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.
The paper explores how foresight can improve stock selling decisions.
problem Maximizing stock selling value with future information.
method Establishing bounds and deriving a simple exercise rule.
result Extracting most of the value from additional future information.
Expands statistical background for knee osteoarthritis treatment models.
problem Developing optimal exercise and weight loss treatments for knee osteoarthritis.
method Precision medicine models and jackknife cross-validation method.
result Jackknife estimator provides consistent value function estimation.
This work proposes a method to price American basket options using a Markovian projection.
problem Pricing American basket options in high dimensions is computationally expensive.
method Use a stopping rule based on a low-dimensional Markovian projection of the basket's dynamics.
result Approximate the optimal early-exercise boundary in a lower-dimensional space, providing bounds for the option price.
Study evaluates technical trading rules on various markets, introduces DFRD+/- method.
problem Evaluating the profitability and robustness of technical trading rules across different markets.
method Investigated 21,000 technical trading rules on 12 markets over 12 years, introduced DFRD+/- method.
result DFRD+/- method is adaptive and more powerful, accommodating discrete p-values.
Model detects exercise fatigue with high accuracy.
problem Detecting exercise fatigue in real-time.
method Feature extraction using AHP, machine learning.
result 98.65% accuracy in detecting exercise fatigue.
A new method for pricing American options using exercise rate optimization.
problem Pricing American options efficiently and accurately.
method Monte Carlo simulation and optimization of exercise rates.
result The method provides the correct option price and is efficient for various models.
Pricing Bermudan swaptions with few exercise dates using analytic methods.
problem Pricing Bermudan swaptions with few exercise dates
method Analytic decomposition and backward induction under rolling forward measures
result Pricing formulas with decomposition and boundary linearity
This paper evaluates how XVA affects option exercise decisions.
problem Ignoring XVA in exercise decisions can lead to incorrect valuation.
method Regression techniques to evaluate XVA's impact on exercise decisions.
result XVA significantly impacts the exercise decision at the boundary.
Investors often miss out on early exercise of American options with dividends, volatility, and jumps.
problem Investors suboptimal exercise of American call options on dividend-paying stocks.
method Used a fast numerical technique to analyze a large database of investor decisions and incorporated stochastic volatility and jumps in pricing models.
result Pricing models with stochastic volatility and jumps reduce the loss from suboptimal exercise by a quarter.
Pen-and-paper exercises cover various machine learning topics.
problem None explicitly stated, focuses on learning through exercises.
method Pen-and-paper exercises on machine learning topics.
result Comprehensive coverage of machine learning concepts through exercises.
Continuous-time optimal stopping solved with deep reinforcement learning
problem Optimal stopping problems in continuous time
method CARLOS (Continuous-time Adaptive Reinforcement Learning for Optimal Stopping)
result Higher prices than existing Bermudan solvers, approaching American upper bound
Game options study gradual exercise and cancellation with transaction costs.
problem Analyzing game options with gradual exercise and cancellation under proportional transaction costs.
method Developed algorithmic constructions for bid and ask prices, superhedging strategies, and optimal mixed stopping times.
result Increased flexibility in hedging leads to tighter bounds on option price.
Paper examines floating exercise boundaries for American options in time-inhomogeneous models.
problem Floating exercise boundaries in time-inhomogeneous models with negative interest rates or yields.
method Semi-analytical approach for pricing American options.
result Specialized pricing methodologies are required for models with floating exercise boundaries.
Machine learning predicts exercise load from heart rate data post-exercise.
problem Monitoring energy expenditure in real life.
method Machine learning methods (linear regression, etc.) applied to heart rate data.
result Random forest and k-nearest neighbors classifiers predict load levels accurately.
Optimizing option exercise policies based on variance optimal martingale measure can lead to unappealing results.
problem Optimizing American option exercise policies under the variance optimal martingale measure can result in unappealing policies.
method Optimizing option exercise policies under the variance optimal martingale measure, then anchoring to the resulting value of this policy.
result Optimizing option exercise policies based on the variance optimal martingale measure can lead to unappealing results.
NeuralCD diagnoses student proficiency in exercises using neural networks.
problem Capturing complex student-exercise interactions for accurate cognitive diagnosis.
method Proposes NeuralCD framework using neural networks to learn complex interactions between students and exercises, incorporating monotonicity for interpretability.
result Demonstrates effectiveness of NeuralCD framework on real-world datasets, achieving both accuracy and interpretability.
