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.
Math and dance blend in choreographer's research.
problem Exploring intersections between dance and mathematics.
method Choreographic practice and mathematical concepts.
result Examples of fractals, braids in choreography.
Automl is the key technology for machine learning problem. Current state of art hyperparameter optimization methods are based on traditional black-box optimization methods like SMBO (SMAC, TPE). The objective function of black-box optimization is non-smooth, or time-consuming to evaluate, or in some way noisy. Recent y…
Bringing transparency to black-box decision making systems (DMS) has been a topic of increasing research interest in recent years. Traditional active and passive approaches to make these systems transparent are often limited by scalability and/or feasibility issues. In this paper, we propose a new notion of black-box D…
We present a general sufficient condition for the formation of black holes due to concentration of angular momentum. This is expressed in the form of a universal inequality, relating the size and angular momentum of bodies, and is proven in the context of axisymmetric initial data sets for the Einstein equations which …
Scaling properties of the BUX index are similar to those observed in other parts of the world. The main difference is that the traditional quantities like volatility, growth and autocorrelation of returns follows more closely the assumptions of the traditional stock market theory developed by Bachelier and by Black and…
Enhanced pairs trading with Black-Litterman model outperforms market indexes.
problem Underperformance of pairs trading in volatile or distressed markets.
method Integrated Black-Litterman model with pairs trading strategy.
result Superior performance compared to S\&P 500 index under various market conditions.
A non-traditional approach to the discretization of differential-geometrical connections was suggested by the authors in 1997. At the same time we started studying first order difference ``black and white triangle operators (equations)'' on triangulated surfaces with a black and white coloring or triangles. In this wor…
Neural network learns to solve Black-Scholes for stock options.
problem Stock option pricing using the Black-Scholes Equation.
method Neural Networks applied to solve the Black-Scholes Equation.
result Neural network can accurately forecast stock option prices.
We consider scattering by an abstract compactly supported perturbation in R^n. To include the traditional cases of potential, obstacle and metric scattering without going into their particular nature we adopt the "black box" formalism developed jointly with Sjostrand [23]. It is quite likely that one could extend the r…
Informer improves option pricing accuracy in volatile markets.
problem Challenges in accurate option pricing due to market volatility and traditional model limitations.
method Applying Informer, a Transformer-based neural network, for option pricing.
result Informer outperforms traditional models in option pricing accuracy.
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.
Hybrid neural network infers states from black-box systems.
problem Inference of state models in black-box systems without source code instrumentation.
method Convolutional and recurrent layers learn correlations between time series signals.
result Improves state change point detection by up to 102%.
Interpretable machine learning has become a strong competitor for traditional black-box models. However, the possible loss of the predictive performance for gaining interpretability is often inevitable, putting practitioners in a dilemma of choosing between high accuracy (black-box models) and interpretability (interpr…
Bayesian optimization (BO) is a successful methodology to optimize black-box functions that are expensive to evaluate. While traditional methods optimize each black-box function in isolation, there has been recent interest in speeding up BO by transferring knowledge across multiple related black-box functions. In this …
Probabilistic numerics expands numerical tasks with black box methods.
problem Difficult conditioning of random variables in numerical tasks.
method Construct probabilistic numerical methods based on final outputs, extrapolating limiting quantities.
result Higher orders of convergence achieved in various numerical tasks.
This research improves option pricing models using Heston, GARCH, and jump diffusion models.
problem Inaccurate option pricing due to Black-Scholes assumptions.
method Monte Carlo simulation, GARCH model, Heston model, Merton jump-diffusion model.
result Heston model produces estimates closer to market prices, Merton model performs well for volatile assets, GARCH model improves volatility forecasts.
Study uses LLMs to improve Black-Litterman portfolio optimization.
problem Systematically generating investor views for Black-Litterman model.
method Translates LLM return forecasts and uncertainty into Black-Litterman inputs.
result LLM-driven portfolios outperform traditional baselines.
