Kavli Affiliate: Hsiao-Mei (Sherry) Cho
| First 5 Authors: [#item_custom_name[1, [#item_custom_name[2, [#item_custom_name[3, [#item_custom_name[4, [#item_custom_name[5| Summary:I study intertemporal hedging demand in a continuous-time multi-asset long-run risk (LRR) model under Epstein–Zin (EZ) recursive preferences. The investor trades a risk-free asset and several risky assets whose drifts and volatilities depend on an Ornstein–Uhlenbeck type LRR factor. Preferences are described by EZ utility with risk aversion $R$, elasticity of intertemporal substitution $ψ$, and discount rate $δ$, so that the standard time-additive CRRA case appears as a limiting benchmark.
To handle the high-dimensional consumption–investment problem, I use a projected Pontryagin-guided deep policy optimization (P-PGDPO) scheme adapted to EZ preferences. The method starts from the continuous-time Hamiltonian implied by the Pontryagin maximum principle, represents the value and costate processes with neural networks, and updates the policy along the Hamiltonian gradient. Portfolio constraints and a lower bound on wealth are enforced by explicit projection operators rather than by adding ad hoc penalties.
Three main findings emerge from numerical experiments in a five-asset LRR economy: textbf(1) the P-PGDPO algorithm achieves stable convergence across multiple random seeds, validating its reliability for solving high-dimensional EZ problems; textbf(2) wealth floors materially reduce hedging demand by limiting the investor’s ability to exploit intertemporal risk-return tradeoffs; and textbf(3) the learned hedging portfolios concentrate exposure in assets with high correlation to the LRR factor, confirming that EZ agents actively hedge long-run uncertainty rather than merely following myopic rules. Because EZ preferences nest time-additive CRRA in the limit $ψto 1/R$, I use CRRA as an explicit diagnostic benchmark and, when needed, a warm start to stabilize training in high dimensions.| Search Query: arXiv Query: search_query=au:Cho OR all:Hsiao-Mei&id_list=&start=0&max_results=3Read More