Research
On-device research index

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.

168,742 papers · 148 categories

Trend · papers per month

95190284379 · Jun 202019922001200920172026
48 results for reserve price design

QubitSwap improves DEX efficiency by reducing impermanent loss and slippage.

problem Challenges in decentralised exchanges, especially impermanent loss and slippage.
method Hybrid approach integrating external oracle price with internal pool dynamics, parameterized by zz.
result Reduction in impermanent loss and slippage compared to traditional DEX frameworks.

COAD maximizes online auction revenue by quantifying uncertainty without known distributions.

problem Designing incentive-compatible mechanisms for online auctions with unknown bidder values and uncertain future participants.
method COAD uses distribution-free uncertainty quantification techniques and integrates machine learning methods to predict bidder values while ensuring revenue guarantees.
result COAD maximizes revenue in online auctions through bidder-specific reserve prices based on lower confidence bounds of valuations.

New pricing framework allocates costs of operating reserves and transmission.

problem Allocating costs of operating reserves and transmission efficiently.
method Causation-based framework using contingency-constrained scheduling models.
result More comprehensive and efficient cost-reflective market operations.

Optimizes reserve prices for first-price auctions to maximize revenue.

problem Optimizing reserve prices for first-price auctions in display advertising.
method Gradient-based algorithm to adaptively update and optimize reserve prices based on bidder responsiveness to experimental shocks.
result Revenue optimization in first-price auctions can be decomposed into demand and bidding components, and techniques are introduced to reduce variance of each.

A new pricing strategy minimizes regret by controlling strategic buyer behavior.

problem Designing a pricing policy for strategic buyers with limited seller information.
method Phased-structure policy with randomized isolation periods.
result Regret of TT-period O~(T)\widetilde{\mathcal{O}}(\sqrt{T}) against a benchmark policy.

Model estimates foreign exchange reserve compositions of undisclosed central banks.

problem Limited information on central bank reserve compositions hinders analysis.
method Hidden Markov Model relating portfolio valuation to exchange rates.
result China's reserve composition likely matches global average, while Singapore holds fewer US dollars.

Study examines value relevance of oil and gas reserve disclosures in London Stock Exchange.

problem Uncertainty in oil and gas reserves poses accounting challenges for investors.
method Empirical analysis using archival data and multifactor framework.
result Changes in reserves and their components are associated with share returns, but insignificantly due to oil price and longitudinal effects. Quality of disclosures positively impacts share returns.

Researchers solved a model of an exhaustible resource with stochastic discoveries.

problem Optimal exploration of an exhaustible resource with uncertain discoveries.
method Impulse control and Poisson process of new discoveries.
result A frontier of critical levels of proven reserves exists, above which exploration is stopped.

This paper studies a finite-fuel two-dimensional degenerate singular stochastic control problem under regime switching that is motivated by the optimal irreversible extraction problem of an exhaustible commodity. A company extracts a natural resource from a reserve with finite capacity, and sells it in the market at a …

2016-02-22abs ↗pdf ↗

We introduce a new approach to incorporate uncertainty into the decision to invest in a commodity reserve. The investment is an irreversible one-off capital expenditure, after which the investor receives a stream of cashflow from extracting the commodity and selling it on the spot market. The investor is exposed to pri…

2018-03-15abs ↗pdf ↗

Stablecoin system improves resilience to extreme market events.

problem Vulnerability of stablecoins to extreme volatility and adversarial attacks.
method MVF-Composer uses multi-agent simulations to stress-test and down-weight manipulative signals.
result Reduces peak peg deviation by 57% and mean recovery time by 3.1x under adversarial conditions.

We examine the problem of dynamic reserving for risk in multiple currencies under a general coherent risk measure. The reserver requires to hedge risk in a time-consistent manner by trading in baskets of currencies. We show that reserving portfolios in multiple currencies V\mathbf{V} are time-consistent when (and only…

2017-12-04abs ↗pdf ↗

Optimizes insurance pricing to minimize ruin probability under various claim dependencies.

problem Determining optimal insurance premiums in the presence of dependencies between claim occurrences.
method Analyzes both independent and dependent claim processes, considering single and multiple risks.
result Optimal insurance premiums depend on initial reserve and claim dependencies.

Develops a support-aware framework for reserve-policy selection in advertising markets.

problem Log-based reserve-price evaluation risks weak support and subgroup harm.
method Support-aware offline decision framework converting logged evidence into certified policies.
result Preserves the best gate-passing policy while eliminating only policies with certified regret.