We study proper losses for discrete generative models without knowing the target distribution.
problem Evaluating generative models in the discrete setting without direct access to the target distribution.
method Define and construct black-box proper losses using statistical estimation theory.
result Black-box proper losses must be of polynomial form and involve more samples than the polynomial degree.
VOPy optimizes multiple objectives with flexible cone-based ordering.
problem Optimizing multiple objectives with partial order constraints.
method Flexible cone-based ordering, modular architecture, integration of existing and novel methods.
result Advances black-box vector optimization in noisy, discrete, or limited budget settings.
Efficiently explains model outputs using HSIC, a dependence measure.
problem Efficiently explain model outputs for various architectures.
method HSIC, RKHS, Reproducing Kernel Hilbert Spaces, black-box attribution.
result Up to 8 times faster than previous methods while maintaining fidelity.
Kolmogorov-Arnold Networks offer interpretable models for energy applications.
problem Lack of interpretability in modern machine learning methods for sensitive industries.
method Symbolic regression with Kolmogorov-Arnold Networks compared to traditional feedforward neural networks.
result Kolmogorov-Arnold Networks yield perfectly interpretable models and learn real, physical relations.
ETCNN uses neural networks to price American options accurately.
problem Accurately pricing American options with inequality constraints.
method ETCNN framework solving BSM equations with exact terminal condition.
result ETCNN achieves high accuracy and robustness across various scenarios.
Reinforcement learning improves option pricing and hedging accuracy.
problem Improving financial instrument pricing and hedging accuracy.
method Q-Learning Black Scholes approach applied to option pricing and hedging.
result The reinforcement learning model accurately estimates option prices and hedging strategies under various volatility and moneyness levels.
New findings on when to use action space exploration in reinforcement learning.
problem Understanding when to use action space exploration over traditional methods.
method Theoretical analysis and empirical testing of simple exploration methods.
result Exploration in action space is preferred when parametric complexity exceeds action space dimensionality and horizon length.
Improves topic modeling using LLM embeddings and Poisson process.
problem Traditional topic modeling ignores word context; LLMs offer better contextual embeddings.
method Convert documents to word embeddings, model as Poisson process, estimate topics using flexible algorithm.
result Method integrates LLMs without fine-tuning, offers advantages over traditional methods.
Optimising black-box functions is important in many disciplines, such as tuning machine learning models, robotics, finance and mining exploration. Bayesian optimisation is a state-of-the-art technique for the global optimisation of black-box functions which are expensive to evaluate. At the core of this approach is a G…
Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results. Different attack strategies have been proposed to generate adversarial examples, but how t…
FlowVAT improves variational inference for multi-modal distributions.
problem Mode-seeking behavior and collapse in variational inference for complex posteriors.
method Conditional tempering approach for normalizing flow variational inference.
result FlowVAT outperforms traditional and adaptive annealing methods in multi-modal distributions, finding more modes and achieving better ELBO values.
Develops a method to find costly high-confidence errors in black box models.
problem Finding rare high-confidence errors missed by random sampling.
method Adversarial perturbation-guided search technique to find errors at rates greater than expected given model confidence.
result Our Adversarial Distance search discovers high-confidence errors at a rate greater than expected given model confidence.
In this paper, we make an important step towards the black-box machine teaching by considering the cross-space machine teaching, where the teacher and the learner use different feature representations and the teacher can not fully observe the learner's model. In such scenario, we study how the teacher is still able to …
RAAL optimizes black box function optimization with multifidelity models.
problem Time-consuming and unfeasible black box optimization.
method Resource Aware Multifidelity Active Learning (RAAL) for efficient optimization.
result RAAL optimizes black box function optimization with multifidelity models.
Paper calculates greeks for DeFi LPs and introduces Impermanent Gain.
problem Liquidity Providers in DeFi are exposed to Impermanent Loss.
method Tailored Black & Scholes formulas for DeFi markets.
result Introduced Impermanent Gain for risk management.
