Research

Research:

Agent-based Model Parameters Estimation with Wasserstein Distance with Sebastiano Manzan (Preprint) (Slides)
- The novel method outperformed 4 other estimation techniques (3 likelihood-based estimation methods and the method of simulated moments) in terms of accuracy and computational cost.

Unearthing Market Dependencies: Data-Driven Neural Networks and Realized Volatility Prediction through Graph Discovery
- Constructed a novel graph discovery integrated Graph Neural Network (GNN) model for predictive tasks and data mining in finance, particularly for high-dimensional data.
- Model predicted short-term intraday Realized Volatility (RV) by using Limit Order Book (LOB) and trade data with a customized underlying network discovered by the data, effectively capturing spillover effects across financial assets. This model demonstrated a 15% improvement of the baseline LightGBM and Neural Net models.

Recommender System Matching Small Medium Enterprises with Influencers Using Graph Neural Networks. (Work in progress)
- Constructing a multi-modal Graph Neural Network recommender system in PyTorch to match small medium enterprises and influencers to maximize marketing effectiveness.

Conference Talks:

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