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.
A new law of large numbers for single catastrophic risks.
problem Insurance risks from a single catastrophic event.
method Large diversification effect through optimal allocation to many reinsurers or investors.
result Derives a strong law of large numbers for insurance risks.
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 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.
Study compares ZBDT model to BDT for financial derivatives valuation.
problem Valuation of financial derivatives under catastrophic events.
method Introduced Zero Black-Derman-Toy (ZBDT) model with jumps to zero interest rate.
result ZBDT model better matches financial slowdown risk.
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.
Adversarial method finds rare catastrophic failures in safety-critical agents.
problem Evaluating safety-critical learning systems for catastrophic failures.
method Adversarial evaluation approach focusing on rare adversarial situations.
result Adversarial evaluation finds catastrophic failures and estimates failure rates faster.
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.
DC-Prophet predicts catastrophic server failures in datacenters.
problem Forecasting catastrophic machine failures in datacenters.
method Two-stage framework based on One-Class Support Vector Machine and Random Forest.
result DC-Prophet achieves an AUC of 0.93 and F3-score of 0.88 in predicting the next machine failure.
Global catastrophe risk pools increase financial resilience by diversifying risk and including more countries.
problem Low- to middle-income countries rely heavily on foreign aid for recovery from extreme weather events, which is slow and uncertain.
method Developed a method to form global catastrophe risk pools that maximize risk diversification and select countries with low bilateral correlations or low shares in the pool risk.
result Global pooling increases risk diversification, lowers countries' shares in the pool risk, and increases the number of countries benefiting from risk pooling.
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.
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.
Study values and optimizes forestry leases under risk and uncertainty.
problem Valuing and optimizing forestry leases in the presence of catastrophe risk and parameter uncertainty.
method Stochastic bio-economic models, Kalman filter, maximum likelihood estimation, RBSDEs, Monte Carlo simulations.
result Conservative strategy is recommended due to parameter uncertainty.
Proposes a method to ensure accurate estimation of rare events in AI systems.
problem Lack of efficiency guarantees in black-box systems for rare-event simulation.
method Integrates deep learning with importance sampling to create a statistically guaranteed estimator.
result Demonstrates effective estimation of rare-event probabilities in AI systems.
Clusters of financial market states identified over 2006-2019.
problem Understanding the statistical properties of financial markets.
method Clustering analysis of correlation matrices constructed from sliding epochs.
result Financial markets can be classified into distinct states with transitions indicating precursors to catastrophic events.
Study reveals signatures of market crashes through eigenvalue analysis of stock return matrices.
problem Understanding the complexity and dynamics of market crashes.
method Cross-correlation structures and eigenspectra of stock return matrices were analyzed over different epochs.
result The smallest eigenvalue can distinguish between internal and external market instabilities.
Experience replay helps neural networks learn new tasks without forgetting old knowledge.
problem Catastrophic forgetting in neural networks trained on non-stationary data.
method Experience replay buffers with a mixture of on- and off-policy learning.
result Experience replay can learn new tasks quickly and reduce catastrophic forgetting.
SALeRA controls SGD learning rate to learn as fast as possible but not faster.
problem Catastrophic learning episodes in SGD training of deep neural networks.
method SALeRA uses two statistical tests: one for speeding up and one for detecting and halting catastrophic episodes.
result SALeRA learns as fast as possible but not faster, as demonstrated by experiments on standard benchmarks.
Study finds some examples are forgotten during neural network learning.
problem Understanding forgetting during neural network training.
method Defined forgetting event and analyzed across multiple data sets.
result High frequency of certain examples being forgotten, some not.
Jeffreys Flow improves robustness of Boltzmann generators for rare event sampling.
problem Rare events and metastable trapping in sampling physical systems with rough energy landscapes.
method Introduces Jeffreys Flow, a robust generative framework using Parallel Tempering distillation and symmetric Jeffreys divergence to mitigate mode collapse and improve mode coverage.
result Minimizing Jeffreys divergence suppresses mode collapse and corrects inaccuracies in multi-modal distributions.
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.
Study examines aftershocks after currency crash, finding power law relaxation.
problem Analyzing aftershocks following a currency exchange rate crash.
method Case study of RUB/USD crash in 2014; power law relaxation and correlation analysis.
result Relaxation and correlation follow power laws, with aging and scaling observed.
AI-assisted framework detects and predicts rare extreme events.
problem Detecting rare extreme events in complex systems.
method Combining BED with DNOs for active learning and forecasting.
result Framework outperforms GPs and uncovers extremes without initial data.
