Implied Volatility Surface Modeling for KOSPI 200 option and ODAX with DSFM

Implied volatility is one of the important topics in financial markets. Due to option data's characteristics, estimating implied volatility is a challenging task for both academia and industry. Dynamic Semiparametric Factor Model (DSFM) is method to model high-dimensional data with dynamic context. It employs semiparametric factor functions and time-varying loadings. One of its application is implied volatility surface (IVS) modeling. This master thesis applys DSFM to estimate IVS of Korean Stock Index (KOSPI 200) options and ODAX. Estimation result is discussed and a comparison between two markets from view of DSFM is studied.