Yevhen Havrylenko

In research, I develop mathematical models that support decision-making processes of actuaries or better reflect reality and enable gaining new economic insights in the context of insurance or financial decisions. My research interests include decision theory, optimal control, and data science. As an educator, I teach Mathematics to bachelor students, Life Insurance to master students, and supervise theses on a range of topics in insurance and finance.

I was a DAAD PRIME Fellow (https://www.daad.de/en/studying-in-germany/scholarships/daad-funding-programmes/prime/) and conducted research in asset-liability management, decision-making theory, and data science with applications to insurance and finance.

I conducted research on the optimal investment-consumption strategies and on interpretable machine learning with applications to insurance.

I conducted 5 research project that resulted scientific publications publications and did 3 applied projects for ERGO & Munich Re (see section below). In addition, I supervised 3 Master theses, 3 seminar works and the TUM-ERGO Machine Learning team of 5 students. Finally, I co-organized 3 academic events, including the International Congress on Insurance: Mathematics and Economics 2019 in Munich.

Department “Global Property & Casualty Actuarial Pricing” Created a tool that recommends the next-best pairs of interacting variables for generalised model models (GLMs) Methodology is based on my research paper “Detection of interacting variables for GLMs via neural networks” co-authored with J. Heger Implementation: R (frontend) and Python (backend, mainly keras and tensorflow packages) Prepared technical as well as methodological documentation of the developed tool

Departmet "Strategic Asset Allocation". Created a tool for robust computation & analysis of relevant investment portfolios risks such as Market Value-at-Risk (VaR), Credit VaR, Foreign-Exchange VaR, Asset-Liability Mismatch Risk (ALMR). Tool functionality: total risk factor VaR & ALMR, disaggregated risks (e.g., for asset classes), marginal risks, robustification using L-estimators. Implementation: MATLAB (computation & plotting) and Excel (user interface).

Department "Strategic Asset Allocation". Analyzed algorithms suitable for clustering financial assets. Conducted clustering analysis of the asset universe of ERGO Group. Created a user-friendly Python-Excel clustering tool for asset allocation decision support.

Course in Financial Market Volatility. Key topics: - symmetric and asymmetric GARCH, CCC- and DCC-GARCH, orthogonal GARCH models - stochastic and local volatility models - volatility trading

Course in Statistics for Business Administration

Project centred around IRBA audit preparation of a German bank. Analyzed deviations between internal models and their prototypes to control credit risk. Corrected found errors using PL/SQL. Developed further the credit decision process prototype in PL/SQL.

Team "Credit Portfolio Risk Measurement & Methodology". Analyzed performance of algorithms for stochastic LGDs generation in C++. Developed and implemented in C++ an efficient algorithm for stochastic LGDs generation. Calibrated a structural credit portfolio model using R.

In my dissertation, I study the optimal investment and risk-sharing strategies for decision makers with constraints, e.g., insurance companies. I solve the corresponding portfolio-optimization problems by advancing existing methodologies to tackle the presence of risk-sharing mechanisms as well as constraints on terminal wealth or investment strategies. Link to dissertation: https://mediatum.ub.tum.de/?id=1692226

Mathematical finance: stochastic analysis, continuous time finance, fixed income markets, portfolio analysis. Statistics: time series analysis, computational statistics, generalized linear models. Optimization: nonlinear optimization advanced, optimization methods in machine learning

Foundations: algebra, mathematical analysis, discrete mathematics, probability theory, statistics. Applied mathematics: data analysis, operations research, numerical methods, decision making theory, econometrics. Computer science: programming (C++, C#), algorithms