13 citations · 14 across the 10 of their papers we have counts for
15 papers
An Interpretable Deep Learning Model for General Insurance Pricing
Patrick J. Laub, Tu Pho, Bernard Wong
This paper introduces the Actuarial Neural Additive Model, an inherently interpretable deep learning model for general insurance pricing that offers fully transparent and interpret…
Arbitrage-free catastrophe reinsurance valuation for compound dynamic contagion claims
Jiwook Jang, Patrick J. Laub, Tak Kuen Siu +1
In this paper, we consider catastrophe stop-loss reinsurance valuation for a reinsurance company with dynamic contagion claims. To deal with conventional and emerging catastrophic…
Distributional Refinement Network: Distributional Forecasting via Deep Learning
Benjamin Avanzi, Eric Dong, Patrick J. Laub +1
A key task in actuarial modelling involves modelling the distributional properties of losses. Classic (distributional) regression approaches like Generalized Linear Models (GLMs; N…
Hawkes Models And Their Applications
Patrick J. Laub, Young Lee, Philip K. Pollett +1
The Hawkes process is a model for counting the number of arrivals to a system which exhibits the self-exciting property - that one arrival creates a heightened chance of further ar…
Exact simulation of extrinsic stress-release processes
Young Lee, Patrick J. Laub, Thomas Taimre +2
We present a new and straightforward algorithm that simulates exact sample paths for a generalized stress-release process. The computation of the exact law of the joint interarriva…
Approximate Bayesian Computations to fit and compare insurance loss models
Pierre-Olivier Goffard, Patrick J. Laub
Approximate Bayesian Computation (ABC) is a statistical learning technique to calibrate and select models by comparing observed data to simulated data. This technique bypasses the…