A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New methods improve insurance data quality for catastrophic events.
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.
Study compares ZBDT model to BDT for financial derivatives valuation.
Optimizes diversification in catastrophe risk pooling using asymptotic analysis.
Research simulates Lloyd's of London's specialty insurance market dynamics.
Global catastrophe risk pools increase financial resilience by diversifying risk and including more countries.
Study proposes a tax-based system to share disaster risk among regions.
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…
Catastrophic events, though rare, do occur and when they occur, they have devastating effects. It is, therefore, of utmost importance to understand the complexity of the underlying dynamics and signatures of catastrophic events, such as market crashes. For deeper understanding, we choose the US and Japanese markets fro…
Quantum method speeds up risk estimation for insurance tail risks.
Study values and optimizes forestry leases under risk and uncertainty.
When will a server fail catastrophically in an industrial datacenter? Is it possible to forecast these failures so preventive actions can be taken to increase the reliability of a datacenter? To answer these questions, we have studied what are probably the largest, publicly available datacenter traces, containing more …
Proposes a method to ensure accurate estimation of rare events in AI systems.
Clusters of financial market states identified over 2006-2019.
Jeffreys Flow improves robustness of Boltzmann generators for rare event sampling.
The dynamical behavior of the currency exchange rate after its large-scale catastrophe is discussed through a case study of the rate of Russian rubles to US dollars after its crash in 2014. It is shown that, similarly to the case of the stock market crash, the relaxation is characterized by a power law, which is in ana…
When applied to training deep neural networks, stochastic gradient descent (SGD) often incurs steady progression phases, interrupted by catastrophic episodes in which loss and gradient norm explode. A possible mitigation of such events is to slow down the learning process. This paper presents a novel approach to contro…
This paper addresses the problem of evaluating learning systems in safety critical domains such as autonomous driving, where failures can have catastrophic consequences. We focus on two problems: searching for scenarios when learned agents fail and assessing their probability of failure. The standard method for agent e…
NDI aims to forecast future natural disasters risk for insurers.
Continual learning is the problem of learning new tasks or knowledge while protecting old knowledge and ideally generalizing from old experience to learn new tasks faster. Neural networks trained by stochastic gradient descent often degrade on old tasks when trained successively on new tasks with different data distrib…
AI-assisted framework detects and predicts rare extreme events.
Framework detects tipping points in complex systems using ML.
A neural network approach to learn Cusp Catastrophe dynamics.
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.
FSNet improves online time series forecasting by balancing fast adaptation and old knowledge.
The challenge in controlling stochastic systems in which low-probability events can set the system on catastrophic trajectories is to develop a robust ability to respond to such events without significantly compromising the optimality of the baseline control policy. This paper presents CelluDose, a stochastic simulatio…
Random forest predicts catastrophe bond spreads with 93% accuracy.
Adam optimizer leads to more forgetting in neural networks.
Bayesian online meta-learning framework tackles catastrophic forgetting in few-shot classification.
Unified Bayesian framework for CAT bond pricing.
Paper introduces Auto DeepVis to explain catastrophic forgetting in continual learning.
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…
Study optimizes climate adaptation strategies for NYC.
Paper tackles catastrophic overfitting in single-step adversarial training.
Inspired by the phenomenon of catastrophic forgetting, we investigate the learning dynamics of neural networks as they train on single classification tasks. Our goal is to understand whether a related phenomenon occurs when data does not undergo a clear distributional shift. We define a `forgetting event' to have occur…
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…
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 …
Study analyzes catastrophic forgetting in continual learning using teacher-student networks.
Artificial neural networks (ANNs) suffer from catastrophic forgetting when trained on a sequence of tasks. While this phenomenon was studied in the past, there is only very limited recent research on this phenomenon. We propose a method for determining the contribution of individual parameters in an ANN to catastrophic…
The paper introduces CoCoCat bonds for multi-region natural catastrophes, accounting for complex dependencies.
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…
Paper tackles catastrophic forgetting in sequential learning.
The principal aim of this work is the evidence on empirical way that catastrophic bifurcation breakdowns or transitions, proceeded by flickering phenomenon, are present on notoriously significant and unpredictable financial markets. Overall, in this work we developed various metrics associated with catastrophic bifurca…
This work tackles catastrophic forgetting in neural networks by mimicking brain's metaplasticity.
This letter uses the Block Maxima Extreme Value approach to quantify catastrophic risk in international equity markets. Risk measures are generated from a set threshold of the distribution of returns that avoids the pitfall of using absolute returns for markets exhibiting diverging levels of risk. From an application t…
New analysis shows rational actors will deploy AGI despite negative social value due to catastrophic risk.