Study on pricing options tied to stock tick complexity.
problem Pricing options based on stock tick complexity.
method Numerical and theoretical analysis of European and American options.
result Numerical and theoretical pricing results for different complexity types.
Simplifies American option pricing with reduced complexity.
problem Complexity reduction in American option pricing.
method Regression-based dual approach for nested Monte Carlo methods.
result Reduces complexity of nested Monte Carlo methods.
Quantum Monte Carlo speeds up option pricing for complex payoff functions.
problem Efficiently pricing options with complex payoff functions using quantum computing.
method Developed a quantum Monte Carlo algorithm for multidimensional Black-Scholes PDEs.
result Proved polynomial computational complexity and speed-up over classical methods.
Enhances option pricing with fractional order Black-Scholes-Merton model.
problem Improving precision and authenticity of option pricing.
method Integrates fractional order Black-Scholes-Merton with neural networks.
result Improves accuracy in capturing complex diffusion dynamics and memory effects.
Quantum computing improves Monte Carlo option pricing for complex derivatives.
problem Complex financial derivatives require extensive computations in high-dimensional spaces.
method Developed a quantum algorithm for simulating many potential asset paths in parallel.
result Quantum algorithm provides highly accurate option pricing and risk analysis.
This paper provides fast estimates for complex option types.
problem Estimating prices for constrained multiple exercise American options.
method Lookahead search for lower estimates and nearest-neighbor martingale for upper estimates.
result Probabilistic convergence guarantees for the algorithms.
The paper offers methods to price complex options using upper and lower bounds.
problem Pricing complex options like Asian and basket options.
method Develops a general framework using lower and upper bounds.
result Lower bounds simplify the problem and provide reasonable approximations.
In this article we propose a novel approach to reduce the computational complexity of various approximation methods for pricing discrete time American options. Given a sequence of continuation values estimates corresponding to different levels of spatial approximation and time discretization, we propose a multi-level l…
New estimator for digital options using path splitting and MLMC.
problem Estimating digital options with stochastic differential equations.
method Repeated path splitting, Multilevel Monte Carlo (MLMC).
result Estimator complexity similar to MLMC for Lipschitz payoffs.
Mathematical tools solve complex option pricing problems.
problem Complex option pricing models in finance.
method Distributional Mellin transform and inversion of multiple Mellin-Barnes integrals.
result Solves various option pricing models including American options.
Complex volatility leads to chaotic fractals in option pricing.
problem Exploring the implications of complex volatility in Black-Scholes model.
method Analyzing the function for pricing European options with complex volatility and solving for implied volatility.
result Chaotic fractals emerge in the calculation of complex implied volatility.
Improved option pricing for assets with jumps and spikes.
problem Inaccurate pricing of European and American options using lognormal diffusion.
method Developed a jump-diffusion model and reduced complexity of pricing algorithms.
result Reduced complexity of European option pricing from O(n^3) to O(n ln n).
Machine learning models outperform traditional option pricing models.
problem Improving option pricing accuracy using complex models.
method Evaluation of machine learning (NN, RF, CatBoost) and traditional models (Black-Scholes, Heston) on synthetic and real data.
result Machine learning models outperform traditional models in predicting option prices.
Signature payoffs price complex derivatives accurately.
problem Pricing complex derivatives like options.
method Signature of price path for continuous payoffs.
result Signature payoffs can price various derivatives accurately.
Second-order optimization speeds up deep hedging for complex options.
problem Hedging exotic options with market frictions in realistic markets.
method Second-order optimization scheme leveraging pathwise differentiability and Kronecker-factoring.
result Our method optimizes the policy in 1/4 the steps of standard optimization.
Paper offers a simpler solution for managing complex financial options.
problem Managing a large number of financial assets with diverse dynamics.
method Developed a simple analytical approximation for market making.
result Shows significant flexibility over existing market making strategies.
A fast method for pricing various financial options.
problem Efficient pricing of discretely monitored early-exercise options.
method A quadrature technique-based method using elementary calculations and a fixed grid.
result Convergence rate of O(1/N4) and complexity of O(MNlogN). Enhanced SFP-FCC method for early-exercise options pricing and hedging.
problem Pricing and hedging early-exercise options under Lévy processes.
method Combines SFP method with Filon-Clenshaw-Curtis (FCC) rules.
result Retains global spectral convergence rate and fast error convergence.
