Blog

I am not a professional blogger but a true passionate about finance research. As various projects are progressing, there is a lot of information which I find to be relevant not only to academics but also to professionals in the finance industry.

The purpose of this blog is to share this information in a concise way and make research knowledge more accessible, with the result of not necessarily using academic references, citations or specific vocabulary. The blog covers topics as varied as FinTech and its applications in finance to the outcomes of my various academic involvements. Enjoy the reading!

Blog

I am not a professional blogger but a true passionate about finance research. As various projects are progressing, there is a lot of information which I find to be relevant not only to academics but also to professionals in the finance industry.

The purpose of this blog is to share this information in a concise way and make research knowledge more accessible, with the result of not necessarily using academic references, citations or specific vocabulary. The blog covers topics as varied as FinTech and its applications in finance to the outcomes of my various academic involvements. Enjoy the reading!

Research Update

My recent research focuses on understanding how investors’ demand pressures alter inventory risk management strategies of market makers in the equity options market.

Derivatives, unlike stocks, are in zero net supply. Thus, positive or negative demand pressures, and inability to hedge perfectly, can lead to various deviations from model-implied prices. These deviations are non-trivial and result in different qualities of executions depending on what time of a day you trade. I am currently working with LiveVol/CBOE to attract attention of academics and industry practitioners about the importance of using intra-day trading equity options data rather than end-of-day closing prices.

Liquidity Guided Machine Learning: The Case of the Volatility Risk Premium

with Eric Ghysels (UNC) and Chengyu Zhang (McGill)

The financial industry has eagerly adopted machine learning algorithms to improve on traditional predictions models. In our paper we caution against blindly applying such techniques. In the case of options trading standard machine learning tends to pick illiquid contracts. We propose new ways to supplement machine learning with financial knowhow.

Volatility and the Cross-Section of Equity Returns: The Role of Short-Selling Constraints

with Paul Schultz (University of Notre Dame)

Price Pressures and Option Returns

with Chengyu Zhang (McGill)

Disagreement in the Equity Options Market and Stock Returns

(Revise and Resubmit, Review of Financial Studies)
with Benjamin Golez (Notre Dame)