Events

Peter Carr Seminar Series: Bryan Liang

Finance and Risk Engineering
Lecture / Panel
 
Open to the Public
Logo for Peter Carr Seminar Series with the lyrics: "There will be an answer," from the song "Let it Be" by the Beatles
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This event is free, but registration is required for those who do not have an NYU ID.

Bryan Liang

Senior Quantitative Researcher at Bloomberg L.P.

Title

Break-Even Volatility - Option Market Making and Beyond

Abstract

We develop a systematic framework for constructing implied volatility surfaces for underlying securities with illiquid or nonexistent option markets, combining break-even volatility (BEVL) with weighted Monte Carlo (WMC). The framework can incorporate information from historical spot prices, option smiles of similar securities, sparse option quotes, and forecasts of future realized volatility.

Rather than assigning equal importance to historical paths in BEVL calculations, we assign probability weights based on their similarity to current market conditions. Alternatively, in the spirit of WMC, these weights can be implied from available option quotes or volatility forecasts. For securities with liquid option markets, the weights can be calibrated to the full observed option smile and subsequently transferred to BEVL calculations for related securities, providing a systematic link between liquid and illiquid volatility markets.

The resulting weighted BEVL surfaces can support market making, cross-vega hedging, and relative-value trading. We also demonstrate how implied historical weights can be applied to a broader range of problems, including implied volatility extrapolation, variance swap valuation, volatility-surface updating from partial market information, implied stock beta estimation, and intraday forecasting using 0DTE options.

Bio

Bryan (Jianfeng) Liang is a Senior Quantitative Researcher at Bloomberg L.P., where he has been a member of the Quantitative Research team since 2011. His work spans a broad range of topics in derivatives, including numerical solutions of nonlinear problems, optimal transport and robust hedging, vol surface construction, market making, trading strategies, structuring, high-performance computing, and applications of machine learning to derivatives.

Prior to joining Bloomberg, Bryan was an interest rate derivatives quantitative researcher in the Derivatives Analysis Group at Goldman Sachs. He received his Ph.D. in Mathematics from the University of Michigan and held faculty positions at Northwestern University and the University of California, Davis before transitioning to finance. Since 2016, Bryan has also served as an adjunct professor of mathematical finance at the Courant Institute of Mathematical Sciences at New York University and at Columbia University.