MNN improves American call option pricing accuracy.
problem Inaccurate valuation of American call options.
method Modular Neural Network (MNN) model.
result MNN model outperforms traditional models and FNN.
Quantum algorithm speeds up pricing of financial derivatives.
problem Pricing autocallable options efficiently.
method Integration-based exponential amplitude loading technique.
result 50x reduction in circuit depth for payoff component.
New efficient method for inverse Z-transform reduces complexity significantly.
problem Efficient numerical realization of inverse Z-transform for large n.
method Derives sufficient conditions for new scheme, applies to option pricing.
result Significant reduction in complexity for large n, especially for European options.
Closed-form formulas for path-independent options in a specific Lévy model.
problem Valuation of path-independent options in the exponential NIG model.
method Closed-form pricing formulas derived using a factorized representation in Mellin space and complex analysis.
result Valid closed-form formulas with quickly convergent series for various options.
Tensor trains speed up option pricing for multi-asset options.
problem Speeding up option pricing for multi-asset options.
method Tensor train learning algorithms to compress functions with parameter dependence.
result The proposed method outperforms Monte Carlo-based pricing in computational complexity.
Study evaluates cryptocurrency option pricing models, finds Kou and Bates models perform best.
problem High volatility and low liquidity in cryptocurrency futures contracts make traditional option pricing models unreliable.
method Calibrated and evaluated the performance of six option pricing models (Black-Scholes, Merton Jump Diffusion, Variance Gamma, Kou, Heston, and Bates) on BTC and ETH futures options.
result Kou and Bates models achieve the lowest pricing errors, with Kou outperforming Bates for BTC and ETH options respectively.
Two neural network methods solve American-style option pricing and hedging.
problem Solving American-style option pricing and hedging problems efficiently.
method Two novel neural network methods: one series of networks and one global network.
result Simultaneous computation of upper and lower bounds with reduced complexity.
Framework selects real estate redevelopment uses by integrating value, risk, complexity, and irreversibility.
problem Persistent underperformance of real estate assets due to structural misalignment.
method Integrates real-options logic and multi-criteria decision analysis.
result Reduces over-complexification and misalignment in strategic use selection.
The paper analyzes American options with time-varying caps, finding complex exercise regions and deriving option pricing formulas.
problem Valuation of American capped call options with time-varying caps, especially when the cap grows or decreases over time.
method Probabilistic arguments and local time, characterizing exercise boundaries through recursive integral equations and piecewise constant segments.
result General representation formulas for option prices, derived from exercise boundaries and local time of the underlying process.
This paper compares machine learning models for pricing European options.
problem Pricing European options using traditional methods like Black Scholes Model.
method Google AutoML Regressor, TensorFlow Neural Networks, and XGBoost Gradient Boosting Decision Trees.
result All models outperformed the Black Scholes Model in terms of mean absolute error.
Paper develops a new method for pricing complex financial options.
problem Pricing European-style double barrier knock-out options for homogeneous diffusions.
method Neumann series of Bessel functions representation for one-dimensional time-homogeneous diffusions.
result Efficient and simple numerical method for pricing and hedging.
Enhances DQN for subtasks with mixed transfer effects.
problem Improving sample efficiency in reinforcement learning with mixed transfer effects.
method Combining options framework with deep Q-networks (DQNs) through different 'option heads' and a supervisory network.
result Augmented DQN shows lower sample complexity in subtasks with negative transfer.
Model accurately calibrates FX market skew for exotic options.
problem Inconsistent prices from different models for FX derivatives.
method Fully parameterized local volatility model with numerical methods.
result Model provides reliable prices for daily trading.
New method calculates credit exposures for complex options efficiently.
problem Efficient calculation of credit exposures for complex options.
method Dynamic Chebyshev method for closed-form approximation.
result Highly efficient evaluation of credit exposures for large paths.
