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Results for “Machine Learning for Option Pricing”

30 findings

72match

Finding 71701 source

Researchers compared data-driven and physics-informed machine learning methods for option pricing, introducing a hybrid model that merges Least Squares Support Vector Regression (LS-SVR) and Feedforward Neural Networks for the Black-Scholes PDE.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

72match

Finding 71731 source

This paper applies machine learning, including LS-SVR and neural networks, to solve the Black-Scholes PDE for option pricing, introducing a hybrid model and rigorously comparing data-driven and physics-informed approaches. While not groundbreaking, its comprehensive analysis and mathematical depth offer fresh perspectives and valuable insights for quantitative finance readers.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

71match

Finding 63221 source

The innovative algorithm unifies option price estimation and hedging strategies within a single optimization framework, demonstrating flexibility across various option types and expiry dates while showing relative error performance remarkably.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

70match

Finding 61331 source

Addressing a challenging derivative modeling problem, the paper numerically solves the passport option pricing $\text{PDE}$ using a specialized neural network enhanced with polynomial activation functions (Laguerre, Hermite, Legendre) and the extreme learning machine technique. This innovative integration efficiently approximates option prices and $\text{Greeks}$, offering a compelling yet moderately original contribution.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

70match

Finding 63211 source

A new deep learning-based $$g$$-pricing method leveraging $$BSDEs$$ integrates neural networks to estimate $$g$$, achieving lower absolute pricing errors compared to the classic Black-Scholes-Merton model while addressing market complexities effectively.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

70match

Finding 61311 source

The study converts a complex passport option pricing problem into a practical numerical framework by integrating polynomial activations and advanced learning techniques, revealing unexpected convergence speed and solution precision improvements.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

70match

Finding 61301 source

Innovative research introduces a single layer polynomial neural network employing Laguerre, Hermite, and Legendre activations with an extreme learning machine algorithm to solve nonlinear backward pricing PDE and compute Greeks.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

69match

Finding 63241 source

The paper introduces a deep learning-based g-pricing mechanism integrating backward stochastic differential equations ($BSDEs$) with modern neural networks for option pricing. It innovatively refines volatility estimation and derivative modeling by merging classical frameworks like the Black-Scholes model with data-driven techniques, delivering fresh perspectives and significant advancements in financial market analysis.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

69match

Finding 63231 source

Empirical analysis with S&P 500 index option data confirms lower mean squared errors, yet highlights challenges like controlling relative errors and instability from quadratic growth in components, suggesting refinements notably.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

67match

Finding 71721 source

Despite rigorous mathematical proofs, including the positive semi-definiteness of the RBF kernel, and strong benchmark results, the study notes a need for more discussion on practical challenges and real-market data validation.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

67match

Finding 61321 source

While demonstrating robust numerical performance and accurate Greek estimation, the research acknowledges critical limitations in model scalability and broader applicability to diverse financial derivatives, urging further exploration in computational finance.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

67match

Finding 41011 source

A comprehensive survey examines fifty years evolution in financial modeling and operations research, detailing option pricing, credit and interest risk, machine learning integration, high-dimensional statistics, plus a renewed behavioral perspective.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

67match

Finding 71711 source

The hybrid approach consistently surpassed traditional models, delivering both high accuracy and computational efficiencycrucial for financewhile bridging the speed of data-driven methods with the physical consistency of physics-informed models.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

67match

Finding 52021 source

By reformulating option pricing as a primal-dual optimization problem, the LS-SVM model captures complex, nonlinear market behaviors while remaining transparent, marking a significant advance in explainable AI for financial decision-making under uncertainty.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

67match

Finding 65511 source

Researchers introduce an innovative neural network framework for pricing and hedging European options in incomplete markets, estimating option prices and hedging strategies while outperforming traditional Black-Scholes methods under stochastic volatility.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

