Programme

Programme

Programme

09:2509:30

Chairman Opening Remarks

09:25 - 09:30

09:3010:00

Keynote address: Machine learning for asset managers

09:30 - 10:00

10:0010:40

Understanding the different approaches to Ibor & modeling

10:00 - 10:40

  • The Dynamic modeling of SOFR
  • Alternatives to implied volatility
  • Ibor fallbacks and quantitate finance
George Hong

Head of Asia-Pacific Quantitative Strategies and Global Product Head for Equities Modelling

Credit Suisse

Liang Wu

Executive Director, Head of CrossAsset Product Management

Numerix

10:4010:55

Morning networking break

10:40 - 10:55

10:5511:20

Case Study: How sentiment data can improve volatility forecasting machine learning (ML) models in times of crisis

11:15 - 23:30

Sylvain Forté

CEO

SESAMm

Sylvain is the co-founder and CEO of SESAMm, an innovative fintech company specializing in big data and artificial intelligence for investment.
Its team builds analytics and investment signals by analyzing billions of web articles and messages using natural language processing and machine learning, the core technologies behind SESAMm products TextReveal and SignalReveal.
Holding a double degree in engineering made in France and Germany, Sylvain's passion for artificial intelligence and finance led him to create SESAMm in 2014.
The company has offices in Paris, New York, Tokyo, and Tunis and works with major hedge funds, banks, and asset management clients around the world for both fundamental and quantitative use cases.

11:2011:45

Managing model risk in time of crisis

10:55 - 11:15

Financial institutions rely on a huge number of analytical models, which are typically constructed under the assumption that the future will follow the pattern history suggests. However, it may not work anymore in a crisis environment. Join our session to explore:

  • 101 ways a model can fail
  • What a financial institution can or should do in an environment lack of regulatory enforcement
  • Current market trends in relation to the governance of 'traditional & non-traditional' models
  • How senior management buy-in & technology can combine to mitigate the effects of model risk
Lu Yin

Principal Risk Advisor (Non-financial Risk)

SAS

Lu Yin’s primary focus at SAS is on the measurement and management of Non-Financial Risks, such as Model Risk, Operational Risk, and Data Risk to name a few. She has extensive experience working across the ASEAN region gained through working as a Quantitative Analyst at KPMG & Accenture. She’s also previously worked in house at Standard Chartered Bank as a Model Validator (focusing on Market & Liquidity Risks). She holds a PhD in Quantum Physics and is experienced in risk management and quantitative modeling.

11:3014:00

Networking break

11:30 - 14:00