Paper examines pricing and hedging for cross-currency swaps referencing backward-looking rates.
problem Pricing and hedging cross-currency swaps with backward-looking rates.
method Uses interest rate and currency futures for hedging, analyzes arbitrage-free multi-curve setting.
result Explicit pricing and hedging results for CCBS with backward-looking rates.
Cross-domain recommendation has been proposed to transfer user behavior pattern by pooling together the rating data from multiple domains to alleviate the sparsity problem appearing in single rating domains. However, previous models only assume that multiple domains share a latent common rating pattern based on the use…
DARec adapts rating patterns across domains without auxillary info.
problem Cross-domain recommendation challenges.
method Deep domain adaptation model (DARec) that transfers rating patterns.
result Best performance on public datasets.
The paper develops a cross-validation method for improving signal denoising techniques.
problem Improving signal denoising methods for nonparametric regression.
method Develops a general cross-validation framework for signal denoising and applies it to Trend Filtering and Dyadic CART.
result Cross validated versions of Trend Filtering and Dyadic CART achieve nearly optimal convergence rates.
Model learns aging rates from single cross-sectional data.
problem Cross-sectional data limits traditional time-series methods.
method Latent-variable model with order-isomorphic nonlinear function.
result Reconstructs aging rates from single cross-sectional data.
Develops a new model for cross-currency derivatives pricing.
problem Pricing cross-currency derivatives in a complex market model.
method Introduces a random field LIBOR market model to handle uncertainty in forward LIBOR rates.
result Derives exact and approximate pricing formulas for various derivatives.
New method controls false discoveries in financial asset pricing.
problem Controlling false discoveries in time series with unknown correlations.
method Double bootstrapping method to control false discovery rate.
result Superior statistical power and controlled false discovery rate.
Abstract framework for cross-currency interest rate contracts.
problem Handling cross-currency markets with collateral and incompleteness.
method Developed a general HJM framework for abstract market indices.
result Enabled simultaneous description of multiple currency interest rate products.
The study compares A and N shares returns of Chinese firms cross-listed in U.S. markets.
problem Analyzing the impact of cross-listing on Chinese stock returns.
method Used CAPM for expected returns, GARCH model for volatility, and event study method.
result Cross-listing significantly affected N shares negatively, with increased volatility for some companies.
The study identifies features making cross-impact relevant in explaining price variance of US assets.
problem Understanding the relevance of cross-impact in explaining price variance of US assets.
method Using tick-by-tick data spanning 5 years for 500 US assets, the study investigates the features making cross-impact relevant.
result Price formation is endogenous within highly liquid assets, influencing less liquid correlated products with a constrained impact velocity.
Model shows triangular arbitrage key to cross-currency correlations in forex markets.
problem Understanding cross-currency correlations in forex markets.
method Agent-based model of market interactions.
result Triangular arbitrage is primary driver of cross-currency correlations.
Paper models foreign exchange markets and develops an on-line portfolio selection algorithm.
problem Modeling and predicting returns in foreign exchange markets.
method Matrix-valued time series model, trading matrices, and cross rate method.
result Proves the profitability and universality of the on-line portfolio selection algorithm.
Study uses multifractal detrended cross-correlation to detect Forex arbitrage opportunities.
problem Detecting arbitrage opportunities in Forex markets.
method Multifractal detrended cross-correlation analysis applied to Forex time series.
result Strong cross-correlations found between exchange rates involved in triangular relations, including AUD and NZD.
Paper optimizes prediction in semi-functional linear models using kernel methods.
problem Optimizing prediction in semi-functional linear models with functional and nonparametric components.
method Double-penalized least squares method in reproducing kernel Hilbert spaces, with regularization parameter selection via generalized cross validation.
result Achieves minimax optimal rates of convergence for both functional and nonparametric components.
Paper presents a defense framework against adversarial examples.
problem Vulnerability of deep neural networks to adversarial examples.
method Cross-layer strategic ensemble defense with input and output transformations.
result Strategic ensemble defense achieves high defense success rates and robustness.
Proposes new method for calibrating treatment effect predictors.
problem Calibrating predictors of heterogeneous treatment effects.
method Causal isotonic calibration and cross-calibration.
result Achieves fast calibration rates under weak conditions.
KQSP method prevents crossing issues in probabilistic K-line forecasts.
problem Quantile and K-line crossing issues in probabilistic K-line forecasts.
method Parameter-free and training-free reconciliation method (KQSP).
result KQSP reduces crossing rates to zero for all test data.
