Athanasios Pantelous

Assoc Professor

Accepting PhD Students

20072023
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Personal profile

Biography

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Associate Professor Athanasios (Thanasi) Pantelous received his Bachelor degree in Mathematics (2001), two-year M.Sc. in Statistics and Operational Research (2005) and M.Sc. in Applied Mathematics (2007) degrees from the Department of Mathematics at National and Kapodistrian University of Athens (NKUA) in Greece (2001), his M.Sc. in Statistics: Statistical methods applied to the Management of Insurance Organizations (2006) and Ph.D. in Statistics (2008) degrees from the Department of Statistics at Athens University of Economics and Business (AUEB), Greece. In 2013, he completed his 2nd PhD degree in Modelling and Systems Science from the School of Mathematics, Computer Science and Engineering at City, University of London, UK. In 2009, he started his academic career as a Lecturer (2009) and Reader (2011) at the Department of Mathematical Sciences and the Institute for Risk and Uncertainty at the University of Liverpool, UK. In October 2017, Dr Pantelous joined Monash University, Australia, as an Associate Professor at the Department of Econometrics and Business Statistics.

Dr. Pantelous is an academic member of RiskLab Australia since January 2018. 

Dr. Pantelous’ primary research interests focus on the general area of quantitative research and mathematical modeling under risk and uncertainty with an emphasis on quantitative and behavioral finance, actuarial science, financial econometrics and mathematics, computational stochastic mechanics and finance as well as operational research. His theoretical mathematical developments have often found diverse applications in finance, insurance and engineering.

Dr. Pantelous has published more than 140 technical papers in peer-reviewed international journals and conference proceedings. He has served in the scientific and/or organizing committees of several international technical conferences, and has co-founded and chaired the Quantitative Finance and Risk Analysis (QFRA) symposium series. He is also an invited Visiting Professor in the School of Management at Shanghai University (China) and Associate Director of Quantitative Finance and Risk Analysis in the Center of Technology and Systems Management at University of Maryland (US). He is currently an Associate Editor of the ASCE-ASME J. of Risk and Uncertainty in Engineering Systems, and has served as a Guest Editor for several special issues in international journals (including, Quantitative Finance, Annals of Operations Research, International Journal of Finance & Economics).

He co-leaded, as Deputy-Director and co-Investigator, the EPSRC and ESRC Center for Doctoral Training (CDT) in Quantification and Management of Risk & Uncertainty in Complex Systems & Environments (2014-2017) in the University of Liverpool, UK. This research and training center has attracted a total funding volume of £21m and involves more than 36 industrial and academic partners from around the globe.

Furthermore, he is certified at the level of Middle Manager (Finance/Insurance) from the Hellenic Institute of Insurance Studies (H.I.I.S.). He has qualified in “Financial Mathematics”, “Risk Theory”, “Investment and Portfolio Theory” and “Survival Models and Life Tables” from the Hellenic Actuarial Society (Recognizable from IFoA, U.K. & SOA, U.S.).

Finally, he has supervised successfully 14 PhD students in the University of Liverpool and Xi'an Jiaotong-Liverpool University (2009 - 2018). Currently, he is supervising 6 PhD students as primary and 2 as secondary (associate) supervisor. 

Supervision interests

PhD Research Project 1: Pricing in a competitive stochastic insurance market.

Commencement Date: Anytime 2018, 3 year scholarship (Monash University or Data61 CSIRO)

Supervisory Team:

Athanasios A. Pantelous, Colin O’Hare (Monash University & RiskLab, Australia),

Tim J. Boonen (University of Amsterdam, The Netherlands)

Proposal 

There is a classic actuarial approach towards the pricing of insurance risk. In particular, the expected value premium principle, or the risk-based principle gained particular academic and practitioner’s interest (see for instance, Kaas et al. 2005). These principles need an approximation of the underlying distribution of insurance risk (claim sizes and timings), but they do not take into account the other insurers in the market. In economic theory on industrial organizations, competition, between alternative insurers in our case, may drive prices down, as observed in the well-known Bertrand competition. In this project, we aim to model the competition of multiple insurers in the market, where the insurance risk is observed and stochastic and all insurers aim to optimize a mean-variance objective function. In particular, we determine the prices as the Nash equilibria in the market. Focusing on a two-period model initially, we wish to see if competition, indeed, leads to lower prices and more insured risk for the insurer. This will allow us to simulate the distribution of the net asset value (also called basic own funds in Solvency II regulation) of the insurance companies. Also, we hypothesize that diversification of insurance policies yield an equilibrium where there is one insurer that attracts almost all policyholders. If the insured risk is too high, there is an interest for the policyholders (and thus the regulator) to introduce competition constraints (such as one preventing a monopole). This offset between regulation and diversification is non-trivial. Our approach extends the deterministic approaches of Taylor (1986) and Wu and Pantelous (2017), where there is no uncertainty in the pay-out of insurance policies. In a deterministic setting, the (risk-based) regulation is essentially irrelevant. Next, we aim to extend our results to a dynamic continuous-time setting. In this setting, open-loop Nash equilibria are studied to determine the premium profile, extending the approach of Boonen et al. (2018) to the case of stochastic insurance risk. This project is computationally more advanced than the first one, but it may allow us to understand what pricing profiles can be expected in stochastic insurance markets. For instance, Boonen et al. (2018) provide a deterministic example with premium cycles. Premium cycles are well-studied empirically, and exist in some insurance markets.

