3 papers in the archive (2022–2025), average empirical rigor 8.3/10, average math complexity 5.3/10. Quadrant mix: Holy Grail ×2, Street Traders ×1.
Works mostly in Volatility Modeling & Forecasting, Risk Management & Tail Risk, Machine Learning for Market Forecasting, Factor Investing & Asset Pricing.
Frequent co-authors: Anubha Goel (2), Juho Kanniainen (2), Martin Magris (1), Damir Filipović (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%).
We introduce an ensemble learning method based on Gaussian Process Regression (GPR) for predicting conditional expected stock returns given stock-level and macro-economic information. Our ensemble learning approach significantly reduces the computational complexity inherent in GPR inference and lend
Time series foundation models (FMs) have emerged as a popular paradigm for zero-shot multi-domain forecasting. These models are trained on numerous diverse datasets and claim to be effective forecasters across multiple different time series domains, including financial data. In this study, we evalua
This study is the first to analyze the performance of a time-series foundation AI model for Value-at-Risk (VaR), which essentially forecasts the left-tail quantiles of returns. Foundation models, pre-trained on diverse datasets, can be applied in a zero-shot setting with minimal data or further impr