By Dr Gregory Clive Taylor

Preprints

Avanzi B; Taylor G; Wong B; Xian A, 2019, Modelling and Understanding Count Processes Through a Markov-Modulated Non-Homogeneous Poisson Process Framework, http://dx.doi.org/10.2139/ssrn.3354342

Taylor G, 2018, An Iterated Bornhuetter-Ferguson Model, http://dx.doi.org/10.2139/ssrn.3225556

McGuire G; Taylor G; Miller H, 2018, Self-Assembling Insurance Claim Models Using Regularized Regression and Machine Learning, http://dx.doi.org/10.2139/ssrn.3241906

Taylor G, 2018, Observations on Industry Practice in the Construction of Large Correlation Structures for Risk and Capital Margins, http://dx.doi.org/10.2139/ssrn.3113756

Avanzi B; Taylor G; Wong B, 2016, Common Shock Models for Claim Arrays, http://dx.doi.org/10.2139/ssrn.2881058

Avanzi B; Taylor G; Vu PA; Wong B, 2016, Stochastic Loss Reserving with Dependence: A Flexible Multivariate Tweedie Approach, http://dx.doi.org/10.2139/ssrn.2753540

Taylor G, 2015, Existence and Uniqueness of Chain Ladder Solutions, http://dx.doi.org/10.2139/ssrn.2660053

Taylor G, A Cape Cod Model for the Exponential Dispersion Family, http://dx.doi.org/10.2139/ssrn.3201982

Avanzi B; Taylor G; Vu PA; Wong B, A Multivariate Evolutionary Generalised Linear Model Framework with Adaptive Estimation for Claims Reserving, http://dx.doi.org/10.2139/ssrn.3413016

Avanzi B; Taylor G; Wong B; Yang X, A Multivariate Micro-Level Insurance Counts Model With a Cox Process Approach, http://dx.doi.org/10.2139/ssrn.3354434

Taylor G, Claim Models: Granular and Machine Learning Forms, http://dx.doi.org/10.2139/ssrn.3387702

Avanzi B; Taylor G; Vu PA; Wong B, On Unbalanced Data and Common Shock Models in Stochastic Loss Reserving, http://dx.doi.org/10.2139/ssrn.3303255

Other

Avanzi B; Taylor G; Wong B, 2014, Research into claim dependencies: an industry and academic collaboration, Actuaries Institute, http://www.actuaries.digital/2014/08/15/research-into-claim-dependencies-an-industry-and-academic-collaboration/

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