New methods improve anomaly detection with reduced false positives.
problem Effective anomaly detection with controlled error rates.
method Leave-one-out-, bootstrap-, and cross-conformal anomaly detection methods.
result Improved anomaly detection with reduced false positives.
We have recently introduced the ``thermal optimal path'' (TOP) method to investigate the real-time lead-lag structure between two time series. The TOP method consists in searching for a robust noise-averaged optimal path of the distance matrix along which the two time series have the greatest similarity. Here, we gener…
New methods improve cross-conformal prediction's prediction sets without sacrificing coverage guarantees.
problem Improving the width of prediction sets in cross-conformal prediction.
method Proposed new variants of existing methods based on recent results on more efficient combination of p-values.
result Smaller prediction sets achieved without compromising theoretical guarantees.
Optimal curves minimize crossings on surfaces.
problem Minimizing crossings on congruence surfaces.
method Examining systoles and their intersections.
result Modular systoles have minimal crossing numbers.
New method improves cross-validation for sparse reduced rank regression models.
problem Inconsistent parameter selection in cross-validation for high-dimensional data.
method Proposes cross-validation of projection-selection patterns to avoid inconsistency issues.
result Develops new scale-free information criteria for minimax optimal error rate.
Mitigates bias in evaluations by considering known outcome information.
problem Bias in evaluations due to external factors like grades or acceptance.
method Formulates bias as a partial ordering, solves regularized optimization problem.
result The debiasing method improves evaluation accuracy by adapting regularization.
Proposes a non-crossing deep neural network quantile regression method.
problem Quantile crossing in nonparametric quantile regression.
method Non-crossing constraints via rectified linear unit penalty function.
result Established non-asymptotic upper bounds for excess risk.
Simple bounds show most cross-sectional predictability findings are likely true.
problem Determining the validity of cross-sectional return predictability findings.
method Developed simple and intuitive bounds on the false discovery rate (FDR).
result Bounds show the FDR is small, indicating most findings are likely true.
For the purpose of elucidating the correlation among currencies, we analyze daily and high-resolution data of foreign exchange rates. There is strong correlation for pairs of currencies of geographically near countries. We show that there is a time delay of order less than a minute between two currency markets having a…
Neural networks improve efficiency in integrating multi-dimensional phase spaces in particle physics.
problem Efficiently integrating multi-dimensional phase spaces in particle physics.
method Optimized Neural Network (NN) algorithm for phase space integration.
result NN-based approach achieves unweighting efficiencies of 30-75% in various particle physics examples.
A scalable PyTorch framework for non-crossing quantile regression.
problem Non-crossing quantile regression to avoid impossible negative probability densities.
method CJQR-ALM combining Augmented Lagrangian Method, differentiable pinball loss, and L-BFGS optimization.
result Achieves near-zero crossing rates on large datasets within minutes.
DHEN improves CVR prediction for ads with multitask learning and auxiliary loss.
problem Predicting conversion rates in ad-recommendation systems.
method DHEN integrates multiple feature-crossing modules and uses a multitask learning framework, ablation studies, and self-supervised auxiliary loss.
result DHEN achieves state-of-the-art performance in CVR prediction.
New method selects best HTE estimator without ground-truth treatment effects.
problem Selecting best HTE estimator from multiple candidates.
method Cross-fitted, exponentially weighted test statistic with two-way sample splitting.
result Empirically, reliable error control and reduced false selections.
Study shows how many crossings arise in curves on surfaces.
problem Understanding crossings in curves on surfaces.
method Proved a minimum crossing number growth rate for m curves. result Effective bounds with optimal growth rates on every orientable surface.
Bayesian model averaging, model selection and its approximations such as BIC are generally statistically consistent, but sometimes achieve slower rates og convergence than other methods such as AIC and leave-one-out cross-validation. On the other hand, these other methods can br inconsistent. We identify the "catch-up …
Paper examines LASSO for high-dimensional predictive regression, improving its performance in forecasting unemployment.
problem High-dimensional predictive regression with many predictors and unit roots.
method LASSO with new probabilistic bounds for consistency.
result LASSO maintains its asymptotic guarantee with standardized predictors and improves forecasting of unemployment.
Improved Gibbs sampler for crossed random effects models scales better with data.
problem Complexity issues in Gibbs samplers for crossed random effects models.
method Proposed a collapsed Gibbs sampler that is provably scalable.
result The collapsed Gibbs sampler outperforms alternative algorithms significantly.
