New methods improve insurance data quality for catastrophic events.
problem Improving precision and size of insurance data for catastrophic events.
method Bootstrap, bootknife, and GAN algorithms.
result Compared MSE and MAE of simulated outputs, direct algorithm for fuzzy expert opinion.
We consider an optimal control problem of a property insurance company with proportional reinsurance strategy. The insurance business brings in catastrophe risk, such as earthquake and flood. The catastrophe risk could be partly reduced by reinsurance. The management of the company controls the reinsurance rate and div…
We propose a model for an insurance loss index and the claims process of a single insurance company holding a fraction of the total number of contracts that captures both ordinary losses and losses due to catastrophes. In this model we price a catastrophe derivative by the method of utility indifference pricing. The as…
The study examines insurance demand under rough volatility and path-dependent shocks.
problem Optimal insurance and investment strategies under rough volatility and path-dependent shocks.
method Rough volatility model and Hawkes process with power kernel, Functional Ito formula extension.
result Individuals demand more catastrophe insurance when path-dependent effects are considered.
Study optimal dividend strategies for insurers with natural catastrophe claims.
problem Maximizing dividends for a catastrophe insurer over its lifetime.
method Two-dimensional stochastic control problem, viscosity solutions, numerical approximation.
result Optimal dividend strategies identified for natural catastrophe insurers.
Study proposes a tax-based system to share disaster risk among regions.
problem Systemic risk in catastrophic events and insurer insolvency.
method Public-private partnership with government intervention through taxation.
result Taxation system effectively shares residual claims in case of insurer insolvency.
Optimizes diversification in catastrophe risk pooling using asymptotic analysis.
problem Maximizing diversification benefit from catastrophic events in insurance pools.
method Asymptotic analysis to solve high-dimensional optimization problem.
result Derives an asymptotically optimal pool that approximates practical optimal pool.
Quantum method speeds up risk estimation for insurance tail risks.
problem Sample-sparsity in classical Monte Carlo methods for tail risk pricing.
method Quantum Amplitude Estimation (QAE) with Grover amplification.
result Quantum method achieves convergence approaching order reciprocal N, enabling high-resolution tail estimation within practical budgets.
This paper analyzes extreme flooding risks and proposes insurance and bond solutions.
problem Severe rise in magnitude and frequency of floods causing catastrophic losses.
method Extremes analysis using Peaks-Over-Threshold method and Point Process model; Value-at-Risk (VaR) and Conditional VaR (CVaR) estimation; Flood zoning insurance and catastrophic bond design.
result Developed flood risk vulnerability and threat analysis considering geography and economic factors; Proposed flood zoning insurance and catastrophic bond design.
Recently, a marked Poisson process (MPP) model for life catastrophe risk was proposed in [6]. We provide a justification and further support for the model by considering more general Poisson point processes in the context of extreme value theory (EVT), and basing the choice of model on statistical tests and model compa…
Catastrophe risk is a major threat faced by individuals, companies, and entire economies. Catastrophe (CAT) bonds have emerged as a method to offset this risk and a corresponding literature has developed that attempts to provide a market-consistent pricing methodology for these and other long-dated, insurance-type cont…
This note presents a kind of the strong law of large numbers for an insurance risk caused by a single catastrophic event rather than by an accumulation of independent and identically distributed risks. We derive this result by a large diversification effect resulting from optimal allocation of the risk to many reinsure…
Research simulates Lloyd's of London's specialty insurance market dynamics.
problem Quantitative study of complex market phenomena in Lloyd's of London.
method Discrete Event Simulation (DES) framework for Lloyd's of London specialty insurance market.
result Model shows sophisticated exposure management reduces syndicate insolvency, and syndication enhances actuarial price accuracy.
Climate extreme events are constantly increasing. What is the effect of these potentially catastrophic events on insurance demand in Italy, with particular reference to the economic activities? Extreme precipitation events over most of the midlatitude land masses and over wet tropical regions will very likely become mo…
The paper introduces CoCoCat bonds for multi-region natural catastrophes, accounting for complex dependencies.
problem Valuation of multi-region contingent convertible bonds under complex dependencies.
method Developed a model accounting for inter-regional dependencies using change-of-measure techniques.
result Significant impact of inter-regional dependencies on CoCoCat bond pricing.
SwiGAN generates drought scenarios for climate risk management.
problem Natural catastrophes and droughts increase insurance costs.
method Conditional GANs for generating spatio-temporal SWI maps.
result Simulates drought patterns up to 2050 for French regions.
