Projects

Crunching mortality using stochastic models

Collaborators: Pavel V. Shevchenko, Uwe Schmock, Gareth W. Peters, Mario V. Wüthrich, J. Hirz, Man Chung Fung, Dorota Toczydlowska, Philippe Deprez

Publications

• Dorota Toczydlowska, Gareth W. Peters, Man Chung Fung, Pavel V. Shevchenko (2017).Stochastic period and cohort effect state-space mortality models incorporating demographic factors via probabilistic robust principal components. Risks 5, 42:1-42:77; doi:10.3390/risks5030042. Preprint https://ssrn.com/abstract=2977306.
• Philippe Deprez, Pavel V. Shevchenko and Mario V. Wüthrich (2017). Machine learning techniques for mortality modeling. European Actuarial Journal. DOI 10.1007/s13385-017-0152-4, Preprint  https://ssrn.com/abstract=2921841.
• J. Hirz, U. Schmock and P.V. Shevchenko (2017). Actuarial Applications and Estimation of extended CreditRisk+. Risks 5 (2), 23:1-23:29, doi:10.3390/risks5020023. Preprint, http://arxiv.org/abs/1505.04757
• M. C. Fung, G.W. Peters and P.V. Shevchenko (2017). A unified approach to mortality modelling using state-space framework: characterisation, identification, estimation and forecasting.  Annals of Actuarial Science. DOI: 10.1017/S1748499517000069. Preprint, https://ssrn.com/abstract=2786559
• J. Hirz, U. Schmock and P. Shevchenko (2017). Crunching mortality and life insurance portfolios with extended CreditRisk+. Risk Magazine, January 2017, pages 98-103. Preprint, http://ssrn.com/abstract=2717518

Resources

• Online calculation software

Collaborators: Pavel Shevchenko, Xiaolin Luo, Johan Andréasson, Alex Novikov, Jin Sun

Publications

Pricing retirement income products

• J. Sun, P.V. Shevchenko, M.C. Fung (2017). A Note on the Impact of Management Fees on the Pricing of Variable Annuity Guarantees. Preprint, http://ssrn.com/abstract=2967045
• P.V. Shevchenko and X. Luo (2017). Valuation of Variable Annuities with Guaranteed Minimum Withdrawal Benefit under Stochastic Interest Rate. Insurance: Mathematics and Economics 76, 104-117; doi: 10.1016/j.insmatheco.2017.06.008. Preprint http://arxiv.org/abs/1602.03238

Optimal decisions in retirement

• Andréasson, Johan G. and Shevchenko, Pavel V. (2017). Assessment of Policy Changes to Means-Tested Age Pension Using the Expected Utility Model: Implication for Decisions in Retirement. Risks 5, 47:1-47:21; doi:10.3390/risks5030047. Preprint https://ssrn.com/abstract=2875551.
• Johan G. Andréasson, Pavel V. Shevchenko, Alex Novikov (2017). Optimal consumption, investment and housing with means-tested public pension in retirement. Insurance: Mathematics and Economics 75, 32-47. Preprint, http://arxiv.org/abs/1606.08984.

Resources

• Optimal decisions in retirement, online calculation software
• Pricing variable annuity guarantees, online calculation software

Collaborators: Pavel Shevchenko, Xiaolin, Luo, Pier Del Moral

Relevant publications

P.V. Shevchenko and P. Del Moral (2017). Valuation of Barrier Options using Sequential Monte Carlo. Journal of Computational Finance 20(4), 107-135. DOI: 10.21314/JCF.2016.324. Preprint, http://arxiv.org/abs/1405.5294.

T. G. Ling and P.V. Shevchenko (2016). Historical Backtesting of Local Volatility Model using AUD/USD Vanilla Options. ANZIAM Journal 57, 319-338. DOI:10.1017/S1446181115000310. Preprint, http://arxiv.org/abs/1406.2133.

