Research Interests

Asset and Derivatives Pricing, Machine Learning


Credit Variance Risk Premiums, with Manuel Ammann
European Financial Management, forthcoming (Open Access at EFM)

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)

Commodity Tail Risks, with Manuel Ammann, Marcel Prokopczuk, and Christoph Würsig (JFM version)
Journal of Futures Markets, forthcoming

Abstract: In this study, we investigate the cross-section of option implied tail risks in commodity markets. In contrast to findings from equity markets, left and right tail risk implied by option markets are both large. Commodity specific variables exert the largest influence on tail risk, while there is no evidence of systematic commodity factors that are linked to tail risk. Additionally, we find strong links to the equity markets, but also co-movements to macroeconomic factors. Left or right tail risks are largely independent of variance risk premiums. Finally, both left and right tail risk are priced in the cross-section of commodity futures returns.

Presentations: Finance Seminar at the Leibniz University of Hannover

Working Papers

Option Return Predictability with Machine Learning and Big Data, with Turan G. Bali, Heiner Beckmeyer, and Florian Weigert (available at SSRN)

Media: alpha architect, LexTech Institute

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; 14th Annual Hedge Fund Conference at Imperial College London 2022

Liquidity Provision to Leveraged ETFs and Equity Options Rebalancing Flows: Evidence from End-of-Day Stock Prices, with Andrea Barbon, Heiner Beckmeyer, and Andrea Buraschi (available at SSRN)

Media: Financial Times, alpha architect

Abstract: Rebalancing of leveraged ETFs (LETFs) and delta-hedging of equity options by intermediaries are two distinct and economically significant sources of liquidity demands. We show that they induce end-of-day momentum and mean-reversion in returns. While gamma effects are persistent throughout our sample, LETFs effects have decreased over time. We empirically study these effects and their potential drivers. We find that LETF flows attract more liquidity provision and their effects on prices are shorter-lived. Intermediaries can strategically decide the timing of their delta-hedging, resulting in less predictable flows. This shows the benefits of information disclosure on market liquidity and price distortion.

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); Societe Generale (2022); Fidelity (2022); 13th Annual Hedge Fund Research Conference Paris 2022; MFA Annual Meeting 2022; SFS Cavalcade North America 2022; Hofstra Financial Regulations & Technology 2022; WFA Annual Meeting 2022

Work in Progress

Guiding Hedge Fund Manager Selection with Machine Learning, with Turan G. Bali, Heiner Beckmeyer, and Florian Weigert