Optimal dividends for insurers with climate tipping point consideration.
problem Natural catastrophe claims and climate tipping point impact insurance profits.
method Two-dimensional stochastic control problems with Erlang distribution for tipping point timing, numerical approximation.
result Non-stationary climate tipping point can benefit shareholders by optimizing dividends.
Paper models cloud outages for cyber insurance stress-testing.
problem Cyber insurance portfolios' vulnerability to simultaneous cloud outages.
method Modeling and calibrating cloud-outage scenarios, measuring diversification.
result Cloud-outage diversification can protect against accumulation risk.
Deviance Voronoi residuals improve earthquake insurance risk assessment.
problem Assessing earthquake insurance risk using spatio-temporal point process models.
method Extended Voronoi residuals and created simulation-based approach.
result Proposed formula for country-wide minimum capital test.
Within the context of the banking-related literature on contingent convertible bonds, we comprehensively formalise the design and features of a relatively new type of insurance-linked security, called a contingent convertible catastrophe bond (CocoCat). We begin with a discussion of its design and compare its relative …
The study models and values CAT bonds across multiple regions.
problem Valuation of CAT bonds with dependencies across different regions.
method Developed models for independent, proportional, and arbitrary two-dimensional distribution cases of catastrophe losses in different areas. Applied normal approximation and Wang's transform for pricing.
result Illustrated differences in scenarios and performance of the approximation on real data.
This study tackles basis risk in weather parametric insurance using Monte Carlo simulations.
problem Mismatch between actual loss and payout in weather parametric insurance leads to loss without payout or payout without loss.
method Empirical research using Monte Carlo simulations to test diversification and hedging strategies.
result Portfolio basis risk and volatility decrease with more contracts, and spatial relationships significantly impact basis risk.
NDI aims to forecast future natural disasters risk for insurers.
problem Increasing intensity and frequency of natural disasters.
method Develops a Natural Disasters Index (NDI) based on NOAA data.
result NDI forecasts future natural disasters risk for insurers.
Paper proposes optimal investment and reinsurance strategies considering financial and insurance risks dependence.
problem Optimal investment and reinsurance strategies under dependent financial and insurance risks.
method Stochastic control approach to maximize expected exponential utility of terminal wealth.
result Minimal dependence between financial and insurance risks significantly impacts investment and reinsurance strategies.
Study shows subordinated Cramér-Lundberg model increases ruin probability.
problem Analyzing the impact of subordinated time-changed claims on insurance ruin probability.
method Examined a compound Poisson process modified by a Lévy subordinator.
result Probability of ruin decreases slowly with initial capital, despite unchanged total claim amount.
The paper analyzes liquidity in decentralized finance, deriving impact functions and de-pegging risks.
problem Understanding and quantifying market impact and de-pegging risk in decentralized finance.
method Derives market impact functions for optimal-growth liquidity providers, views Constant Product Market Maker as a Carnot engine, and links de-pegging risks to catastrophe bonds.
result New insights into liquidity models and de-pegging risks in decentralized finance.
We develop an agent-based simulation of the catastrophe insurance and reinsurance industry and use it to study the problem of risk model homogeneity. The model simulates the balance sheets of insurance firms, who collect premiums from clients in return for ensuring them against intermittent, heavy-tailed risks. Firms m…
Study optimal reinsurance and investment strategies under common shocks affecting financial and actuarial markets.
problem Maximizing expected exponential utility of terminal wealth in a company facing both ordinary and catastrophic claims.
method Modeling common shocks affecting financial and actuarial markets, using stochastic control and Hamilton-Jacobi-Bellman equations.
result Characterization of optimal reinsurance and investment strategies under common shock dependence.
Insurance benefits risk sharing for finite mean risks but not for infinite mean risks.
problem The effect of risk sharing and diversification for infinite mean risks.
method Investigation of risk sharing and diversification for infinite mean models, including stable, Pareto, and Fréchet distributions.
result Risk sharing can have a negative effect for infinite mean models, a phenomenon known as the nondiversification trap.
Paper introduces a new model for cyber insurance pricing.
problem Inaccurate pricing of cyber insurance due to multiple, contagious losses.
method Developed a bivariate compound dynamic contagion process.
result Analytical expressions for the compound process and its moments.
Model quantifies cyber-attacks' impact on firms and insurers.
problem Impact of cyber-attacks on firms' revenues and insurers' portfolios.
method Stochastic SIR model coupled with granular firm growth model.
result Predicts insurer needs to compensate up to two days of revenue in a 100-day incident.
FL improves insurance claims loss prediction without sharing data.
problem Limited data volume and variety due to privacy concerns.
method Federated Learning (FL) to update a global model using local data insights.
result Improved claims loss forecasting compared to individual models.
