Ms. Yingyi Gu joined the Bloomberg Quantitative Research group in 2018. Prior to that, she earned her Master in Finance from Princeton University. At Bloomberg, Ms. Gu’s work focuses on applying innovative quantitative models across all asset classes & using machine learning methods to help reveal embedded signals in various data.
In this talk we are addressing some issues associated with FX volatility surface construction. Regular economic data releases, as well as one-off events like elections, can drastically change the implied volatility profile. On the Bloomberg terminal users may manipulate volatilities by adjusting weights for those events in the function VCAL. We describe here a methodology on how to obtain those economic weights. In addition, we use sentiment data to help us better gauge the relative importance between these events.