Pricing dynamic fund protection under hidden Markov models

Purpose: Investigating the valuation of dynamic fund protection under hidden Markov models Originality: Compared with traditional put options, dynamic fund protection (DFP) plans protect investors from the whole investment period against financial risks. This feature is the main reason why the dynamic fund protection plans have attracted increasing attention from both academic researchers and market practitioners. On the other hand, hidden Markov models present a natural choice for modelling transitions in hidden states of an economy. So it is of significant and practical value to investigate the pricing of dynamic fund protection under hidden Markov models. Design/methodology/approach: The parameters of our model are modulated by a continuous-time, finite-state hidden Markov chain. Firstly, we introduce approaches to estimate the states of the hidden Markov chain. We then employ the Esscher transform to select a pricing kernel for valuing the DFP and adopt the approach partial differential equations to value dynamic fund protections. Findings: Findings include numerical examples to illustrate our approach to value the dynamic fund protection, methods to calculate the price of dynamic fund protection and comparison of the prices in different economy states. Sensitivity tests are also conducted to assess the impacts of different parameters on dynamic fund protection prices.