arXiv subjects

Georgios I. Papayiannis

Publications and source records attributed to Georgios I. Papayiannis.

11 recordsLinked to original sources

arXiv2026

Barycentric model aggregation in the Wasserstein space of distributions and a variational approach to consistency

We study the problem of model aggregation within the Wasserstein space for probability measures on the real line. Given a fixed finite collection of candidate probability models, we consider the associated class of Wasserstein barycenters and develop a data-driven calibration framework in which the aggregation weights are statistically learned from empirical information associated with a target distribution. From a variational perspective based on $Γ$-convergence, we establish consistency of the resulting aggregation scheme, showing that empirical minimizers converge to the minimizers of the actual problem, along with the associated barycentric estimators, under mild conditions. The performance of the proposed method is evaluated through synthetic experiments and illustrated on a real dataset from a temperature monitoring network of sensors.

stat.ME↗

arXiv2026

A tail-shape actuarial index based on equal level relationships between Value at Risk and Expected Shortfall

We introduce a new actuarial tail-shape index, the $θ$-index, based on a probability equal level relationship between Value at Risk and Expected Shortfall. The index is defined at each tail probability level as the parameter value for which Value at Risk coincides with Flexible Expected Shortfall, that is a convex mixture of Expected Shortfall and the mean. This yields a level-dependent, scale-free measure of upper tail behaviour. We study basic theoretical properties of the $θ$-index and introduce a partial order for comparing loss distributions, characterized by the monotonicity of right-tail spread ratios. Additionally, the index leads to characterizations of the tail behaviour of a loss distribution as consistent to the generalized Pareto model, through a direct connection to the mean excess function. Moreover, we derive Euler risk contributions for the $θ$-index and use probability equal level relationships to compute Value at Risk allocations in a more stable way. Finally, the $θ$-index is examined as a diagnostic tool for distinguishing tail regimes and its capabilities are illustrated using the Danish fire insurance dataset.

q-fin.RM↗

arXiv2025

Static Hedging of Freight Rate Risk in the Shipping Market under Model Uncertainty

Freight rate derivatives constitute a very popular financial tool in shipping industry, that allows to the market participants and the individuals operating in the field, to reassure their financial positions against the risk occurred by the volatility of the freight rates. The special structure of the shipping market attracted the interest of both academics and practitioners, since pricing of the related traded options which are written on non-storable assets (i.e. the freight service) is not a trivial task. Management of freight risk is of major importance to preserve the viability of shipping operations, especially in periods where shocks appear in the world economy, which introduces uncertainty in the freight rate prices. In practice, the reduction of freight risk is almost exclusively performed by constructing hedging portfolios relying on freight rate options. These portfolios needs to be robust to the market uncertainties, i.e. to choose the portfolio which returns will be as less as it gets affected by the market changes. Especially, for time periods where the future states of the market (even in short term) are extremely ambiguous, i.e. there are a number of different scenarios that can occur, it is of great importance for the firms to decide robustly to these uncertainties. In this work, a framework for the robust treatment of model uncertainty in (a) modeling the freight rates dynamics employing the notion of Wasserstein barycenter and (b) in choosing the optimal hedging strategy for freight risk management, is proposed. A carefully designed simulation study in the discussed hedging problem, employing standard modelling approaches in freight rates literature, illustrates the capabilities of the proposed method with very satisfactory results in approximating the optimal strategy even in high noise cases.

q-fin.RM↗

arXiv2023

Probabilistic Scenario-Based Assessment of National Food Security Risks with Application to Egypt and Ethiopia

This study presents a novel approach to assessing food security risks at the national level, employing a probabilistic scenario-based framework that integrates both Shared Socioeconomic Pathways (SSP) and Representative Concentration Pathways (RCP). This innovative method allows each scenario, encompassing socio-economic and climate factors, to be treated as a model capable of generating diverse trajectories. This approach offers a more dynamic understanding of food security risks under varying future conditions. The paper details the methodologies employed, showcasing their applicability through a focused analysis of food security challenges in Egypt and Ethiopia, and underscores the importance of considering a spectrum of socio-economic and climatic factors in national food security assessments.

econ.EM↗

arXiv2023

Consensus group decision making under model uncertainty with a view towards environmental policy making

In this paper we propose a consensus group decision making scheme under model uncertainty consisting of an iterative two-stage procedure and based on the concept of Fréchet barycenter. Each step consists of two stages: the agents first update their position in the opinion metric space by a local barycenter characterized by the agents' immediate interactions and then a moderator makes a proposal in terms of a global barycenter, checking for consensus at each step. In cases of large heterogeneous groups the procedure can be complemented by an auxiliary initial homogenization step, consisting of a clustering procedure in opinion space, leading to large homogeneous groups for which the aforementioned procedure will be applied. The scheme is illustrated in examples motivated from environmental economics.

cs.MA↗

arXiv2023

Modelling of functional profiles and explainable shape shifts detection: An approach combining the notion of the Fréchet mean with the shape invariant model

