8 papers in the archive (2023–2026), average empirical rigor 5.4/10, average math complexity 7.8/10. Quadrant mix: Holy Grail ×4, Lab Rats ×4.
Works mostly in Risk Management & Tail Risk, High-Frequency Trading & Optimal Execution, Crypto Markets & DeFi, Volatility Modeling & Forecasting.
Frequent co-authors: Xin-Yu Wang (1), Tan Chin Hong (1), Jow-Ran Chang (1), Hui Hsiang Chen (1), Yi-Shan Wong (1), Rong Gan (1).
Scores are our LLM-assisted heuristics (methodology), not peer review or citation counts. Author pages are generated from paper metadata; name variants may split one person across pages. arXiv author page →
Ordered by a blend of empirical rigor (60%) and math complexity (40%).
A common belief is that leveraged ETFs (LETFs) suffer long-term performance decay due to \emph{“volatility drag”}. We show that this view is incomplete: LETF performance depends fundamentally on return autocorrelation and return dynamics. In markets with independent returns, LETFs exhibit positive e
The aim of this paper is to investigate the impact of rebalancing frequency and transaction costs on the log-optimal portfolio, which is a portfolio that maximizes the expected logarithmic growth rate of an investor’s wealth. We prove that the frequency-dependent log-optimal portfolio problem with c
The Double Linear Policy (DLP) framework guarantees a Robust Positive Expectation (RPE) under optimized constant-weight designs or admissible prespecified time-varying policies. However, the sequential optimization of these time-varying weights remains an open challenge. To address this gap, we prop
This paper extends the existing drawdown modulation control policy to include a novel restart mechanism for trading. It is known that the drawdown modulation policy guarantees the maximum percentage drawdown no larger than a prespecified drawdown limit for all time with probability one. However, whe
This paper investigates the robust optimal control of sampled-data stochastic systems with multiplicative noise and distributional ambiguity. We consider a class of discrete-time optimal control problems where the controller \emph{jointly} selects a feedback policy and a sampling period to maximize
This paper investigates cost-sensitive online window size selection for portfolio management under changing market conditions. Specifically, we propose a two-level framework that constructs portfolios using candidate window sizes and dynamically aggregates them through online learning. By treating c
Noisy data are often viewed as a challenge for decision-making. This paper studies a distributionally robust optimization (DRO) that shows how such noise can be systematically incorporated. Rather than applying DRO to the noisy empirical distribution, we construct ambiguity sets over the \emph{laten
In this paper, we extend the existing double linear policy by incorporating time-varying weights instead of constant weights and study a certain robustness property, called robust positive expectation (RPE), in a discrete-time setting. We prove that the RPE property holds by employing a novel elemen