Alexander Sokol, CompatibL’s Executive Chairman and Head of Quant Research, and Alexei Kondratyev, a Visiting Professor at Imperial College, will showcase their new concept that replaces model selection and calibration by machine learning at Risk Live 2021 on Friday, June 11 12:00 EDT (04:00 GMT).
Alexander Sokol explains this potential paradigm shift:
“For the past four decades, quantifying risk involved model selection and model calibration. Today, we are on the verge of a transition to a new paradigm where model selection and calibration are replaced by machine learning—a process by which a rigorous view of risk is constructed from all the available data, without predefined notions of what stochastic processes or equations should be used to describe that data.”
The speakers will discuss:
- Why risk models that learn from the data are better than risk models that average and interpolate the data, especially during a period of stress
- Examples where calibration fails but machine learning succeeds
- How machine learning can eliminate cognitive biases that influence model selection and calibration
- The Bayesian view of risk as the degree of confidence based on all available data versus the frequentist view of risk based on historical occurrences of specific adverse events
- Building a consistent view of risk across multiple equities, credit names, currencies, or commodities where some names have lots of data and others have little, instead of a binary choice between single name and proxy based calibration
While the presentation will primarily rely on examples from market and credit risk, the discussed concepts are also applicable to other areas of risk management, including operational and cyber risk.
The event will take place as a virtual event on June 7–11, 2021. The full Risk Live 2021 agenda is available here.
Click here to register for the event.
About Risk Live
Risk Live is an annual event bringing risk management professionals together. It is a pioneering festival of ideas that shows what is changing in the industry, how companies are adapting, and how you and your business can get ahead and deal with radical changes in volatile markets.
About Alexander Sokol
Alexander Sokol is the founder, Executive Chairman, and Head of Quant Research at CompatibL. He is also a co-founder of Numerix, where he served as CTO from 1996 to 2003, and a cofounder of Duality Group, where he served as CTO from 2017 to 2020.
Alexander won the Quant of the Year Award in 2018 together with Leif Andersen and Michael Pykhtin for their joint work revealing the true scale of the settlement gap risk that remains even in the presence of initial margin. Alexander’s other notable research contributions include systemic wrong-way risk (with Michael Pykhtin, Risk Magazine), joint measure models, the local price of risk (with John Hull and Alan White, Risk Magazine), and mean reversion skew (Risk Books, 2014).
Alexander earned his BA from the Moscow Institute of Physics and Technology at the age of 18, and his PhD from the L. D. Landau Institute for Theoretical Physics at the age of 22. He was awarded the USSR Academy of Sciences Medal for Best Student Research of the Year in 1988.