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Optimizing Neural Networks for Bermudan Option Pricing: Convergence Acceleration, Future Exposure Evaluation and Interpolation in Counterparty Credit Risk

Shashi Jain / Vikranth Lokeshwar Dhandapani

machine learning / monte carlo simulationResource overview

Optimizing Neural Networks for Bermudan Option Pricing: Convergence Acceleration, Future Exposure Evaluation and Interpolation in Counterparty Credit Risk

Paper2024

by Shashi Jain, Vikranth Lokeshwar Dhandapani

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Suggested foundations for credit and counterparty risk and options and derivatives pricing. Specialist prerequisites vary by paper.

Before you read

Credit Instruments, PD/LGD/EAD, and Rating Migration
Separate default probability, loss severity and exposure.
Conditional Expectation
Read conditional default and exposure quantities.
The Binomial Model
Build replication and risk-neutral probabilities in a tractable model.
Market Model and Risk-Neutral Valuation
Distinguish pricing probabilities from physical forecasts.

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Threshold Models and the Gaussian Copula for Portfolio Default
Study dependence between defaults.
CVA and DVA
Connect exposure and counterparty losses to valuation adjustments.
The Black-Scholes PDE, Formula and Implied Volatility
Connect the pricing equation, closed-form value and implied volatility.

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From martingales to option pricing
Start in discrete time, move to Brownian motion, then read the original pricing argument and simulate it.

Optimizing Neural Networks for Bermudan Option Pricing: Convergence Acceleration, Future Exposure Evaluation and Interpolation in Counterparty Credit Risk

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