Paper proposes government indemnification for AI risks to solve judgment-proof problem.
problem Uninsurable risks from AI, especially existential risks, create a judgment-proof problem.
method A government-provided, mandatory indemnification program using risk-priced fees and Bayesian Truth Serum.
result The approach better leverages private information and signals risk mitigation efforts.
The paper proposes modern computational methods for optimizing reinsurance contracts.
problem Optimizing catastrophe excess-of-loss reinsurance contracts with realistic constraints and risk measures.
method Two approaches: simulated annealing for local search and quantum branch & bound for future potential.
result Quantum branch & bound approach shows potential for future optimization with quantum computers.
Stochastic simulation techniques employed for the analysis of portfolios of insurance/reinsurance risk, often referred to as `Aggregate Risk Analysis', can benefit from exploiting state-of-the-art high-performance computing platforms. In this paper, parallel methods to speed-up aggregate risk analysis for supporting re…
A neural network approach to learn Cusp Catastrophe dynamics.
problem Complex behavior and non-convex parameter space in Cusp Catastrophe models.
method Training a deep neural network to learn dynamics without solving generating parameters.
result Demonstrated a neural network approach for the first time in Cusp Catastrophe models.
This paper describes some of the possibilities of artificial neural networks that open up after solving the problem of catastrophic forgetting. A simple model and reinforcement learning applications of existing methods are also proposed.
The paper values reinsurance contracts for dynamic catastrophe claims without arbitrage.
problem Valuation of reinsurance contracts for dynamic catastrophe claims without arbitrage.
method Compound dynamic contagion process, Esscher transform, Monte Carlo simulation.
result Arbitrage-free premiums for catastrophe stop-loss reinsurance contracts.
Random forest predicts catastrophe bond spreads with 93% accuracy.
problem Predicting spreads in the primary catastrophe bond market.
method Random forest approach using all information in offering circulars.
result Random forest explains 93% of spread variability, significantly better than linear regression (47%).
Adam optimizer leads to more forgetting in neural networks.
problem Understanding and quantifying catastrophic forgetting in neural networks.
method Comparative analysis of various optimization algorithms and metrics in different learning scenarios.
result Adam optimizer causes more forgetting compared to classical algorithms like SGD.
Bayesian online meta-learning framework tackles catastrophic forgetting in few-shot classification.
problem Catastrophic forgetting in few-shot classification problems.
method Bayesian online learning, meta-learning, Laplace approximation, variational inference.
result Framework effectively achieves goal of overcoming catastrophic forgetting in few-shot classification.
Unified Bayesian framework for CAT bond pricing.
problem Uncertainty in catastrophe occurrences and interest rates in CAT bond markets.
method Bayesian framework based on uncertainty quantification of catastrophes and interest rates.
result Unified asset pricing approach with informative expected risk premia.
Paper introduces Auto DeepVis to explain catastrophic forgetting in continual learning.
problem Catastrophic forgetting in continual learning of deep neural networks.
method Auto DeepVis and critical freezing techniques to address catastrophic forgetting.
result Critical freezing outperforms other methods on both past and future tasks.
Study insurance pricing under correlation ambiguity without increasing prices or reducing utility.
problem Understanding the dependence structure between insurance and financial risks.
method Dynamic equilibrium analysis of insurance pricing with worst-case beliefs.
result Correlation ambiguity does not necessarily increase insurance prices or reduce insurers' utility.
Paper proves Pareto efficient insurance for multiple entities.
problem Optimizing insurance for multiple policyholders and insurers.
method Sum-minimization characterization and pairwise implementability analysis.
result Characterization of Pareto efficient insurance arrangements.
Paper tackles catastrophic overfitting in single-step adversarial training.
problem Catastrophic overfitting leads to sudden drop in robust accuracy.
method Proposes a method to prevent overfitting by using all adversarial examples.
result Demonstrates prevention of catastrophic overfitting and improves robustness.
Study on systemic risk in European insurance sector, showing insurer connections during stress.
problem Understanding systemic risk connectedness in European insurance sector.
method Common connectedness framework applied to returns, volatility, value-at-risk, and expected shortfall.
result Insurers are a significant component of systemic risk connectedness, especially during stress episodes.
The paper examines how risk reduction and insurance choices interact under convex premium principles.
problem Interaction between self-protection and insurance demand under convex premium principles.
method Investigates optimal prevention efforts and insurance shares using distortion risk measures.
result Self-protection and insurance are complementary, but ex ante moral hazard can turn this into a substitution effect.