Grigory Vilkov

Professor of Finance
Frankfurt School of Finance & Management
✉️ vilkov@vilkov.net

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🔥 Highlights

• QMOMS Python Package: Implied Moments from Volatility Surface Data on GitHub.
• Mission Accomplished (accepted at RFS): Dynamics of Asset Demands with Confidence Heterogeneity, joint with Adrian Buss and Raman Uppal.
• 0DTE Gamma paper merged and resubmitted to RFS: Do S&P500 Options Increase Market Volatility? Evidence from 0DTEs, joint with Greg Adams, Chukwuma Dim, Bjorn Eraker, Jean-Sebastien Fontaine, Chayawat Ornthanalai.

Current research agenda

• Deep learning and parametric portfolios.
• Automatic feature discovery for high- and medium-frequency conditional alpha modeling.
• Rough voltility modeling.
• Designing and explaining quantile dispersions.

Research

My research focuses on options markets as sources of forward-looking information — including volatility, skewness, correlation, factor betas, and other variables based on risk-neutral return characteristics — and their role in return and risk predictability. I investigate options markets, including 0DTE options, emphasizing trading rules, gamma exposure, volatility propagation, and study factor and quantile dispersions. In portfolio construction, I work on skew-sensitive allocations driven by forward-looking signals and actively explore new ML/DL approaches to constructing efficient portfolios. With co-authors, we explore asset pricing through narratives and sentiment, using NLP applied to financial news and earnings calls. In climate finance, we study how firm-level exposure to climate-related risks is related to financial risks and real firms’ activity and how it is reflected in asset prices.