66match

Finding 52011 source

Researchers unveil a novel, explainable machine learning modelLeast Squares Support Vector Machine (LS-SVM)that predicts option prices in volatile markets, outperforming traditional Black-Scholes, finite difference, and finite element methods in speed and stability.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

66match

Finding 56781 source

The study combines traditional analytical models with machine learning, using historical data and backtesting, while exposing high parameter sensitivity and substantial LSTM data requirements for robust, practical, comprehensive financial modeling.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

66match

Finding 33101 source

The authors link volatility surfaces, factor models and machine learning in one platform, training a multilayer network on S&P 500 option data, but market diversity tests and hyperparameter transparency lag.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

66match

Finding 41041 source

This paper presents a retrospective synthesis bridging financial modeling and operations research over $50$ years. It examines option pricing, interest rate, and credit modeling with machine learning, highdimensional statistics, and behavioral analysis. Summarizing established developments, its historical overview delivers a compelling, refreshed perspective for quantitative finance scholars, offering fresh insights.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

66match

Finding 56771 source

Surprisingly, while the extended model offered rapid computations and robust performance under varied market conditions, an LSTM machine learning model achieved lower prediction errors, greatly outperforming standard finite difference approaches.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

65match

Finding 33081 source

Introduces Deep Vol Factor Model combining deep learning with implied vol factors, reducing pricing errors and accelerating computations on S&P 500 options, outperforming derivatives pricing methods in accuracy and speed.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

65match

Finding 56761 source

Researchers extend the classic Black-Scholes model by integrating stochastic volatility and variable interest rates into a complex partial differential equation solved via finite difference methods, significantly enhancing option pricing realism.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

65match

Finding 52041 source

This paper introduces a kernel-based, explainable LS-SVM within a primal-dual framework for regime-sensitive volatility option pricing, advancing beyond traditional solvers and standard ML. Its dual-data approach and integration of explainability with LS-SVM are relatively novel, offering significant theoretical and practical impact for quantitative finance, though not entirely unprecedented.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

65match

Finding 23381 source

Takahashi and Yamada introduce a groundbreaking framework that merges deep learning with weak approximation methods, enabling the solution of complex, high-dimensional financial problems like pricing Bermudan options with multiple exercise dates.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

65match

Finding 56791 source

This paper extends the classical $$\mathrm{Black\text{-}Scholes}$$ framework by incorporating stochastic volatility and interest rate variation, and compares it with an LSTM model. It integrates numerical methods with machine learning, providing a fresh yet familiar hybrid approach. Although not revolutionary, its innovative adaptation presents significant implications for advanced financial modeling applications.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

64match

Finding 30811 source

Researchers unveil a deep learning framework that embeds terminal payoff conditions into neural networks, dramatically boosting pricing accuracy and hedging for European options, even with non-smooth or discontinuous payoffs in incomplete markets.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

64match

Finding 52031 source

Testing on both synthetic and real market data, LS-SVM surpasses popular machine learning models like ANN, XGBoost, Random Forest, and standard SVM in predictive accuracy, though questions remain about its performance in extreme scenarios and high-frequency trading.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

64match

Finding 65521 source

By incorporating an endogenous terminal condition and enforcing arbitrage constraints via a novel violation loss function, the approach handles non-differentiable payoffs, ensuring price bounds despite the challenges of market incompleteness.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

64match

Finding 30841 source

This paper presents a novel constrained deep learning framework for pricing and hedging European options in incomplete markets, jointly outputting prices and hedges while embedding terminal payoff conditions inspired by PDEs. Its explicit treatment of non-smooth payoffs and robustness to market jumps make it an original, compelling advancement in quantitative finance.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing

64match

Finding 34071 source

Researchers introduce 'deep surrogates,' neural networks that rapidly approximate complex financial models, revolutionizing option pricing analysis with unprecedented speed and scale for high-frequency, large-scale financial computations.

LexicalMeaningQualityGraph

Matched: learning, machine, option, pricing