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Seminar in Applied Economics: Wim Vijverberg

SEP 28, 2021 | 12:00 PM

Wim Vijverberg

Details

WHERE:

Zoom

WHEN:

September 28, 2021: 12:00 PM

ADMISSION:

Free

SPONSOR:

PhD in Economics

Description

Fall 2021 Seminar in Applied Economics will feature Wim Vijverberg. Professor Vijverberg will be presenting on "Factors, Filters, and Cross-Sectional Dependence: The Currency Implied Volatility Surface Forecast Revisited" on September 28th at 12pm via zoom.  

Please feel free to register and attend the event using the zoom link below: 
https://gc-cuny.zoom.us/meeting/register/tJwsceyrqj8rGtWqgpyHquyHz1-SGyREU0hB

Please note: Students, faculty, staff, alumni, and others who participate in any of the following seminars with their camera on or use a profile image are agreeing to have their video or image recorded solely for the purpose of creating a record for participants in this seminar to refer to, including those enrolled students who are unable to attend live.  If you are unwilling to consent to have your profile or video image recorded, be sure to keep your camera off and do not use a profile image. Likewise, participants who un-mute during the seminar or class and participate orally are agreeing to have their voices recorded.  If you are not willing to consent to have your voice recorded, you will need to keep your mute button activated and communicate exclusively using the "chat" feature, which allows participants to type questions and comments live. ‚Äč Abstract: The extremely high correlations among the implied volatility time series of currency options signify a strong cross-sectional dependence among currency options. Successful forecasting of the dynamics of the implied volatility surface (IVS) should account for this cross-sectional dependence. Following the recent spatial panel econometrics literature that developed the notion of strong cross-sectional dependence, this paper introduces new forecasting models by incorporating filters into the implied volatility forecasting process. These new models outperform the existing IVS forecasting models in the finance literature with regularity.