Seminario per il Dottorato in Scienze Attuariali
Asset-liability management for occupational pension funds under market and longevity risk: a case study and alternative modelling approaches Prof. Giorgio Consigli Università degli studi di Bergamo The modelling of institutional ALM problems has a long history in stochastic programming starting in the late 80’s with the first industry developments such as the well-known Yasuda Kasai model (Ziemba, Turner, Carino et al, 1994) specifically for pension fund management (PF ALM). Due to economic and demographic pressures in most OECD countries and an increasing interest on PF ALM developments by the industry and by policy makers, we witness now-a-day a growing demand for R&D projects to the scientific community. Taking the view of a PF manager, the presentation will develop around the definition of a generic pension fund (PF) asset-liability management (ALM) problem and analyse the key underlying methodological implications of: (i) it's evolution from an early stage multistage stochastic programming (MSP) with recourse to most recent MSP and distributionally robust (DRO) formulations, (ii) a peculiar and rich risk spectrum including market risk as well as liability risk, such as longevity risk and demographic factors leading to (iii) valuation or pricing approaches based on incomplete market assumptions and, due to recent International regulation, (iv) a risk-based capital allocation for long-term solvency. The above represent fundamental stochastic and mathematical problems of modern financial optimisation. Two possible approaches to DRO are considered, based on a stochastic control framework or by explicitly introducing an uncertainty set for probability measures and formulating the inner DRO problem as a probability distance minimization problem over a given space of measures. Keywords: asset-liability management, multistage stochastic programming, distributional uncertainty, distributionally robust optimization, solvency ratio, liability pricing, longevity risk, capital allocation.
Prof. Giorgio Consigli
22/03/2019 - 15:00
[Aula V, 4° piano del Dipartimento di Scienze Statistiche. Città Universitaria.]