X. Luo and P.V. Shevchenko (2015). Pricing TARN Using a Finite Difference Method. The Journal of Derivatives 23 (1), 62-72. DOI: 10.3905/jod.2015.23.1.062. Preprint, http://arxiv.org/abs/1304.7563

P.V. Shevchenko (2003). Addressing the Bias in Monte Carlo Pricing of Multi-Asset Options With Multiple Barriers Through Discrete Sampling, The Journal of Computational Finance 6(3), 1-20. Preprint, http://arxiv.org/abs/0904.1157.

Resources

• Pricing Bermudan options: Online calculation software
• Pricing American options: Online calculation software
• Pricing Asian options: Online calculation software
• Pricing Barrier options: Online calculation software
• Pricing TARN: Online calculation software

Publications

• Peters, Gareth William and Shevchenko, Pavel V. and Cohen, Ruben D. and Maurice, Diane (2017). Statistical Machine Learning Analysis of Cyber Risk Data: Event Case Studies. Available at SSRN: https://ssrn.com/abstract=3073704. To appear in FinTech: Growth and Deregulation, RiskBooks, edited by Diane Maurice, Jack Freund and David Fairman.
• Peters, Gareth William and Shevchenko, Pavel V. and Cohen, Ruben D. and Maurice, Diane (2017). Understanding Cyber Risk and Cyber Insurance. Available at SSRN: https://ssrn.com/abstract=3065635. To appear in FinTech: Growth and Deregulation, RiskBooks, edited by Diane Maurice, Jack Freund and David Fairman.

Identifying pricing strategies for cyber insurance premiums

MRes Project: Mr Danny Wan, supervisor: A/Prof Christophe Doche, co-supervisor: Prof Pavel Shevchenko

Modelling Commodity Futures Prices

Team: Prof Pavel Shevchenko, Prof Gareth Peters,  Dr Matthew Ames, Dr Guillaume Bagnarosa, Prof Tomoko Matsui, Dr Nino Kordzakhia, Dr Karol Binkowski

Relevant publications:

• Ames, Matthew and Bagnarosa, Guillaume and Peters, Gareth William and Shevchenko, Pavel V. and Matsui, Tomoko (2016). Which Risk Factors Drive Oil Futures Price Curves? Speculation and Hedging in the Short and Long-Term. Preprint, available at SSRN: https://ssrn.com/abstract=2840730.
• G. W. Peters, M. Brier, P. Shevchenko and A. Doucet (2013). Calibration and filtering for multi factor commodity models with seasonality: incorporating panel data from futures contracts. Methodology and Computing in Applied Probability15(4), 841-874. Preprint, http://arxiv.org/abs/1105.5850.
• Karol Binkowski, P.V. Shevchenko and Nino Kordzakhia (2009). Modelling commodity prices. CSIRO technical report CMIS 09/43.

Modelling Optimal Portfolio Allocations

Team collaborators: Pavel Shevchenko, Gareth Peters,  Matthew Ames, Guillaume Bagnarosa, Tomoko Matsui, Spiridon Penev, Wei Wu

Relevant publications:

• M. Ames, G. Bagnarosa, G. Peters, P.V. Shevchenko and T.Matsui (2017). Forecasting Covariance for Optimal Carry Trade Portfolio Allocations. 41st ICASSP, IEEE international conference on Financial Signal Processing and Machine Learning for Electronic Trading, pp. 5910-5914. DOI: 10.1109/ICASSP.2017.7953290. Available on http://dx.doi.org/10.2139/ssrn.2711586.
• M. Ames, G. Bagnarosa, G.W. Peters, and P. V. Shevchenko (2017). Understanding the Interplay between Covariance Forecasting Factor Models and Risk Based Portfolio Allocations in Currency Carry Trades. To appear in Journal of Forecasting. Available at SSRN: http://ssrn.com/abstract=2699020.
• Spiridon Penev, Pavel Shevchenko, Wei Wu (2017). The impact of model risk on dynamic portfolio selection under multi-period mean-standard deviation criterion. Submitted.

