Aniketh Pittea

PhD student in Actuarial Science


Aniketh is a Kent alumnus who completed the MSc in Applied Actuarial Science in 2013. He started his PhD in January 2016 and is supervised by Dr Pradip Tapadar.

Research interests

Impact of changing population demographics on pension plans


Grants received

  • Awarded a grant by the organisers of the Insurance Data Science conference covering conference fees. The conference took place in July 2018 at Cass Business School.​
  • Awarded a grant of €1,000 by the organisers of the International Congress of Actuaries (ICA2018) to attend and give a talk on Examining Pension Plan Risk in an Economic Capital Framework. The Congress took place in Berlin in June 2018.
  • Awarded a travel grant by the organisers of the Doctoral Colloquium - Queen’s Management School covering travel and other costs to give a talk on Risk Assessment of Pension Schemes. The Colloquium took place June 2017.
  • Awarded a grant by the Institute and Faculty of Actuaries covering travel and other costs to give a talk for the Actuarial Teachers' and Researchers' Conference. The conference took place at the University of East Anglia in July 2016.
  • Awarded a grant by the Institute and Faculty of Actuaries covering travel and other costs to give a talk at the University of Waterloo, Ontario. The talk was about mortality and pension modelling and took place in June 2016.

Conferences attended



  • Oberoi, J., Pittea, A. and Tapadar, P. (2019). A graphical model approach to simulating economic variables over long horizons. Annals of Actuarial Science.
    We present an application of statistical graphical models to simulate economic variables for the purpose of risk calculations over long time horizons. We show that this approach is relatively easy to implement, and argue that it is appealing because of the transparent yet flexible means of achieving dimension reduction when many variables must be modelled. Using United Kingdom data as an example, we demonstrate the development of an economic scenario generator that can be used by life insurance companies and pension funds. We compare different algorithms to select a graphical model, based on p-values, AIC, BIC, and deviance. We find them to yield reasonable results and relatively stable structures in our example, suggesting that it would be beneficial for actuaries to include these models in their toolkit.
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