Derives pricing formulae for power binary and normal distribution standard options.
problem Developing pricing models for binary and standard options.
method Incorporates Buchen's formulae into power binary options and derives a formula for normal distribution standard options.
result Derives pricing formulae for power binary and normal distribution standard options.
Machine learning struggles to predict binary options movements due to randomness.
problem Predicting binary options movements using machine learning.
method Tested multiple machine learning models (RF, LR, GB, kNN) and neural networks (MLP, LSTM) on EUR/USD currency pairs.
result None of the models surpassed the ZeroR baseline accuracy, indicating randomness in binary options.
In this paper we extend Buchen's method to develop a new technique for pricing of some exotic options with several expiry dates(more than 3 expiry dates) using a concept of higher order binary option. At first we introduce the concept of higher order binary option and then provide the pricing formulae of n-th order b…
The paper analyzes binary option markets with exogenous information and price sensitivity.
problem Analyzing binary option markets with exogenous information and price sensitivity.
method Derive and analyze a continuous model of binary option markets with exogenous information, using Filippov surfaces and general assumptions on purchasing rules.
result Price always converges when exogenous information is constant, and price sensitivity affects price lag vs. information.
In this article, we study the problem of pricing defaultable bond with discrete default intensity and barrier under constant risk free short rate using higher order binary options and their integrals. In our credit risk model, the risk free short rate is a constant and the default event occurs in an expected manner whe…
Develops a binary tree model for option pricing with skew dynamics.
problem Option pricing in incomplete markets with skew dynamics.
method Binary tree model with skew Brownian motion dynamics.
result Model preserves skewness under both discrete and continuous time limits.
A method for making predictions with a reject option using conformal prediction.
problem Uncertainty in machine learning predictions, especially when models are unsure.
method Formalizing ML with reject option, using conformal prediction for distribution-free error guarantees.
result Theoretical guarantees on error rate for prediction sets with distribution-free validity.
Adversarial robustness improved by abstaining from decisions.
problem Improving classification accuracy in the presence of adversarial perturbations.
method Introducing an abstain option in binary classification problems, using metrics to quantify performance and robustness.
result There is a tradeoff between nominal performance and adversarial robustness.
The paper studies large deviation principles for stochastic volatility models with reflection, focusing on binary barrier options and call prices.
problem Large deviation principles for stochastic volatility models with reflection.
method Sample path and small-noise large deviation principles for the log-price process.
result Asymptotic behavior of binary barrier options and call prices in the small-noise regime.
Extends BBSM model to incorporate ESG ratings and path dynamics.
problem Price stock options considering historical market index dynamics and ESG ratings.
method Develops discrete, binary tree option pricing model under BBSM with ESG valuation.
result Model accurately fits stock price changes and European call option prices.
Adaptive Multilevel Splitting improves rare event pricing for financial derivatives.
problem Efficient pricing of binary options in rare event regimes with discontinuous payoffs.
method Adaptive Multilevel Splitting (AMS) reformulates rare-event problem as conditional events.
result AMS achieves up to 200-fold improvements over standard Monte Carlo, preserving unbiasedness.
Proposes an arbitrage approach to estimate election outcomes as binary martingales.
problem Estimating binary election outcomes with accuracy and minimizing Brier score.
method Uses dual martingale process and arbitrage valuation to price elections.
result Arbitrage valuation minimizes Brier score and reflects uncertainty in outcomes.
Study volatility models with rough paths, focusing on large deviations and option behavior.
problem Analyzing volatility in financial markets with very rough paths.
method Introduced time-inhomogeneous stochastic volatility models with Volterra Gaussian processes.
result Obtained large deviation principles for log-price processes in super rough Gaussian models.
Paper proposes algorithms for active learning of reject option classifiers.
problem Active learning of reject option classifiers is unaddressed in machine learning.
method Developed novel algorithms using double ramp and double sigmoid loss functions.
result Proposed algorithms efficiently reduce the number of labeled examples required.
We consider the problem of binary classification where one can, for a particular cost, choose not to classify an observation. We present a simple proof for the oracle inequality for the excess risk of structural risk minimizers using a lasso type penalty.
