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Modeling Climate Impact

on Credit Risk 

Riesgo Climático y Modelización del Riesgo Crédito 



Climate change poses both risks and opportunities for financial institutions, now and in the future. As the Earth's temperature rises, increasingly common natural disasters are disrupting ecosystems and human health, causing unforeseen business losses and threatening banks' assets and infrastructure. In response, governments and private sector entities are considering a variety of options to reduce global emissions, which could result in disruptive changes across all economic sectors and regions in the near term.

The UK supervisor classifies these risks into two: physical and transition. The first refer to specific meteorological phenomena (heat waves, floods, forest fires, storms) and long-term changes in the climate, such as a rise in sea level, and these could detract from the value of the properties that act as collateral in mortgages, increasing credit risk.

For their part, transition risks refer to those that may arise in the process of adaptation to a low-carbon economy. For example, the development of the electric car could transform the automotive industry at great speed and impact the value of the financial assets that entities have on the balance sheet.

Due to the above, some regulators such as the Bank of England have required English banks to incorporate climate change into financial risks, and we believe that this practice must be incorporated sooner or later in financial institutions in other latitudes.

​It discusses how to incorporate the financial risks of climate change into existing financial risk management practice, how to use scenario analysis to inform strategy setting and risk assessment and identification, and how to develop an approach to disclosure of financial risks. financial risks of climate change.

The objective of the course is to show the best practices for quantifying and managing climate risk.

Regarding climate risk management, governance, organization, scenario generation, risk assessment, risk appetite, as well as monitoring and disclosure of this risk are explained.

The Task Force Climate-related Financial Disclosure (TCFD) standard is explained.

The impact of climate change is quantified on credit risk models and methodologies such as credit rating, credit scoring, modeling of PD, LGD and EAD parameters of the advanced IRB approach of Basel III, credit risk methodologies for IFRS 9 impairment models , stress testing models of credit risk, concentration risk and capital allocation. In addition, the impact of COVID-19 on credit risk models is explained.

Advanced backtesting techniques are shown, such as discriminant power, stability tests, and calibration.


The use of machine learning to develop advanced models of credit risk and climate change is explained. In addition, it explains how to take advantage of machine learning to validate models and quantify credit risk.

Powerful model risk and credit risk exercises done in Python, R, SAS, Excel and JupyterLab are delivered.



This program is aimed at Directors, Managers, Analysts and Financial Risk Consultants and specialists in credit risk and those interested in climate risk. The content of the course is absolutely practical to apply immediately at work.


La naturaleza verde

Price: 6 000 €


  • Europe: Mon-Fri, CEST 16-19 h


  • America: Mon-Fri, CDT 18-21 h

  • Asia: Mon-Fri, IST 18-21 h





Summer Price: 5 000 €


Level: Advanced


Duration: 30 h



Presentations in PDF

Exercises in: Python, R, SAS, Excel y JupyterLab.



Modeling Climate Impact on Credit Risk 

Anchor 15



Module 1: Introduction to climate change and financial risk management


  • Summary of climate change risk

  • How does climate change translate into financial risk?

  • Exposure to weather-related risks

    • Physical risks

      • Climate change as a physical and meteorological phenomenon and its impacts on natural and artificial systems

      • Basic science of climate change

      • the latest scientific knowledge compiled by the Intergovernmental Panel on Climate Change (IPCC).

    • Transition risks

      • The economic transition with low carbon emissions, its risks and impacts

      • The social response to climate change as a political, economic and technological response

      • the international climate change regime, and current debates and challenges, such as the "Tragedy on the horizon"

      • main policy responses to climate change at the national level (for example, emissions trading

      • introduction to the risks and opportunities that climate change implies for the financial sector (mitigation and adaptation)

      • introduction to transition risks and opportunities in the context of the new Task Force on Climate Related Financial Disclosure (TCFD)

  • Risks of climate change risk

  • Understand the performance of carbon as an asset class.

Module 2: Emerging regulatory expectations

  • Background to regulatory initiatives: Paris Agreement and other frameworks

  • Overview of current regulatory standards (for example, PRA, ECB, HKMA, MAS, etc.)

  • Role of NGFS and standard setters

  • Risk Management Expectations

  • Challenges and opportunities Disclosure, reporting and governance frameworks

  • Growing pressure for financial disclosure

  • Strong ownership and oversight of climate change risk management practices

  • Task Force on Climate-related Financial Disclosures TCFD

    • Understand the guiding principles of TCFD

    • Develop a comprehensive TCFD program

    • implement recommendations

  • Basel

    • The role of climate-related financial risks in the regulatory and supervisory framework

    • Research related to the measurement of climate-related financial risks

    • Measures to raise stakeholder awareness of climate-related financial risks

    • Bank approaches to managing and disclosing weather-related financial risks

    • The supervisory treatment of weather-related financial risks

    • Other initiatives that are underway among the respondents.

