infinite-horizon-stochastic-analysis

You model the infinite-horizon problem within a weighted L^p space, reformulate it as a BSVIE, and compute a resolvent kernel to obtain an actionable approximation. This approach handles long memory and evolving probability measures.

BSVIE formulation maps infinite-horizon stochastic optimization problems into weighted L^p spaces, enabling resolvent-kernel approximations and measure-change techniques like Girsanov for evolving probability measures.

Yes, measure-change techniques such as Girsanov are applied to handle evolving probability measures within infinite-horizon stochastic analysis. This ensures robust long-term decision making in models with long memory.

Yes, infinite-horizon stochastic analysis applies to perpetual asset pricing, climate policy, retirement planning, and long-term maintenance optimization. It uses weighted L^p spaces and BSVIE representations to manage long-term variables.

Weighted L^p spaces require truncation and resolvent-kernel computation to manage convergence over infinite horizons. The approach necessitates careful BSVIE formulation and optional measure-change techniques to handle evolving measures accurately.