Quasi-Monte Carlo speeds up option Greeks calculation on GPUs.
problem Efficiently calculating option Greeks for risk management.
method Quasi-Monte Carlo (QMC) combined with GPU acceleration for pathwise sensitivity calculation.
result Increased computational speed and efficiency in estimating option Greeks.
Crowdsourcing with prospect theory handles spammers in binary classification.
problem Binary classification with spammers and reject option.
method Prospect theory for worker behavior, weighted majority voting for decision fusion.
result Asymptotic system performance and correct classification probability derived.
A sequential classifier minimizes test samples for binary and multi-class classification.
problem Minimizing test samples for sequential classification with unknown distributions.
method Proposes a classifier for binary and multi-class problems, analyzing error probabilities and extending results.
result Significant advantage over non-sequential classifiers, achieving same exponents without rejection option.
New methods for multicategory classification with reject and refine options reduce misclassification costs.
problem Reducing misclassification costs in multicategory classification problems.
method Margin-based multicategory classification methods with reject and refine options.
result The refine option provides more constructive information by ruling out implausible classes.
Study large deviation principle for fractional stochastic volatility models.
problem Large deviation principle for Volterra type fractional stochastic volatility models.
method Prove a small-noise large deviation principle under weaker conditions.
result Derive large deviation principle in small-time regime.
This paper considers binomial approximation of continuous time stochastic processes. It is shown that, under some mild integrability conditions, a process can be approximated in mean square sense and in other strong metrics by binomial processes, i.e., by processes with fixed size binary increments at sampling points. …
In this article, we consider a 2 factors-model for pricing defaultable bond with discrete default intensity and barrier where the 2 factors are stochastic risk free short rate process and firm value process. We assume that the default event occurs in an expected manner when the firm value reaches a given default barrie…
A new method for creating derivatives without oracles.
problem Lack of trust in external oracles for derivatives pricing.
method Using Replicating Market Makers (RMMs) to create derivative instruments.
result Demonstrated the feasibility of on-chain expiring options without oracles.
Enhances preference learning by incorporating response times into binary choices.
problem Limited information from binary choices about preference strength.
method Combines choices and response times using the EZ diffusion model.
result Response times improve utility estimation for strong preferences.
The study examines the discrepancies between binary forecasts and real-world outcomes, revealing their often misleading nature.
problem The confusion between binary forecasts and real-world payoffs in decision-making and prediction.
method Comparative analysis of binary forecasts, bets, and real-world continuous payoffs under different tail conditions.
result Binary forecasting abilities do not translate to better real-world performance, and vice versa, especially under nonlinearities.
Enhances binomial model with machine learning for microstructure effects.
problem Traditional binomial models ignore market microstructure effects like bid-ask spreads.
method Augments binomial tree with Random Forest classifiers trained on market data.
result Achieves 88.25% AUC in forecasting price movements using real-world data.
Prediction markets and crypto options show persistent pricing gaps.
problem Comparing prediction markets and crypto options for identical payoffs.
method Comparing Polymarket Yes prices with Binance call option prices.
result Mean pricing gap of 5.6 percentage points across 214 hourly observations.
A new model prices assets considering market microstructure effects.
problem Including market microstructure effects in dynamic asset pricing.
method Discrete binary tree model with history-dependent underlying security prices.
result The model preserves historical price dynamics and is market-complete, arbitrage-free.
Modern datasets are becoming heterogeneous. To this end, we present in this paper Mixed-Variate Restricted Boltzmann Machines for simultaneously modelling variables of multiple types and modalities, including binary and continuous responses, categorical options, multicategorical choices, ordinal assessment and category…
Parallelizes computation of expected values in binomial trees for financial option pricing.
problem High computational cost of evaluating expected values in binomial trees.
method Parallelizes the calculation of expected values into an 'embarrassingly parallel' problem and uses a parallel Monte Carlo method.
result Parallelization and Monte Carlo methods reduce computational cost and variance.
Method finds optimal binary classification rules under weighted misclassification loss.
problem Optimal binary classification rules for resource-limited settings with cost-sensitive decisions.
method Ensemble learning to derive prediction scores and associated thresholds minimizing weighted misclassification loss.
result Jointly derived score and threshold outperforms methods that derive score first and threshold second.
