Agent's optimization in unique-contract principal-agent problem with adverse selection is recast as stochastic target problem, enabling principal's objective as stochastic optimal control with partial information and state constraints.
Eymen Errais, Kay Giesecke, and Lisa R
6 Pith papers cite this work. Polarity classification is still indexing.
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A low-rank dynamic factor model with AR(1) latent states and binomial observations, when aggregated over time, generates horizon-dependent posterior-implied copulas that reproduce annual eigenvalue amplification on S&P sector default data and improve some forecast scores.
QRAFTI is a multi-agent framework using tool-calling and reflection-based planning to emulate quant research tasks like factor replication and signal testing on financial data.
DeFi vault risk is decomposed into three levels with six on-chain mechanical features generating new loss channels, yielding five aggregated credit risk metrics and an on-chain estimation architecture.
Value of information to informed traders equals price-order flow covariance and totals 0.04% of market cap, much less than active management fees.
Causal PDE-Control Models combine causal drivers with PDE control and filtering to deliver interpretable dynamic portfolio rules that outperform benchmarks in Sharpe ratio and turnover on U.S. equity data.
citing papers explorer
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Principal-agent problems with adverse selection: A stochastic target problem formulation
Agent's optimization in unique-contract principal-agent problem with adverse selection is recast as stochastic target problem, enabling principal's objective as stochastic optimal control with partial information and state constraints.
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Temporal Coarse-Graining of Multi-Sector Default Count Data Generates Posterior-Implied Copulas
A low-rank dynamic factor model with AR(1) latent states and binomial observations, when aggregated over time, generates horizon-dependent posterior-implied copulas that reproduce annual eigenvalue amplification on S&P sector default data and improve some forecast scores.
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QRAFTI: An Agentic Framework for Empirical Research in Quantitative Finance
QRAFTI is a multi-agent framework using tool-calling and reflection-based planning to emulate quant research tasks like factor replication and signal testing on financial data.
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Vault as a credit instrument
DeFi vault risk is decomposed into three levels with six on-chain mechanical features generating new loss channels, yielding five aggregated credit risk metrics and an on-chain estimation architecture.
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The Value of Information: A Puzzle
Value of information to informed traders equals price-order flow covariance and totals 0.04% of market cap, much less than active management fees.
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Causal PDE-Control Models for Dynamic Portfolio Optimization with Latent Drivers
Causal PDE-Control Models combine causal drivers with PDE control and filtering to deliver interpretable dynamic portfolio rules that outperform benchmarks in Sharpe ratio and turnover on U.S. equity data.