MetaRL pre-trained on GBWM problems delivers near-optimal dynamic strategies in 0.01s achieving 97.8% of DP optimal utility and handles larger problems where DP fails.
Emanuel Derman and Iraj Kani
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
A compact 2-qubit QNN approximates Black-Scholes-Merton option prices with usable accuracy when executed on multiple commercial NISQ quantum processors.
Empirical evidence shows that a drift term (rμτ) added to GBM implementation risk improves the fit of put-call parity carry gaps in SPX and RUT options, pointing to drift-sensitive margin burden.
Non-unique time arising from event-driven order flow points to a foundational market incompleteness beyond usual no-arbitrage assumptions.
citing papers explorer
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A Meta Reinforcement Learning Approach to Goals-Based Wealth Management
MetaRL pre-trained on GBWM problems delivers near-optimal dynamic strategies in 0.01s achieving 97.8% of DP optimal utility and handles larger problems where DP fails.
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Option Pricing on Noisy Intermediate-Scale Quantum Computers: A Quantum Neural Network Approach
A compact 2-qubit QNN approximates Black-Scholes-Merton option prices with usable accuracy when executed on multiple commercial NISQ quantum processors.
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The P behind Q: Empirical Evidence from Physical Drift in Put-Call Parity
Empirical evidence shows that a drift term (rμτ) added to GBM implementation risk improves the fit of put-call parity carry gaps in SPX and RUT options, pointing to drift-sensitive margin burden.
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Non-unique time and market incompleteness
Non-unique time arising from event-driven order flow points to a foundational market incompleteness beyond usual no-arbitrage assumptions.