Tighter Robust Upper Bounds for Options via No-Regret Learning
Keywords:APP: Economic/Financial, ML: Online Learning & Bandits
AbstractClassic option pricing models, such as the Black-Scholes formula, often depend on some rigid assumptions on the dynamics of the underlying asset prices. These assumptions are inevitably violated in practice and thus induce the model risk. To mitigate this, robust option pricing that only requires the no-arbitrage principle has attracted a great deal of attention among researchers. In this paper, we give new robust upper bounds for option prices based on a novel η-momentum trading strategy. Our bounds for European options are tighter for most common moneyness, volatility, and expiration date setups than those presented in the existing literature. Our bounds for average strike Asian options are the first closed-form robust upper bounds for those options. Numerical simulations demonstrate that our bounds significantly outperform the benchmarks for both European and Asian options.
How to Cite
Xue, S., Du, Y., & Xu, L. (2023). Tighter Robust Upper Bounds for Options via No-Regret Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 37(4), 5348-5356. https://doi.org/10.1609/aaai.v37i4.25666
AAAI Technical Track on Domain(s) of Application