Using Treasury Note Option Volatility to Forecast Underlying Price Movements on Non-Farm Payroll Dates

Abstract

Monthly Nonfarm Payroll (NFP) Announcements are high-volatility macroeconomic events generally preceded by significant high-frequency trading activity. This exploratory research project involves forecasting underlying price movements on Non-farm Payroll Announcement dates by applying various machine learning methods to novel feature sets constructed using front-term implied volatility surfaces. Although there already exists considerable research exploring the relationship between the options market and the expectancy of spot price movement following high-volatility events, much of the available literature focuses on equity options and earnings announcements; by contrast, this study examines the interpolated implied volatility surfaces of options on 10-year treasury note futures using robust intraday quote data from Chicago Mercantile Exchange. We find that machine learning algorithms trained on this data demonstrate a limited but non-trivial ability to generate meaningful predictions about the magnitude and, to a lesser extent, the direction of spot price movements. Finally, we conclude that a simple multivariate regression model exhibits the best predictive power and performance on validation data sets. This observation is used to propose potentially profitable volatility trading strategies on NFP dates.