A seminar by Professor Pavel Shevchenko from Macquarie University
Title: Solving stochastic dynamic integrated climate-economy models using Least Squares Monte Carlo methods
Abstract: The classical dynamic integrated climate-economy (DICE) model has become the iconic typical reference point for the joint modelling of economic and climate systems, where all six model state variables (including carbon concentration, temperature, and economic capital) evolve over time deterministically and are affected by two controls (carbon emission mitigation rate and consumption). We consider the DICE model with stochastic shocks in various parts of the model and solve it under several scenarios as an optimal stochastic control problem using the Least Square Monte Carlo method (LSMC) - a popular simulation method for solving optimal stochastic control problems in quantitative finance. We consider application of various LSMC methods (including the use of neural network approximation) and discuss their pros and cons.
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