A modelling framework suitable for detecting shape shifts in functional profiles combining the notion of Fréchet mean and the concept of deformation models is developed and proposed. The generalized mean sense offered by the Fréchet mean notion is employed to capture the typical pattern of the profiles under study, while the concept of deformation models, and in particular of the shape invariant model, allows for interpretable parameterizations of profile's deviations from the typical shape. EWMA-type control charts compatible with the functional nature of data and the employed deformation model are built and proposed, exploiting certain shape characteristics of the profiles under study with respect to the generalized mean sense, allowing for the identification of potential shifts concerning the shape and/or the deformation process. Potential shifts in the shape deformation process, are further distinguished to significant shifts with respect to amplitude and/or the phase of the profile under study. The proposed modelling and shift detection framework is implemented to a real world case study, where daily concentration profiles concerning air pollutants from an area in the city of Athens are modelled, while profiles indicating hazardous concentration levels are successfully identified in most of the cases.

stat.ME↗

arXiv2023

A Framework for Treating Model Uncertainty in the Asset Liability Management Problem

The problem of asset liability management (ALM) is a classic problem of the financial mathematics and of great interest for the banking institutions and insurance companies. Several formulations of this problem under various model settings have been studied under the Mean-Variance (MV) principle perspective. In this paper, the ALM problem is revisited under the context of model uncertainty in the one-stage framework. In practice, uncertainty issues appear to several aspects of the problem, e.g. liability process characteristics, market conditions, inflation rates, inside information effects, etc. A framework relying on the notion of the Wasserstein barycenter is presented which is able to treat robustly this type of ambiguities by appropriate handling the various information sources (models) and appropriately reformulating the relevant decision making problem. The proposed framework can be applied to a number of different model settings leading to the selection of investment portfolios that remain robust to the various uncertainties appearing in the market. The paper is concluded with a numerical experiment for a static version of the ALM problem, employing standard modelling approaches, illustrating the capabilities of the proposed method with very satisfactory results in retrieving the true optimal strategy even in high noise cases.

q-fin.PM↗

arXiv2023

An optimal control problem with state constraints in a spatio-temporal economic growth model on networks

We introduce a spatial economic growth model where space is described as a network of interconnected geographic locations and we study a corresponding finite-dimensional optimal control problem on a graph with state constraints. Economic growth models on networks are motivated by the nature of spatial economic data, which naturally possess a graph-like structure: this fact makes these models well-suited for numerical implementation and calibration. The network setting is different from the one adopted in the related literature, where space is modeled as a subset of a Euclidean space, which gives rise to infinite dimensional optimal control problems. After introducing the model and the related control problem, we prove existence and uniqueness of an optimal control and a regularity result for the value function, which sets up the basis for a deeper study of the optimal strategies. Then, we focus on specific cases where it is possible to find, under suitable assumptions, an explicit solution of the control problem. Finally, we discuss the cases of networks of two and three geographic locations.

math.OC↗

arXiv2022

Convex Risk Measures for the Aggregation of Multiple Information Sources and Applications in Insurance

We propose a novel class of convex risk measures, based on the concept of the Fréchet mean, designed in order to handle uncertainty which arises from multiple information sources regarding the risk factors of interest. The proposed risk measures robustly characterize the exposure of the firm, by filtering out appropriately the partial information available in individual sources into an aggregate model for the risk factors of interest. Importantly, the proposed risks can be expressed in closed analytic forms allowing for interesting qualitative interpretations as well as comparative statics and thus facilitate their use in the everyday risk management process of the insurance firms. The potential use of the proposed risk measures in insurance is illustrated by two concrete applications, capital risk allocation and premia calculation under uncertainty.

q-fin.RM↗

arXiv2022

A Robust Decision Making Framework for Optimal Strategy Selection in Warfare under Model Uncertainty

In this paper is presented a framework for treating uncertainty in optimal decision problems occuring in combat situations, in order to robustly select the optimal strategy. A stochastic version of the popular Lanchester's aimed-fire model is considered as the underlying combat system describing the combet dynamics, and upon this an optimal decision rule for allocating forces is constructed. This approach results to a very extendable optimal decision framework, where the optimal strategy is chosen by simultaneously treating robustly uncertainty regarding critical combat parameters.

math.OC↗

arXiv2022

Robust Policy Selection and Harvest Risk Quantification for Natural Resources Management under Model Uncertainty

In this work the problem of optimal harvesting policy selection for natural resources management under model uncertainty is investigated. Under the framework of the neoclassical growth model dynamics, the associated optimal control problem is investigated by introducing the concept of model uncertainty on the initial conditions of the operational procedure. At this stage, the notion of convex risk measures, and in particular the class of Fréchet risk measures, is employed in order to quantify the total operational and marginal risk, whereas simultaneously obtaining robust to model uncertainty harvesting strategies.

math.OC↗