CERM calculates climate risks in bank loans.
problem Estimating climate risks in bank credit portfolios.
method Adapts credit risk models to include physical and transition risks.
result Calculates incremental credit losses due to climate risks.
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arXiv research
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168,695 papers · 148 categories
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48 results for “Climate Risk”
Tuning into Climate Risks: Extracting Innovation from Television News for Clean Energy Firmsq-fin.ST
Study uses TV news to measure climate risks affecting clean energy firms.
problem Understanding how climate risks impact clean energy firms' financial stability.
method Developed climate risk measures from TV news coverage and analyzed their effects on clean energy firms' risks.
result Increased TV news coverage of climate risks correlates with higher systematic risk and lower idiosyncratic risk for clean energy firms.
Geospatial framework assesses climate risks for California's banking and exposed sectors.
problem Evaluating climate risks on banking and exposed sectors in California.
method Integrates hazard mapping, exposure analysis, and scenario-based financial risk assessment.
result Framework supports portfolio monitoring and institutional readiness under new standards.
A Stochastic Climate Model -- An approach to calibrate the Climate-Extended Risk Model (CERM)q-fin.RM
Proposes a new stochastic method to calibrate climate risks in financial models.
problem Estimating climate-related financial risks in bank loan portfolios.
method Stochastic forward-looking methodology to calibrate climate macro-correlation evolution from scientific data.
result A new framework to evaluate climate risks without specific scenario assumptions.
Climate-Contingent Financeq-fin.GN
Climate-contingent finance helps adapt to uncertain climate risks.
problem Uncertainty in future climate scenarios makes proactive adaptation less feasible.
method Underwrite climate adaptation projects with repayment based on future climate scenarios.
result Optimal financing reduces over- and under-preparation risks.
Develops a climate risk model for asset managers.
problem Climate-related risks affecting asset performance and productivity.
method Uses the Vasicek model with downward jumps to represent climate impacts on asset dynamics.
result Expected losses increase over time due to climate-related extreme events.
The study examines how climate risk influences sovereign debt default decisions.
problem The relationship between climate risk and sovereign debt default decisions.
method Calibration of a model to analyze the credit spreads of sovereign bonds and the impact of climate vulnerability on bond spreads.
result Climate risk does not significantly influence the decision to default on sovereign debt.
Investigates how extreme temperature events affect global equity portfolios.
problem Impact of extreme temperature events on global equity portfolios.
method Panel regression analysis and multi-objective portfolio optimization.
result Extreme temperature events negatively impact most sectors' returns.
Study shows climate change can cause a 'run on fossil fuels' affecting prices and production.
problem Impact of climate change expectations on fossil fuel markets and prices.
method Dynamic, general equilibrium model of climate-change-linked transition risk.
result Climate change expectations can lead to either increased or decreased fossil fuel prices, depending on economic responses.
PCL framework optimizes climate risk management across three clusters.
problem Comprehensive risk management in response to climate change impacts.
method Optimization of preemptive adaptation, contingent arrangements, and loss acceptance.
result Balanced portfolio of actions across three clusters optimized for long-term aggregate outlay.
Study integrates climate and text data to improve credit default prediction.
problem Improving credit risk assessment for mSEs with limited financial histories.
method Multimodal framework using LSTM, GRU, and transformer models.
result Integration of multiple data modalities improves credit default prediction.
Developing a climate-aware pricing framework for XL reinsurance and CAT bonds under non-stationary catastrophe risk.
problem Pricing excess-of-loss (XL) reinsurance and catastrophe (CAT) bonds under climate uncertainty.
method Modeling catastrophe arrivals as a Cox process with a temperature-dependent stochastic intensity and aggregate losses following a compound Cox structure.
result Climate dependence materially changes the loss-generation mechanism and affects the valuation of catastrophe-linked contracts.
Copula models for sovereign ratings improved by incorporating climate risk.
problem Modeling nonlinear dependence and clustering in sovereign rating migrations.
method Mixed-difference transformation, MAGMAR(1,1) copula process, consistent and asymptotically normal estimators.
result Gumbel MAGMAR(1,1) specification outperforms other models in empirical performance.
EcoCast predicts biodiversity risks using satellite data and citizen science records.
problem Unprecedented shifts in species distributions due to climate change and habitat loss.
method Spatio-temporal model using sequence-based transformers and continual learning.
result Promising improvements in forecasting bird species distributions compared to Random Forest.
