Generative Bayesian neural network model for risk-neutral pricing of American index options

DOI10.1080/14697688.2018.1490807zbMath1420.91466OpenAlexW2901828117WikidataQ128930991 ScholiaQ128930991MaRDI QIDQ5234315

Huisu Jang, Jaewook Lee

Publication date: 26 September 2019

Published in: Quantitative Finance (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/14697688.2018.1490807

zbMATH Keywords

option pricingmachine learningAmerican index option marketfinancial option modelsgenerative Bayesian learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Stopping times; optimal stopping problems; gambling theory (60G40) Derivative securities (option pricing, hedging, etc.) (91G20)

Related Items (2)

Investment disputes and their explicit role in option market uncertainty and overall risk instability ⋮ Implied volatility smoothing at COVID-19 times

Uses Software

• ElemStatLearn
• PMTK

Cites Work

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• A Novel Pricing Method for European Options Based on Fourier-Cosine Series Expansions
• The Pricing of Options and Corporate Liabilities
• A Jump-Diffusion Model for Option Pricing
• Pricing early-exercise and discrete barrier options by Fourier-cosine series expansions
• Fast support-based clustering method for large-scale problems
• On extracting information implied in options
• American option pricing under stochastic volatility: an empirical evaluation
• An analysis of a least squares regression method for American option pricing
• Generalized autoregressive conditional heteroscedasticity
• Hyperbolic distributions in finance
• Dynamic pattern denoising method using multi-basin system with kernels
• Semiparametric modeling of implied volatility.
• A Second-Order Tridiagonal Method for American Options under Jump-Diffusion Models
• Arbitrage-free smoothing of the implied volatility surface
• Encompassing and indirect inference
• Normal Inverse Gaussian Distributions and Stochastic Volatility Modelling
• Stochastic Volatility for Lévy Processes
• The Variance Gamma Process and Option Pricing
• A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options
• Valuing American Options by Simulation: A Simple Least-Squares Approach
• Option pricing when underlying stock returns are discontinuous
• Backward stochastic differential equations and Feynman-Kac formula for Lévy processes, with applications in finance
• Forecasting S\&P 100 volatility: The incremental information content of implied volatilities and high-frequency index returns

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