Efficiently price high-dimensional Bermudan options using tensor compression.
problem High-dimensional option pricing with computational complexity.
method Hierarchical tensor compression for Monte Carlo and dual martingale methods.
result Tensor compression alleviates the curse of dimensionality for Bermudan option pricing.
DeltaHedge uses AI to optimize portfolio options trading.
problem Balancing risk and return in volatile markets.
method Multi-agent framework integrating reinforcement learning and options hedging.
result Outperforms traditional and standalone models.
Quantum computing speeds up option pricing for multiple assets.
problem High-dimensional integration bottleneck in option pricing.
method Calibrated marginal distributions, Gaussian copula, QAMC with QAE.
result QAMC reduces integration queries by 10-100 times for similar precision.
KANOP uses KANs to efficiently price American options.
problem Efficiently pricing American options with limited data.
method Combines KANs with LSMC to estimate continuation value.
result KANOP provides more accurate option value estimates.
Enhanced options trading strategies using advanced portfolio optimization.
problem Generating consistent positive returns in high-frequency options trading.
method Advanced portfolio optimization techniques applied to SPY options data.
result Sophisticated strategies incorporating advanced Greeks show potential in high-frequency trading.
Paper improves American option valuation in complex models.
problem Valuation of American options in time-dependent jump-diffusion models.
method Integral equations and characteristic functions for explicit exercise boundary determination.
result Efficient and accurate pricing method for American options in various models.
Tensor networks improve exotic option pricing efficiency.
problem Challenges in pricing exotic financial derivatives using standard methods.
method Combining binomial pricing with tensor network techniques (Matrix Product States).
result Linear scaling with parameters and reduced computational complexity.
ANN improves option pricing models by calibrating parameters faster and more accurately.
problem Calibration of GARCH-type option pricing models is computationally intensive and model-dependent.
method Trained ANN models on Monte Carlo simulation data to calibrate GARCH parameters.
result ANN outperforms traditional methods in calibration speed and accuracy.
A new method solves complex financial problems using deep learning.
problem Optimal stopping and option pricing in finance.
method Compound BSDE method, based on reformulating BSDEs.
result The method offers accurate and efficient solutions for high-dimensional problems.
Paper extends option-critic architecture to estimate natural gradient for reinforcement learning.
problem Estimating natural gradient in hierarchical reinforcement learning.
method Introduces natural option critic algorithm to estimate natural gradient for option's policy and termination function.
result Improves over vanilla gradient approach in experimental results.
Quantum algorithm for multi-asset option pricing under different volatility models.
problem Efficiently pricing multi-asset options under various volatility models using quantum computing.
method Developed an end-to-end quantum PDE framework for European option pricing, solving PDEs after discretization on spatial grids.
result Quantum framework provides polynomial improvement in resource usage compared to classical methods.
MO2 learns useful behaviours from past experience for new tasks.
problem Discovering useful behaviours from past experience and transferring them to new tasks.
method Model-Based Offline Options (MO2) framework supporting sample-efficient bottleneck option discovery over continuous state-action spaces.
result MO2 outperforms recent option learning methods on complex long-horizon continuous control tasks.
New model prices crypto options by clustering market regimes and using implied volatility.
problem Inaccurate option pricing for volatile crypto markets.
method Time-regime clustering with Implied Stochastic Volatility Model (ISVM).
result MR-ISVM overcomes complexity and adapts to market dynamics.
The paper presents a practical method for evaluating investment projects using real options.
problem Evaluating investment projects under uncertainty and strategic risk management.
method Binomial trees and real options techniques for evaluating investment projects.
result The method can be used for most real options and introduces Project Value at Risk for feasibility.
The paper introduces new methods for Asian option pricing using Laguerre quadrature.
problem Developing accurate pricing models for Asian options.
method Utilizes Laguerre quadrature and diffusion kernel approach.
result Demonstrates new techniques to solve complex Asian option pricing equations.
This paper compares LSM and ANN/GBM for pricing American put options under a complex model.
problem Pricing American put options using advanced techniques.
method Least-Squares Monte Carlo (LSM) and Artificial Neural Network (ANN) and Gradient Boosted Machine (GBM) Trees.
result LSM outperforms ANN and GBM in pricing American put options.