Paper proposes hybrid approach for transparent credit scoring models.
problem Lack of transparency in machine learning models limits their use in regulated environments.
method Post-hoc interpretation of black-box models guides feature selection, followed by training glass-box models.
result Reduces feature usage from 106 to 10 while maintaining comparable performance.
An interacting Black-Scholes model for option pricing, where the usual constant interest rate r is replaced by a stochastic time dependent rate r(t) of the form r(t)=r+f(t) dW/dt, accounting for market imperfections and prices non-alignment, was developed in [1]. The white noise amplitude f(t), called arbitrage bubble,…
It is very useful to integrate human knowledge and experience into traditional neural networks for faster learning speed, fewer training samples and better interpretability. However, due to the obscured and indescribable black box model of neural networks, it is very difficult to design its architecture, interpret its …
Bayesian calibration of black-box computer models offers an established framework to obtain a posterior distribution over model parameters. Traditional Bayesian calibration involves the emulation of the computer model and an additive model discrepancy term using Gaussian processes; inference is then carried out using M…
We consider the problem of learning a forest of nonlinear decision rules with general loss functions. The standard methods employ boosted decision trees such as Adaboost for exponential loss and Friedman's gradient boosting for general loss. In contrast to these traditional boosting algorithms that treat a tree learner…
This paper explores causal analysis in machine learning for better interpretability.
problem The lack of causality in traditional interpretable machine learning models.
method An overview of causal approaches for interpretable machine learning.
result Causal analysis improves the interpretability of machine learning models.
Improved inference for models with continuous latent variables.
problem Inference accuracy with traditional variational methods is limited.
method Reparameterized Variational Rejection Sampling (RVRS) using a proposal distribution with a reparameterized gradient estimator.
result RVRS offers a better trade-off between computational cost and inference fidelity.
Adaptive uncertainty quantification improves black-box model predictions in generative AI.
problem Improving uncertainty quantification for black-box models in generative AI.
method Adaptive partitioning and local calibration of conformity scores.
result Local tightening of uncertainty sets with adaptive bands.
MEVA aggregates model predictions to improve accuracy without needing model details.
problem Improving model accuracy by combining multiple models.
method Non-intrusive, data-driven framework that treats models as black boxes and optimizes aggregation methods.
result MVA outperforms MEA in estimating aggregated predictions, enhancing robustness and accuracy.
Proposes a method to improve deep active learning for NER tasks.
problem Weaknesses of existing deep active learning algorithms in practice.
method Estimates error decay curves of feature-defined subsets to improve sampling efficiency and robustness.
result Significantly outperforms diversification-based methods for black-box NER taggers and makes sampling more robust to labeling noise.
The paper explores neural networks for improving delta hedging in financial markets.
problem Real-world financial markets do not perfectly match the assumptions of the Black-Scholes model.
method The authors test various neural architectures (RNN, TCN, Attention, MLP) for delta hedging and combine them with traditional models.
result NNHedge framework provides a pipeline for model development and assessment.
Market maker handles negative prices with unique asset swapping.
problem Handling negative prices in financial markets.
method Unique market mechanism with numeraire currency, liquidity extensions.
result Liquidity fingerprint and payoff compared to established models.
Robo-advisors use MPC to create dynamic investment strategies.
problem Static allocation methods limit robo-advisors' effectiveness.
method Combines MPC with Hidden Markov Model and Black-Litterman for dynamic asset allocation.
result MPC-based strategies outperform static approaches in dynamic and risk-budgeting criteria.
Machine learning impacts computational math, offering new functions approximations.
problem Machine learning's black box nature hinders further progress in computational math.
method Analyzes machine learning's impact on computational math and vice versa.
result Integrating computational math with machine learning can enhance both fields.
This study uses DRL to hedge American put options, outperforming traditional methods.
problem Hedging American put options with high accuracy and low transaction costs.
method Deep Deterministic Policy Gradient (DDPG) method, trained on stochastic volatility models.
result DRL agents outperform traditional methods in both simulated and real-world scenarios.