World Business StrategiesServing the Global Financial Community since 2000

Workshop Day: Wednesday 17th May: Advanced Topics in Autoencoders and Autoencoder Market Models (AEMM).

The workshop day is complimentary to all conference attendees, numbers limited so first come first served. 

Advanced Topics in Autoencoders and Autoencoder Market Models (AEMM): 13.30 – 17.30

Session One: Machine Learning Architecture (VAE, VEGD) – 13:30 to 15:00

During this session we will train autoencoders to optimally represent the yield curve using one, two, or three model state variables, and compare our results to implicit and explicit factor representations used by popular classical models.

  • Introduction to Variational Autoencoders (VAE)
    • The roles of encoder and decoder, latent space
    • Deliberately introducing uncertainty in reconstruction
    • Loss function and optimization loop
    • Conditional VAE (CVAE) vs. unconditional VAE
    • Reconstruction with VAE
    • Generation with VAE
  • VAE for the yield curve
    • Training to single currency dataset
    • Training to multi-currency dataset
    • Few-shot learning with multi-currency dataset
  • Hands-on examples with Python:
    • Swap curve VAE trained to single currency dataset
    • Swap curve VAE trained multi-currency dataset
    • Few-shot VAE for currencies with short time series

Coffee Break – 15:00 to 15:30

Session Two: Application to Interest Rate Models – 15:30 to 17:00

For five popular model families (three in Q-measure and two in P-measure), we will review a representative classical model in each family and then build its machine learning counterpart.

  • Introduction to Autoencoder Market Models (AEMM)
    • Dimension Reduction as Compression
    • Combining VAE with stochastic process in latent space
    • Deterministic and stochastic volatility AEMM
  • Models in Q-Measure
    • One Factor Short Rate Models
      • One Factor Hull-White Model (classical)
      • One Factor Short Rate AEMM (ML)
    • Two Factor Short Rate Models
      • Two Factor Hull-White Model (classical)
      • Two Factor Short Rate AEMM (ML)
    • Forward Rate Models
      • HJM, LMM and SABR-LMM Models (classical)
      • AFNS and FHJM Models (classical)
      • Forward Rate AEMM (ML)
  • Models in P-Measure
    • Forecasting Models
      • Dynamic Nelson-Siegel Model (classical)
      • Forecasting AEMM (ML)
    • Stochastic Models
      • CKLS Model (classical)
      • Stochastic AEMM (ML)
  • Hands-on examples with Python
    • Compare curve shapes generated by classical Q-measure models and AEMM
    • Compare interest rate forecasting by classical P-measure models and AEMM

Q&A – 17:00 to 17:30

Alexander Sokol:

Executive Chairman and Head of Quant Research, CompatibL

Alexander Sokol: Executive Chairman and Head of Quant Research, CompatibL

Alexander Sokol is the founder, Executive Chairman, and Head of Quant Research at CompatibL, a trading and risk technology company. He is also the co-founder of Numerix, where he served as CTO from 1996 to 2003, and the co-founder 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, and 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 a PhD from the L. D. Landau Institute for Theoretical Physics at the age of 22. He was the winner of the USSR Academy of Sciences Medal for Best Student Research of the Year in 1988.

  • Discount Structure
  • Special Offer
    When two colleagues attend the 3rd goes free!

  • 70% Academic Discount
    (FULL-TIME Students Only)

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