Closed-form solution found for American put option boundary.
problem Finding the optimal exercise boundary for American put options.
method Three models of stock price dynamics with time-dependent parameters, leading to a closed-form solution for the exercise boundary.
result Explicit closed-form solution for the optimal exercise boundary of American put options.
American options in a multi-asset market model with proportional transaction costs are studied in the case when the holder of an option is able to exercise it gradually at a so-called mixed (randomised) stopping time. The introduction of gradual exercise leads to tighter bounds on the option price when compared to the …
MEx dataset benchmarks HAR and multi-modal fusion for exercise quality.
problem Recognizing and evaluating exercise quality for Musculoskeletal Disorders patients.
method Multi-sensor, multi-modal dataset with four sensors (pressure mat, depth camera, accelerometers) for HAR and exercise quality assessment.
result Reference performance for each sensor identified, exposing their strengths and weaknesses.
Study uses social media analytics to identify exercise-related topics.
problem Understanding exercise-related discussions on social media.
method Data collection, topic modeling, and data annotation.
result 86% of detected topics were meaningful after annotation.
Research analyzes public opinions on Twitter about diabetes, diet, exercise, and obesity.
problem Understanding public health opinions on social media.
method Developed a multi-component semantic and linguistic framework to collect and analyze Twitter data.
result Strongest correlation found between exercise and obesity; other notable correlations between diabetes and obesity, diet and obesity.
New pricing methods for α-quantile and early-exercise options using Spitzer identities.
problem Pricing perpetual Bermudan and American options and α-quantile options. method Based on Spitzer identities for general Lévy processes and Wiener-Hopf method.
result Direct calculation of the optimal exercise barrier for early-exercise options.
This paper analyzes model risk in American put options using Heston volatility model.
problem Model risk in optimal exercise of American put options.
method Benchmark methodology of Hull and Suo [2002], Heston stochastic volatility model, numerical finite difference methods.
result Optimal exercise behavior is influenced by stochastic volatility dynamics and return-volatility correlation, creating model risk.
ECGID research focuses on rest but not exercise, this study evaluates both.
problem ECGID performance under exercise is insufficiently studied.
method Various learning methods applied to ECG dataset.
result Current ECGID methods fail under exercise conditions.
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.
Optimal exercise boundary for put options with delivery lags identified.
problem Analyzing the optimal exercise time for American put options with delivery lags.
method Decomposing the option into a European put and a new American-style derivative, using free boundary techniques.
result The optimal exercise boundary exists and is a strictly increasing and smooth curve.
Paper defines when early exercise of American options is optimal under negative rates.
problem Determining optimal exercise times for American options with negative interest rates.
method Developed a new integral equation to price options and find exercise boundaries under negative rates, using modified fixed point method.
result Successfully developed and validated a new algorithm for pricing American options under negative rates.
The paper presents a method for personalized exercise recommendations that improves learner skill gain.
problem Adapting to individual needs in large, diverse groups of learners in digital environments.
method Contextual Thompson Sampling to select exercises that advance learner skill.
result The method recommends exercises associated with greater skill improvement and adapts to learner differences.
AI predicts currency strength based on economic fundamentals, outperforming traditional methods.
problem Exchange rate disconnect puzzle documented by Meese and Rogoff (1983).
method Generative AI (ChatGPT and DeepSeek) forecasts currency returns using economic data.
result Simple trading strategy based on AI fundamentals outperforms traditional currency factors.
Optimal exercise timing of stock options analyzed with varying information on drift change.
problem Analyzing optimal exercise timing of stock options with varying information on drift change.
method Rigorous mathematical analysis and numerical methods to solve optimal stopping problems.
result Characterization of optimal exercise boundaries and smooth pasting properties in both information scenarios.
Researchers find the optimal exercise time for American options using a specific type of diffusion process.
problem Finding the optimal time to exercise American options with a time-dependent Ornstein-Uhlenbeck process.
method Optimal stopping problem, probabilistic arguments, non-linear Volterra-type integral equation, Picard iteration algorithm.
result They derive a non-linear Volterra-type integral equation and prove the exercise boundary's Lipschitz continuity and differentiability almost everywhere.
