10 citations · 17 across the 8 of their papers we have counts for
8 papers
Universal Diffusion Models for Implied Volatility Surfaces: Learning Shared Dynamics Across Stocks
Mingzhi Yang, Sheng Wang, Chao Zhang +1
Modeling the dynamics of option implied volatility surface (IVS) is crucial for pricing, hedging, and risk-managing option portfolios. We develop a universal conditional diffusion…
DeltaLag: Learning Dynamic Lead-Lag Patterns in Financial Markets
Wanyun Zhou, Saizhuo Wang, Mihai Cucuringu +5
The lead-lag effect, where the price movement of one asset systematically precedes that of another, has been widely observed in financial markets and conveys valuable predictive si…
Forecasting Intraday Volume in Equity Markets with Machine Learning
Mihai Cucuringu, Kang Li, Chao Zhang
This study focuses on forecasting intraday trading volumes, a crucial component for portfolio implementation, especially in high-frequency (HF) trading environments. Given the curr…
Graph Neural Networks for Forecasting Multivariate Realized Volatility with Spillover Effects
Chao Zhang, Xingyue Pu, Mihai Cucuringu +1
We present a novel methodology for modeling and forecasting multivariate realized volatilities using customized graph neural networks to incorporate spillover effects across stocks…
Tail-GAN: Learning to Simulate Tail Risk Scenarios
Rama Cont, Mihai Cucuringu, Renyuan Xu +1
The estimation of loss distributions for dynamic portfolios requires the simulation of scenarios representing realistic joint dynamics of their components. We propose a novel data-…
Volatility forecasting with machine learning and intraday commonality
Chao Zhang, Yihuang Zhang, Mihai Cucuringu +1
We apply machine learning models to forecast intraday realized volatility (RV), by exploiting commonality in intraday volatility via pooling stock data together, and by incorporati…
Cross-Impact of Order Flow Imbalance in Equity Markets
Rama Cont, Mihai Cucuringu, Chao Zhang
We investigate the impact of order flow imbalance (OFI) on price movements in equity markets in a multi-asset setting. First, we propose a systematic approach for combining OFIs at…
A Universal End-to-End Approach to Portfolio Optimization via Deep Learning
Chao Zhang, Zihao Zhang, Mihai Cucuringu +1
We propose a universal end-to-end framework for portfolio optimization where asset distributions are directly obtained. The designed framework circumvents the traditional forecasti…