3 papers in the archive (2024), average empirical rigor 8.2/10, average math complexity 7.2/10. Quadrant mix: Holy Grail ×3.
Works mostly in Volatility Modeling & Forecasting, Machine Learning for Market Forecasting, Risk Management & Tail Risk.
Frequent co-authors: Chao Wang (3), Rangika Peiris (2), Minh-Ngoc Tran (2), Qianli Zhao (1), Giuseppe Storti (1), Lingxiang Zhang (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 long memory and non-linear realized volatility model class is proposed for direct Value at Risk (VaR) forecasting. This model, referred to as RNN-HAR, extends the heterogeneous autoregressive (HAR) model, a framework known for efficiently capturing long memory in realized measures, by integrating
A semi-parametric joint Value-at-Risk (VaR) and Expected Shortfall (ES) forecasting framework employing multiple realized measures is developed. The proposed framework extends the realized exponential GARCH model to be semi-parametrically estimated, via a joint loss function, whilst extending existi
Realised volatility has become increasingly prominent in volatility forecasting due to its ability to capture intraday price fluctuations. With a growing variety of realised volatility estimators, each with unique advantages and limitations, selecting an optimal estimator may introduce challenges. I