Bibliography 

  1. Boonen, T.J., Pantelous, A.A., Wu, R. Non-cooperative dynamic games for general insurance markets, Insurance: Mathematics and Economics, 78 (2018), 123-135.
  2. Kaas, R., Goovaerts, M., Dhaene, J. and Denuit, M.. Modern actuarial risk theory: using R (Vol. 128). Springer Science & Business Media, 2008.
  3. Taylor, G.C. Underwriting Strategy in a Competitive Insurance Environment. Insurance: Mathematics and Economics, 5 (1986), 59-77.
  4. Wu, R., Pantelous, A.A. Potential games with aggregation in non-cooperative general insurance markets, ASTIN Bulletin, 34(1) (2017), 269-302.

 

PhD Research Project 2: The impact of climate change in pricing and reserve in a competitive insurance market 

Commencement Date: Anytime 2018, 3 year scholarship (Monash University or Data61 CSIRO)

Supervisory Team: 

Athanasios A. Pantelous and Colin O’Hare (Monash University & RiskLab, Australia),

Tim J. Boonen (University of Amsterdam, The Netherlands)

Proposal 

Climate change is an important underlying risk factor that might have an impact on the insurance industry, pension funds, the financial sector (as a significant proportion of the financial markets is driven by pension funds) governmental agencies, and decision and policy makers. In the insurance industry, strong market competition has boosted the demand for a competitive premium, where competition drives premiums down. The insurance premium has a substantial impact on the actuarial reserving calculations and on the implementations by the regulatory authorities.

In this project, we have the following objectives. First, we want to identify and categorize the major risk segments for life and non-life insurers, and understand the way these risk segments are affected by climate change. Following this, the impact of uncertainty on the various parameters involved in the applied model will be examined. Secondly, we will model premium dynamics via differential games, and study the insurers’ equilibrium premium dynamics in a competitive market. In this regard, not only will different tools from optimal control, mathematical programming and economic theory be applied to determine the equilibrium premium strategies, but also, we will consider different market conditions. Finally, we will check which of the parameters involved in the model are most sensitive for climate change, focusing on how uncertainty in these parameters may impact on insurance equilibrium pricing and reserving. Additionally, we will demonstrate the impact of these projections on various financial calculations, and will provide a number of ways of quantifying, both graphically and numerically, the model risk in such calculations.

For the purpose of our study, data from the Australian and EU insurance markets will be considered. We focus on temperature changes as our proxy to climate change, while extensions can be obtained by including other important climate change factors such as (lack of) rainfall. A large number of simulation case studies will be conducted. In particular, an objective of our study is to quantify the sensitivity of the profit of the insurers towards the uncertainty of the long-term trend of climate change.

Selected References:

  1. Bobb, J.F, Peng, R.D., Bell, M.L., Dominici, F. Heat-related mortality and adaptation to heat in the United States. Environmental Health Perspectives, 122 (8) (2014), 811-816.
  2. Boonen, T.J., Pantelous, A.A., Wu, R. Non-cooperative dynamic games for general insurance markets, Insurance: Mathematics and Economics, 78 (2018), 123-135.
  3. Seklecka, M., Pantelous, A.A., O’Hare, C. Mortality effects of temperature changes in the United Kingdom, Journal of Forecasting, 36 (2017), 824-841.
  4. Vardoulakis, S., Dear, K., Hajat, S., Heaviside, C., Eggen, B., McMichael, A. J.. Comparative assessment of the effects of climate change on heat- and cold-related mortality in the United Kingdom and Australia. Environmental Health Perspectives122 (12) (2014), 1285–1293.
  5. Wu, R., Pantelous, A.A. Potential games with aggregation in non-cooperative general insurance markets, ASTIN Bulletin, 34(1) (2017), 269-302.

Keywords

  • Quantitative methods
  • Behavioural Finance
  • Computational Mechanics
  • Actuarial Science
  • Quantitative Finance
  • Network Theory
  • Computational Finance
  • Financial Econometrics
  • Risk analysis and management
  • Pricing
  • Operational Research
  • Financial Mathematics

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Projects 2014 2023

Research Output 2007 2018

A closed form approximation and error quantification for the response transition probability density function of a class of stochastic differential equations

Meimaris, A. T., Kougioumtzoglou, I. A. & Pantelous, A. A. 20 Jun 2018 In : Probabilistic Engineering Mechanics. 54, p. 87-94 8 p.

Research output: Contribution to journalArticleResearchpeer-review

A dynamic analysis of S&P 500, FTSE 100 and EURO STOXX 50 indices under different exchange rates

Chen, Y., Mantegna, R. N., Pantelous, A. A. & Zuev, K. M. 1 Mar 2018 In : PLoS ONE. 13, 3, 40 p., e0194067

Research output: Contribution to journalArticleResearchpeer-review

Open Access
File

Affordable levels of house prices using fuzzy linear regression analysis:the case of Shanghai

Zhou, J., Zhang, H., Gu, Y. & Pantelous, A. A. Aug 2018 In : Soft Computing. 22, 16, p. 5407-5418 12 p.

Research output: Contribution to journalArticleResearchpeer-review

An effective solution approach to fuzzy programming with application to project scheduling

Zhang, Q., Zhou, J., Wang, K. & Pantelous, A. A. Sep 2018 (Accepted/In press) In : International Journal of Fuzzy Systems. 16 p.

Research output: Contribution to journalArticleResearchpeer-review

Approximate analytical solutions for a class of nonlinear stochastic differential equations

Meimaris, A. T., Kougioumtzoglou, I. A. & Pantelous, A. A. 1 Sep 2018 (Accepted/In press) In : European Journal of Applied Mathematics. 17 p.

Research output: Contribution to journalArticleResearchpeer-review

Press / Media