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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,291 papers · 148 categories

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6481,2971,9452,593 · Jun 202019922001200920182026
48 results for costs of carrying

Study reveals a hidden cost in derivatives markets through option-implied discount factors.

problem The hidden cost in derivatives markets, not visible in price space.
method Minute-level NBBO data on options, reduced-form specification linking carry gap to implementation risk, trading frictions, and financial conditions.
result An annualized carry gap exists, linked to implementation risk and financial conditions.

Develops a framework to assess infrastructure reliability under natural and malicious events.

problem Assessing reliability and costs of infrastructure under various hazards.
method Coupling mechanical reliability analyses with economical reliability analyses using probabilistic considerations.
result Indicators of probable cost of failure for infrastructure, aiding safety investments.

Bayesian optimization with cost-awareness using Gittins index.

problem Optimizing unknown functions with limited data evaluations and costs.
method Developed a connection between cost-aware Bayesian optimization and the Pandora's Box problem, using the Gittins index as an acquisition function.
result The Gittins index-based acquisition function performs well in cost-aware Bayesian optimization, especially in high dimensions.

Develops optimal currency hedging strategy for fund managers considering liquidity risk.

problem Choosing optimal foreign exchange (FX) hedge tenors to maximize carry returns within liquidity constraints.
method Time-dispersing total hedge value into future time buckets, maximizing FX carry benefit while adhering to liquidity risk metric (CFaR).
result Hedging strategy operates within liquidity budget, demonstrating practical insights for fund managers.

The paper models battery valuation in intraday electricity markets, incorporating liquidity costs.

problem Valuing batteries in intraday electricity markets considering liquidity costs.
method Stochastic model for mid-prices combined with a deterministic model for liquidity costs, using dynamic programming for optimization.
result Liquidity costs significantly impact battery valuation, especially with multiple batteries.

On a constraint manifold we give an explicit formula for the Hessian matrix of a cost function that involves the Hessian matrix of a prolonged function and the Hessian matrices of the constraint functions. We give an explicit formula for the case of the orthogonal group O(n){\bf O}(n) by using only Euclidean coordinates …

2014-03-17abs ↗pdf ↗

The mathematical problem concerning intrinsic storage optimisation is formulated and solved by means of variational analysis. The solution, though obtained in implicit form, still sheds light on many important features of the optimal exercise strategy. It is shown how the solution depends on different constraint types …

2015-06-22abs ↗pdf ↗

Credit (CVA), Debit (DVA) and Funding Valuation Adjustments (FVA) are now familiar valuation adjustments made to the value of a portfolio of derivatives to account for credit risks and funding costs. However, recent changes in the regulatory regime and the increases in regulatory capital requirements has led many banks…

2014-05-02abs ↗pdf ↗

Study characterizes PM2.5 dynamics in Bujumbura using low-cost sensors.

problem Characterize spatio-temporal variability of PM2.5 in Bujumbura.
method Used low-cost sensors to collect data from August 2022 to August 2023. Analyzed hourly, daily, and seasonal PM2.5 concentrations using RNN with LSTM.
result Hourly and annual PM2.5 concentrations in Bujumbura exceed WHO standards.

This paper optimizes international portfolios considering currency overlay costs and constraints.

problem Limited risk diversification in multi-currency portfolios due to currency concentration.
method Proposes a model integrating currency overlay and asset allocation optimization, accounting for costs and constraints.
result Inclusion of costs significantly alters optimal portfolio decisions and risk-return trade-offs.

Backprop-Q extends standard backpropagation for stochastic computation graphs.

problem Applying standard backpropagation to stochastic computation graphs is challenging.
method Construct Q-functions for each stochastic node and use them to train the SCG with standard backpropagation.
result Generalized backpropagation for stochastic computation graphs is feasible and extends learning signals beyond gradients.

New algorithms optimize time series classification speed and accuracy.

problem Efficiently classify time series data quickly without sacrificing accuracy.
method Optimization criterion balancing misclassification and delay costs, derived non-myopic algorithms.
result Supervised-based algorithms outperform unsupervised-based ones in real data sets.

New algorithms improve prediction with expert advice under local differential privacy.

problem Predicting expert advice with privacy constraints.
method Design of two new algorithms: RW-AdaBatch and RW-Meta, leveraging limited-switching behavior and random walks.
result RW-Meta outperforms classical and central DP algorithms by 1.5-3x on predicting hospital COVID patient densities.

Paper finds unique viscosity solution to complex control problems.

problem Complex stochastic control problems with singular terminal state constraints.
method Establishes existence of unique nonnegative continuous viscosity solution using novel comparison principle.
result Unique viscosity solution to HJB equation for linear-quadratic control problems.