Operational Risk Modelling

Books:

• M. G. Cruz, G.W. Peters and P.V. Shevchenko (2015). Fundamental Aspects of Operational Risk and Insurance Analytics: a Handbook of Operational Risk, Wiley.
• G.W. Peters and P.V. Shevchenko (2015). Advances in Heavy Tailed Risk Modeling: a Handbook of Operational Risk, Wiley.
• P.V. Shevchenko (2011). Modelling Operational Risk Using Bayesian Inference. Berlin, Springer.

White paper/media articles:

• Gareth W. Peters, Pavel Shevchenko, Bertrand Hassani and Ariane Chapelle (3 June 2016). Standardized Measurement approach for Operational Risk: Pros and Cons. Uploaded to Basel committee website www.bis.org, response to Basel Committee for Banking Supervision consultative document  “Standardised Measurement Approach for operational risk” issued in March 2016 for comments by 3 June 2016. https://www.bis.org/bcbs/publ/comments/d355/suefu.pdf and http://ssrn.com/abstract=2789006.
• Ariane Chapelle, Bertrand Hassani, Gareth W Peters, Evan Sekeris, Pavel Shevchenko (17 March 2016). Discarding the AMA could become a source of op risk. Published online on Risk.Net (response to Basel committee proposed changes to operational risk capital requirement formulas). http://www.risk.net/operational-risk-and-regulation/opinion/2451089/discarding-the-ama-could-become-a-source-of-op-risk

Book chapters

• P.V. Shevchenko (2014). Operational Risk. Chapter 7, pp. 119-140 in Investment Risk Management, edited by H. Kent Baker and Greg Filbeck, Oxford University Press, New York.
• P.V. Shevchenko and M.V. Wüthrich (2010). Operational Risk: Combining Internal Data, External Data and Expert Opinions. Chapter 13, pp. 401-437 in Rethinking Risk Measurement and Reporting, Volume II edited by Klaus Böcker, Risk Books, London.