Modeling supply chain disruptions from climate hazards with adaptive firms.
problem Systemic physical climate risk in supply chains.
method Agent-based model integrating geospatial hazards and firm adaptation.
result Firms' adaptive strategies reduce disruption by 48%.
CE improves climate uncertainty quantification using GCM ensembles and observational data.
problem Uncertainty in climate projections due to model inadequacies and variability.
method Conformal ensembles integrating GCM ensembles and observational data.
result CE generates statistically rigorous, easy-to-interpret uncertainty estimates.
Hybrid framework predicts Arctic permafrost decline, risks infrastructure, and provides tools.
problem Tackles permafrost decline and infrastructure risk assessment in Arctic territories.
method Hybrid physics-machine learning framework integrating 2.9 million observations.
result Projects mean permafrost fraction decline of -20.3 pp under RCP8.5 forcing, with high-risk zones identified.
The paper examines spillovers between agriculture, crude oil, carbon, and climate markets.
problem Understanding dynamic spillovers between agriculture, crude oil, carbon emission, and climate markets.
method A novel decomposed connectedness approach.
result Overall spillovers are mainly contemporaneous, not lagged; climate change significantly impacts others; agricultural markets have heterogeneous effects; corn is a major risk contributor.
Study models risks for low-carbon economy in Balkan countries, focusing on shadow economy and populism.
problem Risks and uncertainties in establishing a low-carbon economy in Balkan countries with transition economies.
method Transdisciplinary approach combining economic policy, public opinion, and climate change models.
result Identifies shadow economy and populism as key risk factors for low-carbon economy implementation.
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.
Proposes a new framework for environmental CVA with robust wrong-way risk.
problem Limited operational implementations of translating environmental scenarios into CVA.
method Three components: hazard rate mapping, tail generators, and KL divergence-based wrong-way risk bound.
result Nature CVAs can vary significantly across different ecosystem generators.
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.
Study analyzes climate impact on agricultural prices, offering insurance solutions.
problem Financial risk from climate-induced agricultural price volatility.
method Historical and future climate projections, EGARCH and SARIMAX models, Black-Scholes framework.
result Improved agricultural risk modeling and insurance mechanisms.
The study uses machine learning to predict CAT bond coupons based on climate data.
problem Predicting CAT bond coupons using climate data.
method Combining climate indicators with machine learning models (random forest, gradient boosting, etc.).
result Extremely randomized trees achieved the lowest RMSE in predicting CAT bond coupons.
Model shows how financial markets can decarbonize under climate uncertainty.
problem Decarbonization of financial markets under climate uncertainty.
method Mean-field game approach to model firm decisions and investor interactions.
result Climate uncertainty weakens the impact of green-minded investors on decarbonization.
Study assesses drought and late-frost risks in Bavaria using vine copulas.
problem Assessing risks of late-frost and drought in Bavaria due to climate change.
method Used vine copula models for non-Gaussian and asymmetric dependencies, with univariate and bivariate regression analyses.
result Identified 'at-risk' regions for forest adaptation.
Proves lower discount rates are needed for future losses.
problem Determining appropriate discount rates for future losses.
method Analyzes climate change and discount rates debate.
result Risk requires a lower, not higher, discount rate.
New model predicts banana disease risk from climate data.
problem Managing Black Sigatoka disease under climate change.
method Latent Neural ODEs to model infection dynamics.
result Superior generalization performance up to one month ahead.
Model predicts unseen climate extremes to inform risk planning.
problem Missing unseen climate extremes in historical records.
method DeepX-GAN model capturing spatial dependence.
result Unseen heat extremes disproportionately threaten vulnerable regions.
Study predicts doubling of U.S. maize insurance claims due to climate change.
problem Climate change increases U.S. maize loss probability, impacting insurance claims.
method Neural Network Monte Carlo simulations to predict crop loss metrics.
result Doubling of annual probability of maize Yield Protection insurance claims by mid-century.
The paper introduces ESE scores for farmers to assess climate change risks.
problem Assessing climate change risks in individual farmers' credit evaluations.
method Integrating ESG variables into joint liability models and using a mean-variance utility function.
result Optimal group sizes and individual-ESE score relationships under various climatic conditions.
Study assesses climate risks on supply chains and financial systems using detailed firm emissions data.
problem Lack of firm-level CO2 emissions data hinders assessment of transition risks from carbon pricing.
method Used detailed Hungarian firm emissions data and a simple economic ABM model to simulate carbon pricing impacts.
result 45% of companies are directly exposed to carbon pricing, leading to significant economic and financial losses.
