no code implementations • 5 Feb 2024 • Chi Truong, Matteo Malavasi, Han Li, Stefan Trueck, Pavel V. Shevchenko
We model the severity of extreme sea level events using the block maxima approach from extreme value theory, and then develop a real options framework, factoring in climate change, sea level rise uncertainty, and the growth in asset exposure.
no code implementations • 10 Oct 2023 • Aleksandar Arandjelović, Geoffrey Kingston, Pavel V. Shevchenko
A key finding of the literature is that the demand for life insurance and the demand for life annuities are symmetrical.
no code implementations • 22 Feb 2022 • Gareth W. Peters, Matteo Malavasi, Georgy Sofronov, Pavel V. Shevchenko, Stefan Trück, Jiwook Jang
We argue that the choice of such methods is akin to a form of model risk and we study the risk sensitivity that arise from choices relating to the class of robust estimation adopted and the impact of the settings associated with such methods on key actuarial tasks such as premium calculation in cyber insurance.
no code implementations • 21 Feb 2022 • Pavel V. Shevchenko, Jiwook Jang, Matteo Malavasi, Gareth W. Peters, Georgy Sofronov, Stefan Trück
In this study we examine the nature of losses from cyber related events across different risk categories and business sectors.
1 code implementation • 28 Dec 2021 • Aleksandar Arandjelović, Thorsten Rheinländer, Pavel V. Shevchenko
We study the problem of reducing the variance of Monte Carlo estimators through performing suitable changes of the sampling measure which are induced by feedforward neural networks.
no code implementations • 5 Nov 2021 • Matteo Malavasi, Gareth W. Peters, Pavel V. Shevchenko, Stefan Trück, Jiwook Jang, Georgy Sofronov
We address these questions through a combination of regression models based on the class of Generalised Additive Models for Location Shape and Scale (GAMLSS) and a class of ordinal regressions.
no code implementations • 1 Nov 2021 • Pavel V. Shevchenko, Daisuke Murakami, Tomoko Matsui, Tor A. Myrvoll
We reformulate and solve the DICE model as an optimal control dynamic programming problem with six state variables (related to the carbon concentration, temperature, and economic capital) evolving over time deterministically and affected by two controls (carbon emission mitigation rate and consumption).
no code implementations • 5 Aug 2021 • Spiridon Penev, Pavel V. Shevchenko, Wei Wu
In the worst case scenario, the optimal robust strategy can be obtained in a semi-analytical form as a solution of a system of nonlinear equations.
no code implementations • 24 Sep 2020 • Dorota Toczydlowska, Gareth W. Peters, Pavel V. Shevchenko
We propose a novel generalisation to the Student-t Probabilistic Principal Component methodology which: (1) accounts for an asymmetric distribution of the observation data; (2) is a framework for grouped and generalised multiple-degree-of-freedom structures, which provides a more flexible approach to modelling groups of marginal tail dependence in the observation data; and (3) separates the tail effect of the error terms and factors.
no code implementations • 27 Aug 2019 • Andreas Lichtenstern, Pavel V. Shevchenko, Rudi Zagst
In this article we solve the problem of maximizing the expected utility of future consumption and terminal wealth to determine the optimal pension or life-cycle fund strategy for a cohort of pension fund investors.
no code implementations • 4 Jun 2019 • Jin Sun, Kevin Fergusson, Eckhard Platen, Pavel V. Shevchenko
We consider two pricing approaches, the classical risk-neutral approach and the benchmark approach, and we examine the associated static and optimal behaviors of both the investor and insurer.