The paper analyzes real-time methods to detect rapidly varying liquidity in markets.
problem Increased trade execution price uncertainty due to rapid price variations by high-frequency traders.
method A four-state Markov switching model to identify volatile liquidity states.
result The model can generate a signal to delay orders, reducing price volatility for market participants.
Optimizes asset allocation with illiquid assets using MPC.
problem Strategic asset allocation with illiquid alternative asset classes.
method Formulates illiquid dynamics as a random linear system and proposes a convex optimization based MPC policy.
result Performance close to a fully liquid scenario, despite time delay and uncertainty.
This paper studies the optimal timing to liquidate credit derivatives in a general intensity-based credit risk model under stochastic interest rate. We incorporate the potential price discrepancy between the market and investors, which is characterized by risk-neutral valuation under different default risk premia speci…
The paper optimizes trading strategies for assets modeled by a randomized Brownian bridge.
problem Optimizing trading strategies for assets with uninformative noise and unknown terminal prices.
method Modeling asset price evolution with an exponential randomized Brownian bridge and solving for optimal trading strategies numerically.
result Disconnected continuation/exercise regions appear under certain prior distributions.
This paper studies the risk-adjusted optimal timing to liquidate an option at the prevailing market price. In addition to maximizing the expected discounted return from option sale, we incorporate a path-dependent risk penalty based on shortfall or quadratic variation of the option price up to the liquidation time. We …
We propose a model of inter-bank lending and borrowing which takes into account clearing debt obligations. The evolution of log-monetary reserves of N N N banks is described by coupled diffusions driven by controls with delay in their drifts. Banks are minimizing their finite-horizon objective functions which take into a…
The study examines when large trades are considered news or liquidity shocks in a market model.
problem Understanding when large trades are news or liquidity shocks in a market model.
method A sequential competitive limit order book model with asymmetric information and Student-t tails for liquidity demand.
result Heavy-tailed liquidity demand flattens and concavifies price impact, delaying price discovery.
New strategy improves liquidity takers' performance in markets with latency.
problem Latency affects liquidity takers' ability to execute limit orders effectively.
method Modelled LOB and MLOs as a marked point process, used variational analysis and FBSDEs to find optimal price limits.
result Optimal trading strategy improves marksmanship in markets with latency.
New method detects market liquidity changes using order book data.
problem Detecting changes in market liquidity.
method Marked Hawkes processes and minimax quickest detection problem for doubly-stochastic Poisson process.
result Optimal stopping rule for detecting intensity changes in market liquidity.
Research proposes a decentralized invoice discounting system using Kelly criterion.
problem Persistent funding gap for SMEs and inefficiencies in traditional factoring.
method Automated Market Maker (AMM) with Kelly criterion for premium calculation.
result Resilient decentralized system with optimal profit distribution policies.
A new DRL model optimizes hedging with market impact for low-liquidity stocks.
problem Optimizing hedging strategies for stocks with limited liquidity.
method Integrates Deep Reinforcement Learning with realistic market impact features.
result Optimal hedging policies learned from DRL model perform better in low-liquidity scenarios.
New method constructs multilayer networks from financial data, capturing dependencies across different risk factors.
problem Difficult construction of multilayer networks, neglecting time delays and interdependencies.
method Tucker tensor autoregression for direct multilayer network construction.
result Captures within and between connections, identifies strong interconnections between volumes and prices layers.
Study finds discrepancies in open interest reporting for Bitcoin perpetual swaps.
problem Misquoted open interest in perpetual swaps leads to liquidity and solvency concerns.
method Analyzed tick-by-tick data from seven exchanges to identify discrepancies.
result Open interest reported by exchanges varies widely, some implausible.
Axient handles debt-free finality for leveraged binary event markets.
problem Managing debt and finality in leveraged event positions with uncertain outcomes.
method Axient separates leverage maturity from claim maturity, using a protocol to select smallest sale covering debt.
result Proves robust ex-ante debt clearing and debt-free-finality invariants, maximal residual spot exposure, and payout-vector invariance.
Develops a stochastic approach to financial market delays.
problem Modeling delays in financial markets with multiple assets.
method Introduces a general stochastic framework for information and order execution delays.
result Delayed markets maintain fundamental asset pricing theorems and no asymptotic free lunch condition.
Paper tackles action delays in reinforcement learning, proposing a delay-aware framework.
problem Action delays degrade reinforcement learning performance in real-world systems.
method Formal definition of delay-aware MDP, transformation into standard MDP with augmented states, delay-aware model-based reinforcement learning framework.
result Proposed framework is more efficient in training and transferable between systems with various delay durations.
New algorithm tackles delayed feedback in Lipschitz bandits with sublinear regret.
problem Delayed feedback in Lipschitz bandits.
method Design of algorithms for bounded and unbounded stochastic delays.
result Sublinear regret guarantees for both bounded and unbounded delays.