RKT model improves knowledge tracing by considering exercise relations and student forget behavior.
problem Traditional KT models fail to consider both exercise relations and student forget behavior.
method RKT model uses relation-aware self-attention to incorporate exercise relations and student forget behavior.
result RKT model outperforms state-of-the-art KT methods on real-world datasets.
A deep learning framework assesses physical rehabilitation exercises.
problem Lack of versatile, robust, and practical assessment methods for rehabilitation exercises.
method Deep learning framework with metrics, scoring functions, and neural networks.
result First implementation of deep neural networks for rehabilitation performance assessment.
New framework values ESOs with multiple exercises and job termination risk.
problem Valuing ESOs with complex exercise patterns and job termination risk.
method Fourier transform, finite differences, and maturity randomization methods.
result Analytic formulae for ESO costs under various conditions.
Three physics-constrained regression exercises for image velocimetry and turbulence modeling.
problem Image velocimetry and turbulence modeling challenges.
method Physics-constrained regression exercises implemented as toy problems.
result Python codes provided for all exercises.
The paper extends VIX option pricing models and finds optimal exercise boundaries.
problem Pricing American VIX options under generalized volatility models.
method Extended 3/2 and 1/2 models, used free-boundary approach and local time-space calculus.
result Found optimal exercise boundaries for VIX factor process.
New method uses Hermite polynomials for American option valuation.
problem Valuation of American options with complex jump-diffusion dynamics.
method Hermite polynomial expansions of transition density and early exercise premium.
result Converging approximations to true option prices and exercise boundaries.
The purpose of this paper is to construct the early exercise boundary for a class of nonlinear Black--Scholes equations with a nonlinear volatility depending on the option price. We review a method how to transform the problem into a solution of a time depending nonlinear parabolic equation defined on a fixed domain. R…
Develops a real-time exercise recommendation system using deep learning.
problem Improving accuracy in exercise recommendation systems without user feedback.
method Deep recurrent neural network with attention mechanisms, real-time expert feedback.
result Improved accuracy in exercise recommendation system after real-time active learning.
In this paper we generalize and analyze the model for pricing American-style Asian options due to (Hansen and Jorgensen 2000) by including a continuous dividend rate q and a general method of averaging of the floating strike. We focus on the qualitative and quantitative analysis of the early exercise boundary. The fi…
The paper extends a learning heuristic to high-dimensional contexts, reducing the risk of unusual actions.
problem Sequential learning problems in high dimensions, especially in dynamic pricing and auctions.
method Introducing a conservative εt-greedy rule that limits the adoption of new actions to a focused set of promising actions. result Reasonable bounds for cumulative regret and improved regret bound for conservative version compared to non-conservative.
We study the behavior of the critical price of an American put option near maturity in the exponential Lévy model when the underlying stock pays dividends at a continuous rate. In particular, we prove that, in situations where the limit of the critical price is equal to the stock price, the rate of convergence to the l…
Optimizes storage valuation using variational analysis.
problem Optimizing intrinsic storage valuation.
method Variational analysis to solve the problem.
result The optimal exercise strategy is revealed, and the solution depends on different constraint types.
This article gives solutions to the exercises in Bestvina and Feighn's paper on Sela's work on limit groups. We prove that all constructible limit groups are limit groups and give an account of the shortening argument of Rips and Sela.
New method simplifies analysis of exercise timing for ambiguous integral option contracts.
problem Impact of ambiguity on optimal exercise timing of integral option contracts.
method Parameterized family of excessive functions generating supermartingales, simplifying multidimensional problem to one-dimensional static optimization.
result Value of optimal policy and worst case measure expressed in terms of these processes.
Researchers find a way to price American options without relying on specific asset price models.
problem Determining the upper bound on the price of American options under model uncertainty.
method Using martingale optimal transport problem to describe model uncertainty and proving that optimal exercise schemes must be nonrandomized under certain conditions.
result The price upper bound and its relaxed version coincide under suitable convexity conditions, removing the need for the model-free price upper bound to be nonrandomized.
In the standard models for optimal multiple stopping problems it is assumed that between two exercises there is always a time period of deterministic length δ, the so called refraction period. This prevents the optimal exercise times from bunching up together on top of the optimal stopping time for the one-exercise c…