Selected preprints

• Algorithmic trading and stochastic integration
  (with Uwe Schmock) [arXiv]
  arXiv:2609.31578, 2026.
• On the approximation of posterior laws in compound loss models by conditional Wasserstein GANs
  (with Pavel V. Shevchenko and George Tzougas) [arXiv]
  arXiv:2608.27229, 2026.
• Optimal investment, consumption, and insurance with durable goods under stochastic depreciation risk
  (with Ryle S. Perera, Pavel V. Shevchenko, Tak Kuen Siu and Jin Sun) [arXiv]
  arXiv:1903.00631v2, 2025.

Refereed journal articles

5. Solving stochastic climate-economy models: a deep least-squares Monte Carlo approach
   (with Pavel V. Shevchenko, Tomoko Matsui, Daisuke Murakami and Tor A. Myrvoll) [DOI] [arXiv]
   Mathematical Finance 36(4), 2026, 751–770.
6. Optimal risk mitigation by deep reinsurance
   (with Julia Eisenberg) [DOI] [arXiv]
   North American Actuarial Journal 30(3), 2026, 627–639.
7. The impact of climate change on reserves in life insurance
   (with Pavel V. Shevchenko) [DOI] [SSRN]
   European Actuarial Journal, 2026, advance online publication.
8. Importance sampling for option pricing with feedforward neural networks
   (with Thorsten Rheinländer and Pavel V. Shevchenko) [DOI] [arXiv]
   Finance and Stochastics 29(1), 2025, 97–141.
9. Life cycle insurance, bequest motives and annuity loads
   (with Geoffrey Kingston and Pavel V. Shevchenko) [DOI] [arXiv]
   Journal of Economic Dynamics and Control 157, 2023, Article 104759.

Theses

• Theory of neural networks with applications in finance, insurance and climate-economy modelling
  Cotutelle doctoral thesis, TU Wien and Macquarie University, 2024. [DOI]
• Elements of large deviations theory for Banach-space-valued Brownian motion and Ciesielski's isomorphism in weighted Hölder spaces
  Master's thesis, TU Wien, 2020. [DOI]