Framework detects tipping points in complex systems using ML.
problem Detecting tipping points in complex, emergent systems.
method Combining manifold learning, neural networks, and Gaussian processes.
result Reduced-order models for mesoscopic and mean-field dynamics.
Paper tackles forgetting in neural networks, proposing solutions.
problem Catastrophic forgetting in artificial neural networks.
method Simple model and reinforcement learning applications.
result Proposes solutions to prevent forgetting in neural networks.
The study values a new type of insurance-linked security called CocoCat bonds.
problem Valuing a new type of insurance-linked security called contingent convertible catastrophe bonds.
method Formalized design, derived analytical valuation formulae, used time-inhomogeneous compound Poisson process for natural catastrophe losses, and applied exponential change of measure and Girsanov-like transformation.
result CocoCat bond prices are most sensitive to interest rates, conversion fractions, and trigger levels.
FSNet improves online time series forecasting by balancing fast adaptation and old knowledge.
problem Online time series forecasting challenges in handling abrupt and recurring patterns.
method Inspired by CLS theory, FSNet uses a dynamic balance between fast adaptation and old knowledge retrieval.
result FSNet achieves robustness to both new and recurring patterns through dynamic balancing and associative memory.
Model prices insurance catastrophe derivatives using utility indifference pricing.
problem Price insurance catastrophe derivatives considering both ordinary and catastrophic losses.
method Utility indifference pricing, stochastic optimization, piecewise deterministic Markov processes.
result Numerical study illustrates the pricing model's effectiveness.
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.
Many complex systems exhibit extreme events far more often than expected for a normal distribution. This work examines how self-similar bursts of activity across several orders of magnitude can emerge from first principles in systems that adapt to information. Surprising connections are found between two apparently unr…
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%).
Study optimizes climate adaptation strategies for NYC.
problem Catastrophic damages from extreme weather in NYC.
method Real options analysis and extreme value theory.
result Optimal adaptation pathways identified for NYC.
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 helps identify which ANN parameters cause forgetting.
problem Catastrophic forgetting in neural networks.
method Determines individual parameter contributions to forgetting.
result Identifies specific ANN parameters causing forgetting.
Intrinsic fear prevents catastrophic states in reinforcement learning.
problem Catastrophic forgetting in reinforcement learning.
method Intrinsic fear (IF) is a learned reward shaping that penalizes the Q-learning objective based on the probability of imminent catastrophe.
result Intrinsic fear models prevent periodic catastrophes in reinforcement learning agents.
Prototype controls stochastic drug resistance in cells.
problem Emergence of drug-resistant cells from random mutations.
method Deep reinforcement learning for adaptive drug dosing.
result 100% success rate in suppressing cell proliferation.
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.
This paper develops a two-step estimation methodology, which allows us to apply catastrophe theory to stock market returns with time-varying volatility and model stock market crashes. Utilizing high frequency data, we estimate the daily realized volatility from the returns in the first step and use stochastic cusp cata…
Study analyzes catastrophic forgetting in continual learning using teacher-student networks.
problem Catastrophic forgetting in continuously learning systems.
method Teacher-student learning framework, similarity of input distributions and target functions.
result Network can avoid catastrophic forgetting with small input distribution similarity and large target function similarity.
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.
The paper examines GANs' forgetting and mode collapse, showing how they relate and impact training.
problem Catastrophic forgetting and mode collapse in GANs during continual learning.
method Investigates the continual learning nature of GANs, analyzing discriminator's output landscapes and convergence.
result Catastrophic forgetting and mode collapse are interrelated and prevent GANs from converging.
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…
New algorithm selects features using Catastrophe model for better regression analysis.
problem Improving feature selection in regression analysis to enhance model performance.
method The algorithm ranks features based on their fit to the Catastrophe model and uses Akaike information criterion for ranking.
result The proposed algorithm outperforms RELIEF in various datasets.
Proposes a method to prevent neural networks from forgetting learned tasks.
problem Catastrophic forgetting in neural networks.
method Attention-based selective plasticity of synapses inspired by the cholinergic neuromodulatory system.
result Competitive performance on benchmark tasks compared to state-of-the-art methods.
Paper tackles catastrophic forgetting in sequential learning.
problem Catastrophic forgetting in sequential learning.
method Regularizes training with sketches of Jacobian matrix of past data.
result Proves overcoming catastrophic forgetting for linear and wide neural networks.