For more information about our consultancy, JSheep or JCALFget in touch.


Understand catastrophe events through statistical modelling

Underpinned by excellence, expertise and cutting-edge statistical methods, our statistics team offers an environmental consultancy service, specialising in linear, generalised linear and non-linear statistical modelling, and covariate effect analysis.

The team is experienced in the study of natural catastrophe events and their financial impact using advanced statistical models. These models contribute to most of the major components of a probabilistic catastrophe model, including hazard maps, stochastic event sets, vulnerability functions, and loss calculations.

In the past we have designed the “full uncertainty” simulation-based loss calculation used in JBA’s proprietary software, JCalf and contributed to the foundation of the loss calculation algorithm used by the Oasis Loss Modelling Framework which is changing the industry.


For re/insurers we offer

  • Statistical consultancy related to the construction of full probabilistic models, including quantification of uncertainty, model calibration, and model component validation
  • Development of a customised stochastic event set as an input to a probabilistic model, based on an advanced high-dimensional extremal dependence model
  • Provision of high-quality statistical inputs to hazard map or event footprint development, including growth curve estimation and spatial analysis based on extreme value theory
  • Advice on catastrophe event loss calculation and the tail risk estimation with respect to the solvency capital requirement in Solvency II



For environmental engineers

  • Statistical modelling of variables within a complex system where direct physical models are costly or unfeasible, e.g. large-scale rainfall-runoff models
  • Estimation of the probability of rare events which may or may not have been observed, e.g. the joint probability of extreme sea surge, high tide, and inland river flood
  • Quantification of the impact of a set of environmental variables on a variable of interest, e.g. the analysis of water usage and the quantification of the effectiveness of water companies’ control measures during droughts
  • Time series and forecasting models for stochastic processes