Model compression improves dynamic forecasting ensembles while reducing computational costs.

problem High computational costs and lack of transparency in dynamic forecasting ensembles.
method Model compression applied to dynamic forecasting ensembles of various types of models.
result Compressed models achieve comparable predictive performance and significant computational savings.

Proposes a robust framework for recommendation with implicit feedback considering cost-sensitive learning.

problem Recommendation with implicit feedback often assumes a strict low-rank structure and ignores class imbalance.
method Decomposes U-I matrix into low-rank and sparse components, incorporating cost-sensitive learning.
result Cost-sensitive error is upper bounded by O(αmn)O(\fracα{\sqrt{mn}}), demonstrating improved performance.

The paper tackles dynamic collateral control for spot-perpetual basis trading in decentralized finance.

problem Dynamic control of collateral in spot-perpetual basis trading in decentralized finance.
method Solves a static control problem and derives an asymmetric dynamic extension, validated with live execution.
result The dynamic control approach provides a more robust operating benchmark and shows significant rebalancing effects.

Proposes a robust risk measure to minimize capital errors.

problem Minimizing capital determination errors due to risk overestimation and underestimation.
method Uses supremum over probability measures to minimize overestimation and underestimation costs.
result Guarantees the existence of a solution and explores properties of minimizer and minimum as risk and deviation measures.

While statistical learning methods have proved powerful tools for predictive modeling, the black-box nature of the models they produce can severely limit their interpretability and the ability to conduct formal inference. However, the natural structure of ensemble learners like bagged trees and random forests has been …

2014-06-07abs ↗pdf ↗

Optimal control of reserve assets for stablecoins to maintain peg stability.

problem Balancing immediate liquidity and yield on reserve assets for stablecoin peg maintenance.
method Developed a stochastic model predictive control framework with moment closure for event intensities, incorporating a soft-thresholding structure for rebalancing.
result Optimal policy shifts predictably toward cash as expected outflows intensify or windows lengthen, preserving most bill carry in calm markets and quickly building cash during stress.

The generative aspect model is an extension of the multinomial model for text that allows word probabilities to vary stochastically across documents. Previous results with aspect models have been promising, but hindered by the computational difficulty of carrying out inference and learning. This paper demonstrates that…

2012-12-12abs ↗pdf ↗

New batched Langevin Thompson Sampling reduces communication costs for sequential decision making.

problem Efficiently learning unknown reward distributions and transition dynamics in batched settings.
method Langevin Thompson Sampling with logarithmic communication costs.
result Order-optimal regret guarantees for stochastic MABs and RL.

This work tackles robust optimization with multiple objectives for structural design.

problem Optimizing structural design under uncertainty for multiple conflicting objectives.
method Adaptive surrogate modeling using Kriging to approximate computationally expensive models.
result The importance of replacing the heating system in building renovations is highlighted.

Detection of rare variants by resequencing is important for the identification of individuals carrying disease variants. Rapid sequencing by new technologies enables low-cost resequencing of target regions, although it is still prohibitive to test more than a few individuals. In order to improve cost trade-offs, it has…

2009-09-02abs ↗pdf ↗

The paper analyzes trading strategies using exponential moving averages.

problem Investigating trading strategies based on exponential moving averages.
method Logarithmic utility maximization and long-term growth rate maximization problems with closed-form solutions for specific drift models.
result Closed-form solutions for optimal ExpMA strategies under specific drift models.

Study finds unsupervised imputation before cross-validation can reduce computational costs without significantly degrading model performance.

problem High computational costs in pipeline modeling algorithms with imputation steps.
method Empirical assessment of unsupervised imputation before vs during cross-validation.
result Reduced variance of imputation before cross-validation leads to lower overall root mean squared error.

The paper identifies inconsistencies in post-crisis derivative pricing methods and derives no-arbitrage expressions.

problem Inconsistencies in post-crisis derivative pricing methods, particularly regarding cost components to a risk-free money account.
method Derives no-arbitrage expressions for default-risky derivative contracts with and without collateral.
result Avoids inconsistencies in derivative pricing methods by deriving no-arbitrage expressions.

Paper compares ETF and futures carry rates in segmented Bitcoin markets.

problem Limitations in cross-margining between spot Bitcoin and CME futures.
method Estimates carry rates from IBIT options and CME futures, uses put-call parity and daily ETF holdings.
result Mean and median wedge in carry rates is 2.58 and 2.52 percent, respectively.