New algorithms ensure fair selection in combinatorial semi-bandit with unrestricted delays.
problem Fair selection in stochastic combinatorial semi-bandit with delayed feedback.
method Introduced merit-based fairness constraints and new bandit algorithms for reward and fairness.
result Achieved sublinear expected reward and fairness regrets with dependence on delay distribution quantiles.
Banker-OMD improves online learning with delayed feedback.
problem Handling delayed feedback in online learning.
method Generalized Online Mirror Descent (OMD) framework.
result Achieves nearly-optimal performance in three bandit scenarios.
New algorithm handles delayed feedback robustly, reducing regret without knowing delay bounds.
problem Bandits with variably delayed feedback, especially excessive delays.
method Implicit exploration scheme, adaptive skipping, drifted regret control.
result Can tolerate arbitrary excessive delays up to order T, reducing regret.
Proposes a nonparametric model for predicting conversion rates with delayed feedback.
problem Predicting conversion rates with time delays and unknown distribution.
method Nonparametric delayed feedback model without assuming a specific distribution.
result The proposed model outperforms existing methods in conversion rate prediction.
Gradient descent with delayed updates converges faster with noise, even when delays are significant.
problem Analyzing convergence of gradient descent with delayed gradients and stochastic noise.
method Novel technique using generating functions for convergence analysis.
result Convergence bounds show that stochastic noise mitigates the negative effects of delays, improving performance.
New algorithm for multiarmed bandits with variable, unbounded delays achieves similar regret bounds.
problem Variable, unbounded delays in multiarmed bandits.
method Introduces a new algorithm that skips rounds with excessively large delays and uses a doubling scheme.
result Achieves the same regret bound as Exp3 with variable, unbounded delays.
Paper tackles delays in multi-agent reinforcement learning, improving performance.
problem Challenges in reinforcement learning due to delays in real-world systems.
method Proposes a novel framework for multi-agent reinforcement learning with delays, using Delay-Aware Markov Games and centralized-decentralized training.
result Demonstrates significant improvement in performance with delay-aware multi-agent reinforcement learning.
Derives a Feynman-Kac formula for a fixed delay CIR model.
problem Modeling financial processes with fixed delay.
method Proves existence and uniqueness of a strong solution for a specific SDDE.
result Derives a Feynman-Kac type formula leading to an affine bond pricing formula.
New bandit problem with delayed, aggregated feedback analyzed.
problem Stochastic K K K -armed bandit problem with delayed, aggregated anonymous feedback. method Developed algorithm matching worst case regret of non-anonymous problem.
result Regret increase can be maintained in the harder delayed, aggregated anonymous feedback setting.
Study on synchronization in financial markets with time delays.
problem Understanding market dynamics and synchronization in financial systems with time delays.
method Examined a system of coupled non-linear delay-differential equations, linearized for small delays, and analyzed collective dynamics using bifurcation diagrams and numerical solutions.
result Demonstrated that limit cycles can be maintained in coupled N-asset models with appropriate parameterization, leading to market synchronization.
BayTiDe discovers time-delayed differential equations from noisy data.
problem Discovering time-delayed differential equations from data with large delays and noise.
method Bayesian inference with a sparsity-promoting prior.
result BayTiDe accurately identifies time-delayed differential equations with accuracy proportional to data resolution.
New algorithm tackles stochastic bandits with varying arm-dependent delays.
problem Applying existing algorithms to stochastic delayed bandit settings is restricted by strong assumptions on delay distributions.
method Proposes a simple UCB-based algorithm called PatientBandits that weakens assumptions on delay distributions.
result Provides bounds on regret and performance lower bounds for the PatientBandits algorithm.
Model analyzes how delayed information impacts option pricing.
problem Effects of delayed information on option pricing.
method Binomial model, closed form formula for convex contingent claims, convergence analysis.
result Delayed information exaggerates the volatility smile.
TSMB handles time delays in multivariate time series data.
problem Varying time delays in multivariate time series data complicate predictions.
method Time Series Model Bootstrap (TSMB) framework for nonparametric time delay estimation.
result TSMB improves model performance in dynamic data environments.
Delayed-RNN approximates stacked and bidirectional RNNs.
problem Improving RNN expressiveness and representational capacity.
method Weight-constrained delayed-RNN, equivalent to stacked-RNNs, with partial acausality.
result Delayed-RNN can approximate stacked and bidirectional RNNs, outperforming them in some tasks.
New algorithm reduces regret in delayed feedback generalised linear bandits.
problem Regret in delayed feedback generalised linear bandits.
method Adaptation of optimistic algorithm to delayed feedback.
result Achieves a regret bound independent of the horizon's delay penalty.
