The integration of algorithmic trading and reinforcement learning, known as AI-powered trading, has significantly impacted capital markets. This study utilizes a model of imperfect competition among informed speculators with asymmetric information to explore the implications of AI-powered...

The integration of algorithmic trading with reinforcement learning, termed AI-powered trading, is transforming financial markets. Alongside the benefits, it raises concerns for collusion. This study first develops a model to explore the possibility of collusion among informed speculators in a...

This paper studies a class of exponential family models whose canonical parameters are specified as linear functionals of an unknown infinite-dimensional slope function. The optimal minimax rates of convergence for slope function estimation are established. The estimators that achieve the...

Some existing nonparametric two-sample tests for equality of multivariate distributions perform unsatisfactorily when the two sample sizes are unbalanced. In particular, the power of these tests tends to diminish with increasingly unbalanced sample sizes. In this article, we propose a new...

This thesis consists of three essays that theoretically and empirically investigate the asset pricing and macroeconomic implications of uncertainty shocks, propose new measures for model robustness, explain the joint dynamics on equity excess returns and real exchange rates

In the loan markets for distressed corporate borrowers, a few specialized lenders finance a large fraction of loans. Ultra-high yield spreads prevail even after removing the credit- and liquidity-risk component. Borrowers are in desperate need of financing but face limited funding options, while...