19 citations · 27 across the 13 of their papers we have counts for
15 papers
Risk-Neutral Generative Networks
Zhonghao Xian, Xing Yan, Cheuk Hang Leung +1
We present a generative approach to price options and extract risk-neutral densities from the market. Specifically, we model the underlying log-returns on the time-to-maturity cont…
Parsimonious Generative Machine Learning for Non-Gaussian Tail Modeling
Xing Yan, Yue Zhao, Qi Wu +1
The presence of non-Gaussian tails is a prevalent characteristic in many financial modeling scenarios, necessitating the use of complex non-Gaussian distributions such as the gener…
Invariant Random Forest: Tree-Based Model Solution for OOD Generalization
Yufan Liao, Qi Wu, Xing Yan
Out-Of-Distribution (OOD) generalization is an essential topic in machine learning. However, recent research is only focusing on the corresponding methods for neural networks. This…
The Causal Impact of Credit Lines on Spending Distributions
Yijun Li, Cheuk Hang Leung, Xiangqian Sun +6
Consumer credit services offered by e-commerce platforms provide customers with convenient loan access during shopping and have the potential to stimulate sales. To understand the…
Dynamic CVaR Portfolio Construction with Attention-Powered Generative Factor Learning
Chuting Sun, Qi Wu, Xing Yan
The dynamic portfolio construction problem requires dynamic modeling of the joint distribution of multivariate stock returns. To achieve this, we propose a dynamic generative facto…
Improving Uncertainty Quantification of Variance Networks by Tree-Structured Learning
Wenxuan Ma, Xing Yan, Kun Zhang
To improve the uncertainty quantification of variance networks, we propose a novel tree-structured local neural network model that partitions the feature space into multiple region…
Ensemble Multi-Quantiles: Adaptively Flexible Distribution Prediction for Uncertainty Quantification
Xing Yan, Yonghua Su, Wenxuan Ma
We propose a novel, succinct, and effective approach for distribution prediction to quantify uncertainty in machine learning. It incorporates adaptively flexible distribution predi…
Decorr: Environment Partitioning for Invariant Learning and OOD Generalization
Yufan Liao, Qi Wu, Zhaodi Wu +1
Invariant learning methods, aimed at identifying a consistent predictor across multiple environments, are gaining prominence in out-of-distribution (OOD) generalization. Yet, when…
Robust Causal Learning for the Estimation of Average Treatment Effects
Yiyan Huang, Cheuk Hang Leung, Xing Yan +5
Many practical decision-making problems in economics and healthcare seek to estimate the average treatment effect (ATE) from observational data. The Double/Debiased Machine Learnin…
Risk and return prediction for pricing portfolios of non-performing consumer credit
Siyi Wang, Xing Yan, Bangqi Zheng +4
We design a system for risk-analyzing and pricing portfolios of non-performing consumer credit loans. The rapid development of credit lending business for consumers heightens the n…
Robust Orthogonal Machine Learning of Treatment Effects
Yiyan Huang, Cheuk Hang Leung, Qi Wu +1
Causal learning is the key to obtaining stable predictions and answering \textit{what if} problems in decision-makings. In causal learning, it is central to seek methods to estimat…
The Causal Learning of Retail Delinquency
Yiyan Huang, Cheuk Hang Leung, Xing Yan +4
This paper focuses on the expected difference in borrower's repayment when there is a change in the lender's credit decisions. Classical estimators overlook the confounding effects…
Generative Learning of Heterogeneous Tail Dependence
Xiangqian Sun, Xing Yan, Qi Wu
We propose a multivariate generative model to capture the complex dependence structure often encountered in business and financial data. Our model features heterogeneous and asymme…
Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning
Xing Yan, Weizhong Zhang, Lin Ma +2
We propose a parsimonious quantile regression framework to learn the dynamic tail behaviors of financial asset returns. Our model captures well both the time-varying characteristic…
Cross-sectional Learning of Extremal Dependence among Financial Assets
Xing Yan, Qi Wu, Wen Zhang
We propose a novel probabilistic model to facilitate the learning of multivariate tail dependence of multiple financial assets. Our method allows one to construct from known random…