WASP: Voting-based ex Ante method for Selecting joint Prediction strategy

TL;DR

This paper introduces WASP, a voting-based ex ante method for selecting optimal joint prediction strategies by combining diverse models and accuracy measures through Monte Carlo simulation, demonstrated on insurance data.

Contribution

It proposes a novel voting-based approach for ex ante prediction strategy selection that incorporates multiple models and accuracy measures, adaptable to new scenarios.

Findings

Effective in selecting prediction strategies for insurance portfolio management.

Utilizes Monte Carlo simulation for scenario analysis.

Implemented in R for practical application.

Abstract

This paper addresses the topic of choosing a prediction strategy when using parametric or nonparametric regression models. It emphasizes the importance of ex ante prediction accuracy, ensemble approaches, and forecasting not only the values of the dependent variable but also a function of these values, such as total income or median loss. It proposes a method for selecting a strategy for predicting the vector of functions of the dependent variable using various ex ante accuracy measures. The final decision is made through voting, where the candidates are prediction strategies and the voters are diverse prediction models with their respective prediction errors. Because the method is based on a Monte Carlo simulation, it allows for new scenarios, not previously observed, to be considered. The first part of the article provides a detailed theoretical description of the proposed method, while the second part presents its practical use in managing a portfolio of communication insurance. The example uses data from the Polish insurance market. All calculations are performed using the R programme.