B1099
Title: Regularized changepoint detection in panel data models applied for predictions of implied volatility dynamics
Authors: Matus Maciak - Charles University (Czech Republic) [presenting]
Abstract: Implied volatility (IV) serves as an important and powerful tool when analyzing financial markets. We propose a novel approach to estimate the overall IV dynamics represented by the underlying panel data model with changepoints. A robust semi-parametric regression framework and atomic pursuit techniques lasso based regularization, in particular, are applied to estimate the underlying analytical structure of the implied volatility surface and a statistical test is used to detect significant changepoints. The overall complexity of the model relies on changepoints that may occur over time, in the analytical structure of the IV smiles, or both. Theoretical and practical details are discussed and the main statistical properties are derived. Empirical properties are investigated in a simulation study and real-life applications are presented to illustration wide and general applicability.
