Research
Discover the latest research papers written by OptionMetrics,
our customers, and researchers worldwide leveraging
OptionMetrics data.
N. Käfer, M. Moerke, and T. Wiest: “Option Factor Momentum”
We document profitable cross-sectional and time-series momentum in a broad set of 56 option factors constructed from monthly sorts on daily delta-hedged option positions. Option factor returns are highly autocorrelated, but momentum profits of strategies with longer formation periods are ... Read More
P. Neo, C. Tee: “Tail Risk Hedging: The Search for Cheap Options”
We find that a simple heuristic of sorting liquid equity options by dollar price to construct a portfolio of cheap put options leads to a surprisingly robust tail risk hedge - the superior performance holds even when compared against advanced ... Read More
A. Naranjo, M. Nimalendran, and Y. Wu: “Betting on Elusive Returns: Retail Trading in Complex Options”
Retail trading in complex (multi-leg) options has grown significantly following the in- troduction of zero commissions by several brokerage firms. We show that the returns on these complex orders are negative on average (-16.4% over three-day holding periods), and that ... Read More
G. Freire and O. Kleen: “Equity Options and Firm Characteristics”
We study the relation between a comprehensive set of firm characteristics and the entire universe of individual equity option prices. We find that 42 out of 86 characteristics are priced in the option market, in the sense that they significantly ... Read More
G. DeSimone: Demand for Option Order Delta
OptionMetrics’ latest research paper, Demand for Option Order Delta (DOOD), proposes a new options-based metric to estimate demand imbalance for delta by end-users of options. Read the full white paper below to learn more. Download Read More
T. Bali, H. Beckmeyer, M. Moerke, F. Weigert: Option Return Predictability with Machine Learning and Big Data
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. The nonlinear machine learning models ... Read More
Highlighted Research
Reducing Risk through Multifactors: Implied Variance Asymmetry and Implied Beta
By G. DeSimone & O. Shih
February 15, 2024
OptionMetrics' latest study challenges conventional risk assessment using metrics like implied variance asymmetry (IVA), calculated as the measure of downside variance relative to upside variance, and option-implied beta strategies.