Search arXiv⌕ Search

arXiv · 1807.06892

A unifying approach to constrained and unconstrained optimal reinsurance

Abstract

In this paper, we study two classes of optimal reinsurance models from perspectives of both insurers and reinsurers by minimizing their convex combination where the risk is measured by a distortion risk measure and the premium is given by a distortion premium principle. Firstly, we show that how optimal reinsurance models for the unconstrained optimization problem and constrained optimization problems can be formulated in a unified way. Secondly, we propose a geometric approach to solve optimal reinsurance problems directly. This paper considers a class of increasing convex ceded loss functions and derives the explicit solutions of the optimal reinsurance which can be in forms of quota-share, stop-loss, change-loss, the combination of quota-share and change-loss or the combination of change-loss and change-loss with different retentions. Finally, we consider two specific cases: Value at Risk (VaR) and Tail Value at Risk (TVaR).

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yuxia Huang, Chuancun Yin. 2018-07-18. A unifying approach to constrained and unconstrained optimal reinsurance. https://arxiv.org/abs/1807.06892

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

A multi-view contrastive learning framework for spatial embeddings in risk modelling

Incorporating spatial information, particularly when related to climate, weather, and demographic factors, is crucial for improving underwriting precision and enhancing risk management in insurance. However, spatial data are often unstructured, high-dimensional, and difficult to integrate into predictive models. Embedding methods are needed to convert spatial data into meaningful representations for modelling tasks. We propose a novel multi-view contrastive learning framework for generating spatial embeddings that combine information from multiple spatial data sources. To train the model, we construct a spatial dataset that merges satellite imagery and OpenStreetMap features across Europe. The framework aligns these spatial views with coordinate-based encodings, producing low-dimensional embeddings that capture both spatial structure and contextual similarity. Once trained, the model generates embeddings directly from latitude-longitude pairs, enabling any dataset with coordinates to be enriched with meaningful spatial features without requiring access to the original spatial inputs. In a case study on French real estate prices, we compare models trained on raw coordinates against those using our spatial embeddings as inputs. The embeddings consistently improve predictive accuracy across generalised linear, additive, and boosting models, while providing post-hoc explainable spatial effects and demonstrating generalisation of the fitted spatial effects to regions without training observations. A second case study on flood claim counts across Belgian postal codes confirms that the embeddings improve territorial risk classification in an insurance context.

q-fin.RM↗

VaR at Its Extremes: Impossibilities and Conditions for One-Sided Random Variables

Value-at-Risk (VaR) may reward diversification at some probability levels and penalize it at others. We study the two extremal regimes in which one effect prevails at every level simultaneously, for sums of one-sided risks. For risks supported on $[0,\infty)$, VaR sub-additivity at all levels, exact VaR additivity and co-monotonicity are equivalent without any integrability assumption; for such risks this removes the finite-mean hypothesis of Imamura and Kato (2026), which cannot be dropped for two-sided risks. Global super-additivity, in turn, is equivalent to first-order stochastic dominance of the aggregate over its co-monotonic counterpart. We factorize this condition through the maximum of independent copies of the margins into negative simplex dependence (NSD), a condition on the joint law that is strictly weaker than negative lower orthant dependence, and simplex dominance (SD) of the aggregator $Φ(x_1,\dots,x_n)=\sum_{i=1}^n x_i\log F_{X_i}(x_i)$, which for continuous margins holds whenever each reciprocal risk $1/X_i$ has a decreasing failure rate on average. The criterion admits heterogeneous margins, yields a lower bound on the diversification penalty that depends on the margins only, is exact for unit Fréchet margins, and accommodates positively and tail-dependent risks such as common-shock models. Independently of any factorization, a vector that is VaR super-additive but not VaR additive has at least two infinite-mean components, and this bound is sharp. A reflection principle extends all results to finite endpoints: lower-bounded risks are never strictly sub-additive, upper-bounded risks are never strictly super-additive, and compactly supported risks are neither.

q-fin.RM↗

A tale of two allocations: Risk capital contributions versus risk contributions in the tail

We compare two natural proportional notions of a risk component's contribution to the aggregate tail risk of a collection of risks: the fraction of aggregate tail risk capital allocated to the component under Conditional Tail Expectation (CTE), and the component's expected realized share of aggregate risk under Geometric Tail Expectation (GTE). The resulting proportional allocations generally differ. For arbitrary random vectors-allowing atoms, signed risks, and any tail domain--we establish when the allocations agree, determine their ordering when they do not, and characterize their asymptotic separation. Both proportional allocations are weighted averages of the conditional risk share of a component given the aggregate: the proportional CTE allocation weights tail scenarios by severity, whereas the proportional GTE allocation weights them uniformly. Their difference is therefore a normalized tail covariance. The proportional CTE and GTE allocations agree throughout a tail exactly when the conditional risk share is constant there; under a mild unimodality condition, dominance is characterized by its monotonicity. Among independent exponential dispersion models with heterogeneous natural parameters, exact agreement is possible only for the scaled Poisson family; under comonotonicity, it is equivalent to proportional quantile functions. In the extreme tail, the limiting relationship between the proportional CTE and GTE allocations is governed by the ratio of Expected Shortfall to Value-at-Risk: boundedness ensures common limits, convergence to one forces the allocations to merge, and divergence can cause their limits to separate.

q-fin.RM↗