The paper deals with optimal portfolio choice problems when risk levels are given by coherent risk measures, expectation bounded risk measures or general deviations. Both …
The optimal reinsurance problem is a classic topic in actuarial mathematics. Recent approaches consider a coherent or expectation bounded risk measure and minimize the …
The objective of this paper is twofold. On the one hand, the optimal combination of reinsurance and financial investment will be studied under a general framework. Indeed …
K Uğurlu - Journal of Computational and Applied Mathematics, 2017 - Elsevier
In this paper, we consider the control problem with the Average-Value-at-Risk (AVaR) criteria of the possibly unbounded L 1-costs in infinite horizon on a Markov Decision Process …
H Assa, N Gospodinov - Decisions in Economics and Finance, 2018 - Springer
In this paper, we study market consistent valuations in imperfect markets. In the first part of the paper, we observe that in an imperfect market one needs to distinguish two type of …
K Uğurlu - Journal of Computational and Applied Mathematics, 2018 - Elsevier
We use one-step conditional risk mappings to formulate a risk averse version of a total cost problem on a controlled Markov process in discrete time infinite horizon. The nonnegative …
M Righi - Quantitative Finance, 2024 - Taylor & Francis
We expose a theoretical hedging optimization framework with variational preferences under convex risk measures. We explore a general dual representation for the composition …
H Assa, A Balbás - Mathematics and Financial Economics, 2011 - Springer
Abstract This work studies Good Deals in a scenario in which a firm uses decision-making tools based on a coherent risk measure, and in which the market prices are determined with …
H Assa, KM Karai - Journal of Optimization Theory and Applications, 2013 - Springer
In this paper, we will describe a framework that allows us to connect the problem of hedging a portfolio in finance to the existence of Pareto optimal allocations in economics. We will …