Hedging and Pricing Structured Products Featuring Multiple Underlying Assets

TL;DR

This paper introduces a machine learning-based pricing method that accelerates the valuation of multi-asset autocallable notes and a distributional reinforcement learning hedging strategy that outperforms traditional methods in risk management.

Contribution

It proposes a novel machine learning approach for fast pricing and a distributional RL algorithm for improved hedging of structured notes with multiple underlying assets.

Findings

Pricing is 250 times faster than Monte Carlo simulation.

RL hedging achieves significantly better VaR and CVaR metrics.

The approach enhances efficiency and risk management in structured product trading.

Abstract

Hedging a portfolio containing autocallable notes presents unique challenges due to the complex risk profile of these financial instruments. In addition to hedging, pricing these notes, particularly when multiple underlying assets are involved, adds another layer of complexity. Pricing autocallable notes involves intricate considerations of various risk factors, including underlying assets, interest rates, and volatility. Traditional pricing methods, such as sample-based Monte Carlo simulations, are often time-consuming and impractical for long maturities, particularly when there are multiple underlying assets. In this paper, we explore autocallable structured notes with three underlying assets and proposes a machine learning-based pricing method that significantly improves efficiency, computing prices 250 times faster than traditional Monte Carlo simulation based method. Additionally, we introduce a Distributional Reinforcement Learning (RL) algorithm to hedge a portfolio containing an autocallable structured note. Our distributional RL based hedging strategy provides better PnL compared to traditional Delta-neutral and Delta-Gamma neutral hedging strategies. The VaR 5% (PnL value) of our RL agent based hedging is 33.95, significantly outperforming both the Delta neutral strategy, which has a VaR 5% of -0.04, and the Delta-Gamma neutral strategy, which has a VaR 5% of 13.05. It also provides the hedging action with better left tail PnL, such as 95% and 99% value-at-risk (VaR) and conditional value-at-risk (CVaR), highlighting its potential for front-office hedging and risk management.