Journal Papers

• G.W. Peters, P.V. Shevchenko, B. Hassani and A. Chapelle (2016). Should the advanced measurement approach be replaced with the standardized measurement approach for Operational Risk? Journal of Operational Risk 11(3), 1-49, DOI: 10.21314/JOP.2016.177, http://arxiv.org/abs/1607.02319
• G. W. Peters, R.S. Targino and P.V. Shevchenko (2013) Understanding Operational Risk Capital Approximations: First and Second Orders. The Journal of Governance and Regulation 2(3), 58-78. DOI: 10.22495/jgr_v2_i3_p6. Preprint, http://arxiv.org/abs/1303.2910
• P.V. Shevchenko and G. W. Peters (2013). Loss Distribution Approach for Operational Risk Capital Modelling under Basel II: Combining Different Data Sources for Risk Estimation. The Journal of Governance and Regulation 2(3), 33-57. DOI: 10.22495/jgr_v2_i3_p5. Preprint, http://arxiv.org/abs/1306.1882
• G.W. Peters, A.D. Byrnes and P.V. Shevchenko (2011). Impact of Insurance for Operational Risk: Is it worthwhile to insure or be insured for severe losses? Insurance: Mathematics and Economics, 48, 287-303. Preprint, http://arxiv.org/abs/1010.4406
• G. W. Peters, P.V. Shevchenko, M. Young and W. Yip (2011). Analytic Loss Distributional Approach Models for Operational Risk from the α-Stable Doubly Stochastic Compound Processes and Implications for Capital Allocation. Insurance Mathematics & Economics 49(3), 565-579. Preprint, http://arxiv.org/abs/1102.3582
• P.V. Shevchenko (2010). Calculation of aggregate loss distributions. The Journal of Operational Risk 5(2), 3-40. Preprint, http://arxiv.org/abs/1008.1108
• P. V. Shevchenko (2010). Implementing loss distribution approach for operational risk. Applied Stochastic Models in Business and Industry 26(3), 277-307. Preprint, http://arxiv.org/abs/0904.1805
• P.V. Shevchenko and G. Temnov (2009). Modelling operational risk data reported above time varying threshold. The Journal of Operational Risk 4(2), 19-42. Preprint, http://arxiv.org/abs/0904.4075
• X. Luo and P.V. Shevchenko (2009). Computing Tails of Compound Distributions using Direct Numerical Integration. The Journal of Computational Finance 13(2), 73-111. Preprint, http://arxiv.org/abs/0904.0830
• D. D. Lambrigger, P.V. Shevchenko and M. V. Wüthrich (2008). Data combination under Basel II and Solvency 2: Operational Risk goes Bayesian. The Bulletin of the French Actuaries (Bulletin Français d’Actuariat) 8(16), 4-13. Published version
• P.V. Shevchenko (2008). Estimation of Operational Risk Capital Charge under Parameter Uncertainty. The Journal of Operational Risk 3(1), 51-63. Preprint, http://arxiv.org/abs/0904.1771.  DOI: 10.21314/JOP.2008.039
• D. D. Lambrigger, P.V. Shevchenko and M. V. Wüthrich (2007). The Quantification of Operational Risk using Internal Data, Relevant External Data and Expert Opinions. The Journal of Operational Risk 2(3), 3-27. Preprint, http://arxiv.org/abs/0904.1361. DOI: 10.21314/JOP.2007.030
• X. Luo, P. V. Shevchenko and J.B. Donnelly (2007). Addressing Impact of Truncation and Parameter Uncertainty on Operational Risk Estimates. The Journal of Operational Risk 2(4), 3-26. Preprint, http://arxiv.org/abs/0904.2910. DOI: 10.21314/JOP.2007.034
• H. Bühlmann, P.V. Shevchenko and M. V. Wüthrich (2007). A “Toy” Model for Operational Risk Quantification using Credibility Theory. The Journal of Operational Risk 2(1), 3-19. Preprint, http://arxiv.org/abs/0904.1772. DOI: 10.21314/JOP.2007.023
• P.V. Shevchenko and M. V. Wüthrich (2006). The Structural Modelling of Operational Risk via the Bayesian Inference: Combining Loss Data with Expert Opinions. The Journal of Operational Risk 1(3), 3-26. Preprint, http://arxiv.org/abs/0904.1067

Johan G. Andréasson (2014-2017), Modelling optimal decisions in retirement using expected utility stochastic control theory.  Supervisors: Prof Pavel Shevchenko (Macquarie University), Prof Alex Novikov (University of Technology Sydney)

This research project aims to 1) develop a utility model that captures the characterstics of Australian retirees, and calibrate the model with empirical data, 2) investigate issues related to Australian means-tested pensions, 3) develop numerical methods maximizing expected utility model to find optimal decision for housing, investment into risky assets, consumption.

Publications: 

• Andréasson, J. G., Shevchenko, P. V., Novikov, A. (2017). Optimal consumption,
  investment and housing with means-tested public pension in retirement. Insurance: Mathematics and Economics 75, 32-47.
• Andréasson, J. G., Shevchenko, P. V. (2017). Assessment of policy changes to means-tested Age Pension using expected utility model: Implication for decisions in retirement. Preprint available at SSRN: 2979889.

Publications:

J. Sun, P.V. Shevchenko, M.C. Fung (2017). A Note on the Impact of Management Fees on the Pricing of Variable Annuity Guarantees. Preprint, http://ssrn.com/abstract=2967045.

Publications:

Master Thesis: Jie Zhu (2017). Advanced Monte Carlo Methods for Pricing Bermudan Options and their Applications in Real Options Analysis.

Publications:

Master Thesis: Danny Wan (2018). Identifying pricing strategies for cyber insurance premiums