How does the predictability of future noisy flows impact asset prices? We answer this question by developing a dynamic multi-asset price impact model. The model setup is general---both flows and fundamental returns can be correlated for the cross-section of assets, and flows can exhibit a...

We propose gradient-based simulation-optimization algorithms to optimize systems that have complicated stochastic structure. The presence of complicated stochastic structure, such as the involvement of infinite-dimensional continuous-time stochastic processes, may cause the exact simulation of...

Simulation is often used to evaluate and compare performances of stochastic systems, where the underlying stochastic models are estimated from real-world input data. Collecting more input data can derive closer-to-reality stochastic models while generating more simulation replications can reduce...

We study the problem of simulating a class of nonstationary spatio-temporal Poisson processes. The Poisson intensity function is non-stationary and piecewise linear in both the time dimension and the spatial location dimensions. We propose an exact simulation algorithm based on the inversion...

We design new policies that ensure both worst-case optimality for expected regret and light-tailed risk for regret distribution in the stochastic multi-armed bandit problem. Recently, \cite{fan2021fragility} showed that information-theoretically optimized bandit algorithms suffer from some...

We propose a new approach to modeling informed order flows. Our approach generalizes arbitrage pricing to incorporate both information and demand effects, giving rise to a new quadratic factor model of price impacts. Our approach offers two fresh insights into the economics of informed trading....