Functional PCA for Risk-Neutral densities in Bayes Hilbert space

In this work, we investigate the main drivers of risk-neutral densities of quoted stocks, using the functional principal component analysis (FPCA). To this end, we first construct a historical series of risk-neutral densities corresponding to quoted option prices with fixed time to maturity, using exponential expansions of orthogonal polynomials. Then, we apply the centered log-ratio transformation (CLRT) to the extracted densities and we perform the FPCA in the Bayes–Hilbert space. The CLRT provides an isometric isomorphism between the Bayes space of square log-integrable densities and the classical Hilbert space of square-integrable functions. As a result, the projected data onto the principal component basis correspond to the CLRT-transformed densities, and the application of the inverse CLRT yields proper density functions. Furthermore, by modeling the historical series of FPCA loadings as a stochastic process, we exploit the FPCA representation for forecasting purposes. Finally, we discuss extensions of this framework to cross-asset analyses and to the modeling of option price surfaces.
This is a joint work with A. Amici e G. Fusai.