Adapts Exp3 to adversarial bandits with delays and data.
problem Adversarial multi-armed bandits with delayed feedback.
method Tuned Exp3 variants with step-size adaptation and implicit exploration.
result Optimal regret bounds of log ( K ) ( T K + D ) \sqrt{\log(K)(TK + D)} log ( K ) ( T K + D ) with high probability. Capacity-Constrained Online Convex Optimization with Delayed Feedback
problem Online learning with delayed feedback under a hard capacity constraint
method Reduction to a delayed and weighted OCO problem using a scheduler
result First regret guarantees for capacity-constrained OCO under convex and strongly convex losses
New algorithm tackles non-stationary delayed feedback in recommender systems.
problem Challenges in learning from delayed feedback in non-stationary environments.
method Developed a UCRL-based algorithm for non-stationary, delayed bandits with intermediate observations.
result Sublinear regret guarantees for the proposed algorithm in non-stationary delayed environments.
PCTS optimizes noisy, delayed, multi-fidelity feedbacks in black-box optimization.
problem Optimizing unknown functions with noisy, delayed, and multi-fidelity feedbacks.
method ProCrastinated Tree Search (PCTS) with DUCB1 and DUCBV algorithms.
result PCTS achieves better regret bounds for delayed, noisy, and multi-fidelity feedbacks.
Study online learning with delays and capacity constraints, achieving optimal regret bounds.
problem Online learning with delays and capacity constraints.
method Novel scheduling and preemptive techniques, matching upper and lower bounds.
result Achieves optimal regret bounds across all capacity levels.
Study market delay effects on contingent claims pricing.
problem Delayed market information impacts contingent claims pricing.
method Analyzes Black-Scholes and binomial models with delay.
result Scaling limit of super-replication prices equals G-expectation.
New Async-SGD and Async-SGDI methods converge for non-convex problems with unbounded delays.
problem Improving convergence of asynchronous stochastic gradient descent with unbounded delays in non-convex learning.
method Developed Async-SGD and Async-SGDI methods for non-convex optimization with unbounded gradient delays, proving convergence rates and establishing a unifying sufficient condition.
result Proved o ( 1 / k ) o(1/\sqrt{k}) o ( 1/ k ) convergence rate for Async-SGD and o ( 1 / k ) o(1/k) o ( 1/ k ) for Async-SGDI. Online learning with delayed feedback has received increasing attention recently due to its several applications in distributed, web-based learning problems. In this paper we provide a systematic study of the topic, and analyze the effect of delay on the regret of online learning algorithms. Somewhat surprisingly, it t…
Federated learning technique improves convergence speed with communication delays.
problem Communication delays between edge nodes and aggregator in federated learning.
method Developed FedDelAvg, a technique that generalizes federated averaging to incorporate a weighting between current local model and delayed global model.
result FedDelAvg achieves a significant improvement in convergence speed, especially when optimizing the weighting scheme to account for delays.
Optimizes liquidity provision intervals for profitable AMM participation.
problem Financial losses from poor liquidity provision intervals and reallocation costs.
method Developed a tractable stochastic optimization problem.
result Computes optimal liquidity provision intervals for profitable liquidity concentration.
Study shows how crypto asset liquidity is affected by wash trading and proposes treatment to reduce liquidity diffusion.
problem Understanding and reducing crypto asset wash trading to improve liquidity.
method Proposed a two-component model for liquidity (jump and diffusion) and demonstrated the effectiveness of autoregressive models.
result Treatment on wash trading significantly reduces liquidity diffusion but not liquidity jump.
This paper analyzes async-parallel algorithms with unbounded delays, proving convergence and providing a stepsize formula.
problem Problems with asynchrony in parallel iterations and unbounded delays.
method Probabilistic analysis of async-parallel methods with large unbounded delays, providing an explicit stepsize formula.
result An explicit formula for stepsize that guarantees convergence under large unbounded delays.
Study uses randomized allocation for delayed rewards in multi-armed bandits.
problem Delayed rewards in contextual multi-armed bandits.
method Randomized allocation with nonparametric estimation.
result Strongly consistent strategy for delayed rewards.
New algorithms handle unknown delays in online learning problems.
problem Handling unknown delays in bandit online learning.
method DEXP3 for MAB and DBGD for BCO, with a unified analysis.
result Regret bounds of O ( K d ˉ ( T + D ) ) {\cal O}\big( \sqrt{K\bar{d}(T+D)} \big) O ( K d ˉ ( T + D ) ) for DEXP3 and O ( K ( T + D ) ) {\cal O}\big( \sqrt{K(T+D)} \big) O ( K ( T + D ) ) for DBGD. The study introduces new liquidity measures and models for assets with extreme liquidity.
problem Modeling assets with extreme liquidity, especially in crypto markets.
method Developed innovative liquidity premium measures, liquidity-adjusted return and volatility models, and used ARMA-GARCH/EGARCH models.
result The liquidity-adjusted models outperform traditional models in predicting asset performance at extreme liquidity.