Bermudan Swaptions

Deep Hedging Bermudan Swaptions ArXiv ID: 2411.10079 “View on arXiv” Authors: Unknown Abstract Abstract This paper proposes a novel approach to Bermudan swaption hedging by applying the deep hedging framework to address limitations of traditional arbitrage-free methods. Conventional methods assume ideal conditions, such as zero transaction costs, perfect liquidity, and continuous-time hedging, which often differ from real market environments. This discrepancy can lead to residual profit and loss (P&L), resulting in two primary issues. First, residual P&L may prevent achieving the initial model price, especially with improper parameter settings, potentially causing a negative P&L trend and significant financial impacts. Second, controlling the distribution of residual P&L to mitigate downside risk is challenging, as hedged positions may become curve gamma-short, making them vulnerable to large interest rate movements. The deep hedging approach enables flexible selection of convex risk measures and hedge strategies, allowing for improved residual P&L management. This study also addresses challenges in applying the deep hedging approach to Bermudan swaptions, such as efficient arbitrage-free market scenario generation and managing early exercise conditions. Additionally, we introduce a unique “Option Spread Hedge” strategy, which allows for robust hedging and provides intuitive interpretability. Numerical analysis results demonstrate the effectiveness of our approach. ...

Deep Joint Learning valuation of Bermudan Swaptions ArXiv ID: 2404.11257 “View on arXiv” Authors: Unknown Abstract This paper addresses the problem of pricing involved financial derivatives by means of advanced of deep learning techniques. More precisely, we smartly combine several sophisticated neural network-based concepts like differential machine learning, Monte Carlo simulation-like training samples and joint learning to come up with an efficient numerical solution. The application of the latter development represents a novelty in the context of computational finance. We also propose a novel design of interdependent neural networks to price early-exercise products, in this case, Bermudan swaptions. The improvements in efficiency and accuracy provided by the here proposed approach is widely illustrated throughout a range of numerical experiments. Moreover, this novel methodology can be extended to the pricing of other financial derivatives. ...