From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks

arXiv:2311.16333v2 Announce Type: replace-cross
Abstract: We reinvigorate maximum likelihood estimation (MLE) for macroeconomic density forecasting through a novel neural network architecture with dedicated mean and variance hemispheres. Our architecture features several key ingredients making MLE work in this context. First, the hemispheres share a common core at the entrance of the network which accommodates for various forms of time variation in the error variance. Second, we introduce a volatility emphasis constraint that breaks mean/variance indeterminacy in this class of overparametrized …

abstract architecture arxiv context core cs.lg econ.em features forecasting key likelihood making maximum maximum likelihood estimation mean mle modeling network network architecture networks neural network neural networks novel through type variance work