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- W4297324064 abstract "I develop a numerical algorithm for stochastic impulse control in the spirit of Regression Monte Carlo for optimal stopping. The approach consists in generating statistical surrogates (aka functional approximators) for the continuation function. The surrogates are recursively trained by empirical regression over simulated state trajectories. In parallel, the same surrogates are used to learn the intervention function characterizing the optimal impulse amounts. I discuss appropriate surrogate types for this task, as well as the choice of training sets. Case studies from forest rotation and irreversible investment illustrate the numerical scheme and highlight its flexibility and extensibility. Implementation in R is provided as a publicly available package posted on GitHub." @default.
- W4297324064 created "2022-09-28" @default.
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- W4297324064 date "2022-09-27" @default.
- W4297324064 modified "2023-10-04" @default.
- W4297324064 title "Regression Monte Carlo for Impulse Control" @default.
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- W4297324064 doi "https://doi.org/10.5802/msia.18" @default.
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