In finance, one usually deals not with prices but with growth rates R, defined as the difference in logarithm between two consecutive prices. Here we consider not the trading volume, but rather the volume growth rate R~, the difference in logarithm between two consecutive values of trading volume. To this end…
JSCN improves cross-domain recommendation by learning domain-invariant user representations.
problem Cross-domain recommendation data sparsity and domain-incompatibility issues.
method JSCN uses multi-layer spectral convolutions on different graphs to learn domain-invariant user representations and domain adaptive user mappings.
result Significant improvement in cross-domain recommendation performance (9.2% recall, 36.4% MAP improvements).
Improves NMT with user feedback from eBay ratings and search tasks.
problem Improving neural machine translation quality with user feedback.
method Offline bandit learning of NMT parameters using real user feedback from eBay.
result Implicit task-based feedback from cross-lingual search tasks improves NMT quality.
Study evaluates UK CDC schemes, finding intergenerational cross-subsidies in flat-accrual schemes and dynamic-accrual schemes can reduce but not eliminate them.
problem Intergenerational cross-subsidies in UK CDC schemes, particularly in flat-accrual schemes.
method Comparison of flat-accrual and dynamic-accrual CDC schemes, analysis of performance and level of cross-subsidies.
result Dynamic-accrual schemes can reduce but not eliminate intergenerational cross-subsidies, while flat-accrual schemes often have significant cross-subsidies.
Cryptocurrency market decouples from Forex, showing multifractality.
problem Decoupling of crypto market from Forex.
method High-frequency recordings and multiscale cross-correlations.
result Cryptocurrency market shows multifractality, decoupling from Forex.
A new method controls risk for set predictors using cross-validation.
problem Inefficient set predictors when data limited.
method Cross-validation conformal risk control (CV-CRC).
result CV-CRC offers theoretical guarantees and reduces set size.
In order to study the geometry of interest rates market dynamics, Malliavin, Mancino and Recchioni [A non-parametric calibration of the HJM geometry: an application of Itô calculus to financial statistics, {\it Japanese Journal of Mathematics}, 2, pp.55--77, 2007] introduced a scheme, which is based on the Fourier Seri…
Maximizes coding rate difference for robust, discriminative features.
problem Learning robust, discriminative features from high-dimensional data.
method Maximal Coding Rate Reduction (MCR^2) principle.
result Significantly more robust to label corruptions in classification.
Study improves predictive performance testing for high-dimensional data using exhaustive nested cross-validation.
problem Reproducibility issues in K-fold cross-validation for high-dimensional data. method Proposes a novel predictive performance test based on exhaustive nested cross-validation, addressing computational complexity with a closed-form expression.
result Demonstrates the effectiveness of Ridge-based methods in high-dimensional predictive performance testing.
Proposes ADC for cross-domain recommendation balancing user preferences.
problem Users' preferences change across different domains (e.g., social media, e-commerce).
method Designs a neural architecture and cross-domain loss function to adaptively balance user preferences.
result ADC model effectively balances the impact of domains with different complexities.
Linear-Core Surrogates combine fast optimization and statistical efficiency in classification and structured prediction.
problem The trade-off between smoothness and margin-based losses in classification and structured prediction.
method Linear-Core (LC) Surrogates, a family of convex loss functions that stitch a linear core to a smooth tail.
result LC Surrogates achieve fast linear consistency rates while maintaining differentiability and strict H-consistency bounds. This work compares and evaluates various sampling methods for neural language models.
problem Lack of systematic comparison and myths about sampling methods.
method Monte Carlo sampling, importance sampling, compensated partial summation, noise contrastive estimation.
result All sampling methods can perform equally well if posterior probabilities are corrected.
Paper introduces efficient methods for estimating cross-partial derivatives and sensitivity indices.
problem Efficiently estimating cross-partial derivatives and sensitivity indices in complex models.
method Using randomized points and constraints, the paper develops estimators with optimal convergence rates and low bias.
result The estimators achieve optimal rates of convergence and do not suffer from the curse of dimensionality.
In this article, we derive concentration inequalities for the cross-validation estimate of the generalization error for empirical risk minimizers. In the general setting, we prove sanity-check bounds in the spirit of \cite{KR99} \textquotedblleft\textit{bounds showing that the worst-case error of this estimate is not m…