We consider infinite dimensional optimization problems motivated by the financial model called Arbitrage Pricing Theory. Using probabilistic and functional analytic tools, we provide a dual characterization of the super-replication cost. Then, we show the existence of optimal strategies for investors maximizing their e…

2019-04-25abs ↗pdf ↗

This paper optimizes brokerage contracts for multiple clients trading a single asset.

problem Optimizing brokerage contracts for multiple clients trading a single asset.
method Endogenously determines clients' reservation values and strategically chooses clients. Characterizes optimal portfolios computationally.
result Characterizes optimal portfolios of clients and their profits, showing dependence on price impact coefficients.

Framework insures AI actions with reserve capital, preventing loss.

problem Ensuring safety and accountability for AI actions with varying side effects.
method Developed Actuarial Action Interface (AAI) and Authority Frontier to price and gate AI actions.
result Found common refusal and release patterns across domains, with varying required reserve capital.

Traditional non-life reserving models largely neglect the vast amount of information collected over the lifetime of a claim. This information includes covariates describing the policy, claim cause as well as the detailed history collected during a claim's development over time. We present the hierarchical reserving mod…

2019-10-28abs ↗pdf ↗

A new RL approach optimizes reserve prices in multi-phase auctions, reducing revenue regret.

problem Optimizing reserve prices in multi-phase second-price auctions with noisy and potentially untruthful bidders.
method Combines RL techniques with buffer periods, a novel algorithm, and LSVI-UCB extension.
result Achieves optimal revenue regret under known and unknown noise conditions.

Reinforcement learning improves insurance claims reserving by learning from all claim trajectories.

problem Traditional reserving models learn only from settled claims, missing valuable data from ongoing claims.
method Formulated as a Markov decision process, uses reinforcement learning to update OCL estimates sequentially.
result Soft Actor-Critic implementation achieves competitive claim-level accuracy and strong aggregate performance.

This paper improves bidding price prediction for ancillary services markets, boosting revenues.

problem Volatility in renewable energy sources affects grid stability and revenue optimization.
method Machine learning models (SVR, DT, k-NN) and offset adjustment for pay-as-bid markets.
result The proposed approach increases potential revenues by 27.43% to 37.31% compared to baseline models.

The paper develops a new framework for pricing and hedging liquidity in crypto markets.

problem Arbitrage and risk management in crypto market making.
method Developed a new mathematical framework using a coordinate system defined by price and intrinsic liquidity.
result Established a linear dependence of asset reserves and value functions on intrinsic liquidity, facilitating arbitrage-free pricing and delta hedging.

In this paper, we study the stochastic version of the one-sided full information bandit problem, where we have KK arms [K]={1,2,,K}[K] = \{1, 2, \ldots, K\}, and playing arm ii would gain reward from an unknown distribution for arm ii while obtaining reward feedback for all arms jij \ge i. One-sided full information bandit ca…

2019-06-20abs ↗pdf ↗

This paper addresses recalibration issues in hedging callable assets, proposing a new risk-adjusted approach.

problem The mismatch between dynamic hedging theory and practice due to daily recalibration.
method Extends HVA model risk approach to callable assets, focusing on recalibration and model risks.
result Model risk reserves adjusted for exercise decisions may significantly exceed basic valuation differences.

Overprocuring reserves can improve network efficiency by using excess reserves for congestion management.

problem Optimizing energy and reserve allocation between zones to minimize costs and ensure deliverability.
method Developed allocation models for co-allocating traded energy and reserve products, considering both deterministic and stochastic flows.
result Excess reserve supplies can be used for congestion management, leading to additional network benefits.

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.

A novel probabilistic approach forecasts imbalance prices in Belgium.

problem Forecasting imbalance prices in short-term energy markets.
method Two-step approach: compute net regulation volume state transition probabilities, then infer imbalance prices.
result The probabilistic approach outperforms deterministic and Gaussian Process models.

RL-CVaR model improves insurance reserving under economic stress.

problem Managing insurance reserve setting under claim development uncertainty and macroeconomic stress.
method Reinforcement Learning (PPO) with CVaR constraints, trained under regime-aware curriculum.
result RL-CVaR policy reduces solvency violations and tail-risk compared to classical methods.

Paper proposes a new reserving model using machine learning techniques.

problem Managing uncertainties in premium sufficiency and reserves for future claims.
method Stacked model combining Gradient Boosting, Random Forest, Artificial Neural Networks, and log-normal approach.
result The proposed model improves traditional reserving techniques, leading to more accurate reserving risk assessment.