Asset and Derivatives Pricing, Machine Learning
Leveraged ETFs, Option Market Imbalances, and End-of-Day Price Dynamics, with Andrea Barbon, Heiner Beckmeyer, and Andrea Buraschi (available at SSRN; previously circulated under the title End-of-Day Momentum and Option Hedging)
Media: Financial Times
Abstract: Leveraged ETFs and market makers who are active in options markets must adjust imbalances arising from market movements to achieve delta-neutrality. This dynamic adjustment may cause either end-of-day return momentum or reversal depending on the size of the imbalance versus the prevailing liquidity. We find that a large and negative (positive) aggregated gamma imbalance, relative to the average dollar volume, gives rise to an economically and statistically significant end-of-day momentum (reversal). We compare this channel to the rebalancing of leveraged ETFs and find that the effect generated by leveraged ETFs is economically larger. Consistent with the notion of temporary price pressure, the documented effects quickly revert at the next day's open. Information-based explanations are unlikely to cause the results, suggesting a non-informational channel through which leveraged ETFs and options markets affect underlying stocks towards the market close.
Presentations: NFA Annual Meeting 2021; 5th SAFE Market Microstructure Conference (2021); 37th International Conference of the French Finance Association (2021); FMA Annual Meeting 2021; EFMA Annual Meeting 2021; FMA Conference on Derivatives and Volatility (2021); Goldman Sachs STS (2021); Bank of America Merrill Lynch (2021); Morgan Stanley (2021); 13th Annual Hedge Fund Research Conference 2022
Abstract: Drawing upon more than 12 million observations over the period from 1996 to 2020, we find that allowing for nonlinearities significantly increases the out-of-sample performance of option and stock characteristics in predicting future option returns. Besides statistical significance, the nonlinear machine learning models generate economically sizeable profits in the long-short portfolios of equity options even after accounting for transaction costs. Although option-based characteristics are the most important standalone predictors, stock-based measures offer substantial incremental predictive power when considered alongside option-based characteristics. Finally, we provide compelling evidence that option return predictability is driven by informational frictions and option mispricing.
Presentations: Virtual Derivatives Workshop PhD Session (2021); Goldman Sachs STS (2021); Hull Tactical Asset Allocation (2021); BVI-CFR Workshop 2021
Media: alpha architect
Abstract: This paper studies variance risk premiums in the credit market using a novel data set of swaptions quotes on the CDX North America Investment Grade and High Yield indices. We find that returns of credit variance swaps are negative and economically large, irrespective of the credit rating class. Shorting credit variance swaps yields annualized Sharpe ratios well above their counterparts in other asset classes. The returns remain highly statistically significant when accounting for transaction costs and cannot be explained by established risk-factors and structural model variables. By means of corridor variance swaps, we also dissect the overall variance risk premium into receiver and payer variance risk premiums. We show that credit variance risk premiums are mainly driven by the payer corridor, which is associated with worsening macro-economic conditions.
Presentations: AFA PhD Poster Session (2019); Finance Research Seminar, University of St.Gallen (2019); Finance Research Seminar, University of Konstanz (2019); SoFiE Financial Econometrics Summer School (2019); Paris Financial Management Conference (2019); 26th Annual Meeting of the German Finance Association Doctoral Workshop (2019)