive way. Although xVA models and advanced Funding rationales fill conference agendas, the basic question of Volatility and Model choice is far from answered in the world of small to mid-size banks and financial institutions.

    Struggling between theoretical appropriateness and technical feasibility, we are far from standardized model approaches. Although we observe a "trend to normality", some still wait for the return of the "old world", trying to avoid implementation cost.

    In some cases - even within a specific bank - different departments follow different approaches. Front Office prices might not match those of risk controlling, and risk prices need not necessarily enter client's valuations.

    This mismatch causes real-life problems such as inconsistencies or data issues. Pricing, Client Valuation and Risk are - beside others - hit. The talk gives an overview of the current "SME Rates Vol Model status quo", its current challenges and their implications, and shows some critical issue arising from living in both worlds.

    Dr. Stefan Fink (Presented by UnRisk)
    Senior Manager, Advisory, KPMG in Austria

    Stefan Fink has a PhD in economics and has been working for financial institutions throughout his career. Currently he is Senior Manager Advisory - Financial Risk Management at KPMG Austria. He is also a reknowned lecturer, giving lectures in Austria.

    Iain Clark
    Using FX Volatility Skew to Assess the Implied Probability of Hard Brexit | Abstract
    Using FX Volatility Skew to Assess the Implied Probability of Hard Brexit

    Much of the debate around the British exit (Brexit) from the European Union has centred on the potential macroeconomic impact. In this talk we instead focus on understanding market expectations for price action around the Brexit referendum date and for 30 March 2019, when the two year time window that started with the Article 50 notification on 29 March 2017 will terminate. For the 2016 referendum, we construct a mixture model corresponding to two scenarios for the GBPUSD exchange rate after the referendum vote, one scenario for “remain” and one for “leave”.

    Calibrating this model to four months of market data, from 24 February to 22 June 2016, we find that a “leave” vote was associated with a predicted devaluation of the British pound to approximately 1.37 USD per GBP, a 4.5% devaluation, and quite consistent with the observed post-referendum exchange rate move down from 1.4877 to 1.3622. We argue that we can apply the same bimodal mixture model technique to construct two states of the world corresponding to soft Brexit (continued access to the single market) and hard Brexit (failure of negotiations in this regard).

    While it is too early to assess the probability of hard Brexit with certainty, we apply a similar method to investigate GBPUSD volatility skew information for the next 18 months and describe how predictive event risk signals can be built to guide trading and risk management strategies as March 2019 approaches.

    Dr. Iain J. Clark
    Founder/Managing Director, Efficient Frontier Consulting

    Iain Clark is the founder of Efficient Frontier Consulting Ltd, a quantitative analytics and risk consultancy. He was former Head of FX and Commodities Quantitative Analysis at Standard Bank and Head of FX Quantitative Analysis at UniCredit and Dresdner Kleinwort. He holds a PhD in applied mathematics and an MSc in financial mathematics. His employers and clients include Lehman Brothers, BNP Paribas, JP Morgan, CME Group, Byhiras, Zurcher Kantonalbank, Scotiabank and Commerzbank. Iain is the author of two Wiley Finance books - Foreign Exchange Option Pricing: A Practitioner's Guide (Wiley, 2011) and Commodity Option Pricing: A Practitioner's Guide (Wiley, 2014).


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    Conference Organizers

    Quant Insights is presented by the CQF Institue & Wilmott
    CQF Instiute

    Part of Fitch Learning, the CQF Institute is a global membership platform for educating and building the quant finance community.

    Fitch Learning

    Part of the Fitch Group, Fitch Learning partners with clients to enhance knowledge, skills and conduct. Fitch Learning advises and builds learning solutions to accelerate the achievements of individuals and companies.


    Wilmott is the leading resource for the quant finance community, comprised a website and discussion forum and Wilmott magazine.

    Conference Sponsors

    Platinum Sponsors
    Gold Sponsor
    Julia Computing
    Affiliate Sponsor



    Fitch Ratings Auditorium,
    30 North Colonnade,
    Canary Wharf,
    E14 5GN