Minimizing post-shock forecasting error through aggregation of outside information

TL;DR

This paper introduces a new forecasting method that leverages information from similar past shocks in related time series to improve post-shock predictions, validated through simulations and real stock data.

Contribution

It proposes a novel approach to incorporate external shock information into forecasting models, with risk-reduction conditions and validation procedures.

Findings

Method improves forecast accuracy after shocks

Bootstrap and cross-validation assess performance effectively

Validated on stock price data and simulations

Abstract

We develop a forecasting methodology for providing credible forecasts for time series that have recently undergone a shock. We achieve this by borrowing knowledge from other time series that have undergone similar shocks for which post-shock outcomes are observed. Three shock effect estimators are motivated with the aim of minimizing average forecast risk. We propose risk-reduction propositions that provide conditions that establish when our methodology works. Bootstrap and leave-one-out cross validation procedures are provided to prospectively assess the performance of our methodology. Several simulated data examples, and a real data example of forecasting Conoco Phillips stock price are provided for verification and illustration.