activity
20162026
most citedModelling and understanding count processes through a Markov-modulated non-homogeneous Poisson process framework

22 citations · 155 across the 27 of their papers we have counts for

collaborators
Showing 2022Show all

6 papers · 1 filter

math.OC2022★ 5 cited

Stable Dividends under Linear-Quadratic Optimization

Benjamin Avanzi, Debbie Kusch Falden, Mogens Steffensen

The optimization criterion for dividends from a risky business is most often formalized in terms of the expected present value of future dividends. That criterion disregards a pote…

stat.ME2022

Ensemble distributional forecasting for insurance loss reserving

Benjamin Avanzi, Yanfeng Li, Bernard Wong +1

Loss reserving generally focuses on identifying a single model that can generate superior predictive performance. However, different loss reserving models specialise in capturing d…

math.OC2022

Optimal reinsurance design under solvency constraints

Benjamin Avanzi, Hayden Lau, Mogens Steffensen

We consider the optimal risk transfer from an insurance company to a reinsurer. The problem formulation considered in this paper is closely connected to the optimal portfolio probl…

math.OC2022

On the surplus management of funds with assets and liabilities in presence of solvency requirements

Benjamin Avanzi, Ping Chen, Lars Frederik Brandt Henriksen +1

In this paper we consider a company whose assets and liabilities evolve according to a correlated bivariate geometric Brownian motion, such as in Gerber and Shiu (2003). We determi…

stat.ME2022★ 4 cited

Detection and treatment of outliers for multivariate robust loss reserving

Benjamin Avanzi, Mark Lavender, Greg Taylor +1

Traditional techniques for calculating outstanding claim liabilities such as the chain ladder are notoriously at risk of being distorted by outliers in past claims data. Unfortunat…

stat.ME2022

On the impact of outliers in loss reserving

Benjamin Avanzi, Mark Lavender, Greg Taylor +1

The sensitivity of loss reserving techniques to outliers in the data or deviations from model assumptions is a well known challenge. It has been shown that the popular chain-ladder…