7 papers
A data-driven Fourier-mixture neural-network method for density estimation
Duy-Minh Dang, Volter Entoma
We propose a data-driven Fourier-trained neural-network method for estimating fixed-horizon probability densities from empirical characteristic-function (CF) information. The estim…
Monotone 2D Integration Scheme for Mean-CVaR Optimization via Fourier-Trained Transition Kernels
Duy-Minh Dang, Hao Zhou
We present a strictly monotone, provably convergent two-dimensional (2D) integration method for multi-period mean-conditional value-at-risk (mean-CVaR) reward-risk stochastic contr…
Convergence of Neural Network Policies for Risk--Reward Optimization
Chang Chen, Duy-Minh Dang
We develop a neural-network framework for multi-period risk--reward stochastic control problems with constrained two-step feedback policies that may be discontinuous in the state.…
Money-Back Tontines for Retirement Decumulation: Neural-Network Optimization under Systematic Longevity Risk
German Nova Orozco, Duy-Minh Dang, Peter A. Forsyth
Money-back guarantees (MBGs) are features of pooled retirement income products that address bequest concerns by ensuring the initial premium is returned through lifetime payments o…
Multi-period Mean-Buffered Probability of Exceedance in Defined Contribution Portfolio Optimization
Duy-Minh Dang, Chang Chen
We investigate multi-period mean-risk portfolio optimization for long-horizon Defined Contribution plans, focusing on buffered Probability of Exceedance (bPoE), a more intuitive, d…
A monotone piecewise constant control integration approach for the two-factor uncertain volatility model
Duy-Minh Dang, Hao Zhou
Option contracts on two underlying assets within uncertain volatility models have their worst-case and best-case prices determined by a two-dimensional (2D) Hamilton-Jacobi-Bellman…