Study measures investment funds' climate transition risk, finds moderate losses.
problem Measuring the impact of climate transition on investment portfolios.
method Comprehensive framework using geographical, sectoral, company and ISIN-level data.
result Investment funds suffer a moderate 5.7% loss in high transition risk scenario.
Modeling bank portfolio risk under climate transition impacts.
problem Evaluating risk measures for a bank's collateralized loans in a climate transition economy.
method Developed an end-to-end modeling framework using stochastic processes and dynamic macroeconomic variables.
result Derived expressions for risk measures as functions of climate transition parameters.
CCVA adjusts for climate change impacts on financial valuation.
problem Climate change impacts on financial valuation are currently ignored.
method Flexible parameterization to capture climate impacts on hazard rates.
result Significant impacts on interest rate swaps even with slow climate change.
HECT tests climate model outputs for reproducibility.
problem Ensuring climate models accurately reflect physical processes.
method Probabilistic classifiers for high-dimensional spatio-temporal data.
result A principled way to assess statistical reproducibility of climate models.
TemperatureGAN generates hourly atmospheric temperature data with high fidelity.
problem Generating accurate hourly atmospheric temperature data for climate risk assessment.
method Generative Adversarial Network (GAN) conditioned on months, locations, and time periods.
result TemperatureGAN produces high-fidelity hourly atmospheric temperature data with good spatial and temporal consistency.
Climate has been an important factor in shaping the distribution and incidence of dengue cases in tropical and subtropical countries. In Costa Rica, a tropical country with distinctive micro-climates, dengue has been endemic since its introduction in 1993, inflicting substantial economic, social, and public health repe…
Study on insurance risk management and sustainable development.
problem Lack of attention to non-climate change aspects of sustainable development in insurance.
method Analysis of recent developments and legislative initiatives in insurance risk management.
result Strategies for small- and medium-sized enterprises to manage sustainable development risks.
AI boosts study of rare weather extremes with lower costs.
problem Difficulty in studying rare weather events due to limited data and models.
method Coupling AI forecasts with physics models using rare-event algorithms.
result Efficiently characterizes very rare events like once-per-millennium heatwaves.
This study analyzes how carbon pricing affects credit risk measures in a portfolio.
problem Impact of carbon pricing on credit risk measures in a portfolio.
method Adapted stochastic multisectoral model to account for GHG emissions costs and carbon prices.
result Carbon pricing distorts firm value distributions, increases banking fees, and reduces profitability.
Stochastic model prices weather derivatives for Indian states, highlighting temperature volatility impacts.
problem Quantifying financial risk in Indian markets due to seasonal weather variations.
method Modified Ornstein-Uhlenbeck process with jumps for temperature dynamics, calibrated with historical data, Monte Carlo simulations for pricing.
result Volatility significantly impacts weather derivative pricing, with higher prices in colder states and lower in hotter states.
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.
Modeling climate change costs with stochastic interest rates shows inequality, but funding abatement can reduce this.
problem Evaluating the costs and benefits of climate change mitigation with uncertain discount rates.
method Amended DICE model with stochastic interest rates and funding abatement costs.
result Introducing funding abatement can reduce intergenerational inequality in climate change costs.
Combines GANs and EVT for better modeling of spatial climate extremes.
problem Modeling dependencies between climate extremes, especially in high-dimensional spaces.
method Generative Adversarial Networks (GANs) combined with Extreme Value Theory (EVT).
result evtGAN outperforms classical GANs and statistical approaches in modeling spatial extremes.
Portfolio Optimization for Index Tracking with Constraints on Downside Risk and Carbon Footprintq-fin.RM
This paper optimizes decarbonized indices for financial tracking, balancing risk and environmental impact.
problem Balancing financial performance with environmental responsibilities in the context of climate risks.
method Develops decarbonized indices using mean-VaR and mean-ES optimization methods.
result Optimized indices reduce financial risk and carbon footprint, providing a balanced investment option.
Study assesses sugar beet yields under EU's neonicotinoids ban and climate change.
problem Impact of yellow virus on sugar beet yields under neonicotinoids ban and climate change.
method Modeling using climate datasets and simulations of aphid flight and abundance.
result Reconstructs sugar beet yields using 'as if' approach without neonicotinoids.
A Natural Disasters Indexq-fin.RM
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.