We propose a general framework for the simultaneous modeling of equity, government bonds, corporate bonds and derivatives. Uncertainty is generated by a general affine Markov process. The setting allows for stochastic volatility, jumps, the possibility of default and correlation between different assets. We show how to…
Survey on assessing and improving classifier calibration for better decision making.
problem Ensuring classifiers correctly quantify prediction uncertainty.
method Overview of principles, methods, and evaluation metrics for calibration.
result New methods and extensions from binary to multiclass settings.
Solves the film scheduling and staggered showtimes problem for movie theaters.
problem Maximize attendance and revenue by scheduling films with staggered showtimes.
method Binary integer linear optimization to find optimal schedules for each cluster of neighboring locations.
result Optimal scheduling cannot be done for all locations at once, but must be done for each cluster.
No multi-class labels needed for multi-class classification.
problem Multi-class classification without requiring class-specific labels.
method Meta classification learning using pairwise similarity prediction.
result The method learns a multi-class classifier from binary classifier for pairwise similarity.
Algorithm learns binary function efficiently under arbitrary covariate shift.
problem Learning binary function under arbitrary distributions P and Q.
method PQ-learning algorithm using reliable learner with selective classification.
result Polynomial-time algorithm for covariate shift learning.
Enhanced Random Forests outperform XGBoost across binary classification datasets.
problem Improving performance of Random Forests in binary classification.
method Adaptive sample and model weighting, iterative algorithm for sample weights, personalized tree weighting schemes.
result Significantly outperforms XGBoost across 15 binary classification datasets.
Developed R package for creating nomograms for any ML algorithms.
problem Creating nomograms for any machine learning algorithms.
method Formulated a function to transform ML prediction models into nomograms, requiring specific datasets.
result Created 5 types of nomograms for various ML algorithms and predictor types.
In this paper I empirically investigate prediction markets for binary options. Advocates of prediction markets have suggested that asset prices are consistent estimators of the "true" probability of a state of the world being realized. I test whether the market reaches a "consensus." I find little evidence for converge…
Gonogo offers tools for sensitivity experiments in R.
problem Conducting, analyzing, and simulating sensitivity experiments.
method Suite of R functions for various adaptive procedures.
result Achieving overlapping data and refining testing in distribution tails.
Optimized reverse quantum annealing speeds up portfolio optimization.
problem Optimizing portfolios using quantum and classical methods.
method Hybrid quantum-classical approach, including reverse quantum annealing.
result Optimized reverse quantum annealing is 100 times faster than forward quantum annealing.
Classifier chains link binary classifiers for multi-label learning, achieving state-of-the-art performance.
problem Multi-label learning problems where multiple labels can be assigned to instances.
method Linking off-the-shelf binary classifiers in a chain structure.
result Classifier chains achieve state-of-the-art performance across various datasets and metrics.
Bayesian inference identifies model parameters from financial data to detect arbitrage opportunities.
problem Identifying model parameters from financial data to detect arbitrage opportunities.
method Bayesian inference approach using Markov Chain Monte Carlo (MCMC) algorithm.
result Bayesian inference can estimate unknown trend and volatility coefficients from measured data.
Motivated by an application in computational biology, we consider low-rank matrix factorization with {0,1}-constraints on one of the factors and optionally convex constraints on the second one. In addition to the non-convexity shared with other matrix factorization schemes, our problem is further complicated by a c…
Predicting battery lifespan from early cycles using deep learning.
problem Predicting battery lifespan from the first 100 cycles.
method Used a deep network design including 1-D convolution, LSTMs, and an optional Attention layer.
result Achieved competitive results, with a test MAPE error of 12.5%.
Large deviation principles for multivariate stochastic volatility models.
problem Understanding the behavior of log-processes in multivariate stochastic volatility models.
method Establishing a comprehensive sample path large deviation principle for log-processes.
result Asymptotic formulas for first exit times and barrier option prices derived from the LDP.
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.
Study bounds for prices of European and American options with optional termination.
problem Bounding prices of options with potential termination.
method Duality results linking upper prices of vulnerable options to American options with constrained exercise times.
result Linking upper prices of vulnerable options to American options and game options.