  • IFRS

    • ​Making materiality judgments

    • Apply Materiality Judgments to Weather-Related and Other Emerging Risks

    • Financial reporting considerations

    • Disclosure of weather-related risks and other emerging risks

    • Management comment: provides context to the financial statements

    • Abstract: Materiality judgments must satisfy the information needs of investors.

Module 3: ESG and climate change risk


  • Environmental, social and corporate governance (ESG) refers to the three central factors in measuring the sustainability and social impact of an investment in a company or business.

  • Current trends in the ESG market

  • Analysis of ESG ratings

  • Adapt investment strategies to the effects of climate change

    • Build a portfolio that reflects the transition to a low carbon economy

    • Integrating the carbon transition and physical climate risk

  • Distinguish the risks, challenges and opportunities associated with ESG

Module 4: Climate Risk Management

  • Scenario Analysis and Stress Testing

  • What is the difference between "normal" stress tests and climate change risk stress tests?

    • Bank of England stress test

  • Summary of scenario analysis in the context of climate change risk

  • Data management to assess the risks of climate change.

  • Benefits and Issues of performing scenario analysis

  • Climate change risk methodologies

  • How to model the risk of climate change?

  • Using and evaluating data

  • Understand modeling methodologies

    • temperature alignment

    • Decarbonization Pact Methodology

    • Climate change applied to credit risk

  • Challenges in climate change risk modeling

  • Incorporation of climate change risk strategies

  • Integrate climate change risk management into financial risk management frameworks

  • Adoption of KPI, KRI to monitor climate risks.

  • Use of self-assessments

  • Improvement and management of business commitment

  • How to successfully integrate climate risk strategies into the business.

Module 5: Credit risk transition risk


  • Scenario analysis to assess the transition risk component of a portfolio's credit risk

  • Introduction: preparing banks for the low carbon transition

    • A growing need for climate scenario analysis

    • The challenge for banks

    • Take advantage of and integrate the resources available to banks

  • An integrated approach to transition risk assessment Transition scenarios

    • Understand transition scenarios and their sources

    • Using scenarios for transition risk assessment

    • Closing the gap between climate scenarios and financial risk assessment

  • Borrower Level Calibration

  • Portfolio Impact Assessment

    • Link expected loss to transition impacts on portfolios

    • Assessment of probability of default (PD)

    • Loss Given Default (LGD) Assessment

  • Putting the Approach to Work: Lessons Learned from Banking Pilots

  • Piloting the transition risk methodology

  • Definition of sectors and segments

  • Evaluate the relative sensitivities of the segments

  • Determination of calibration points at the borrower level Case studies and results

  • The pilot transition scenario

  • pilot results

  • Transition Opportunities: Exploring an Institutional Strategy

  • evaluating the market

  • Grounding Opportunity Assessments in Scenario Analysis

  • Assessing the market attractiveness of the segment

  • Identification of banking capabilities

  • Discovering the opportunities with the greatest potential

  • Future Directions: Developing the Next Generation of Transition Risk Analysis

Module 6: Physical risks and opportunities


  • An Integrated Approach to Physical Risk Assessment

  • Borrower Characteristics

  • Insurance as a risk mitigator due to extreme climatic and meteorological events

  • climate change scenarios

  • Impacts of climate change on the probability of default PD

    • Evaluation of changes in the productivity of the sector

    • Adjustment of income statement metrics

    • Determination of changes in the probability of default

  • Real Estate: Climate Change Impacts on LTV Loan-to-Value

    • Estimation of the impacts of extreme events on the value of properties.

    • Determining Changes in LTV Loan-to-Value Ratio

  • Physical Opportunities: Exploring an Institutional Strategy

  • Taxonomy of opportunities and data sources

  • evaluating the market

  • Evaluation of the financing demand of the sector

  • Sector evaluation

  • Assess the institutional capacity and market positioning of a bank

  • Evaluate opportunities

  • Future Directions: Towards the Next Generation of Physical Risk and Opportunity Analysis

  • Develop internal analytics and capabilities within banks

  • Strengthening the research base

  • Develop analytical platforms and tools to support physical assessments of risks and opportunities.

  • Improve information flows on physical risk and adaptation between banks and borrowers

  • Improve dialogue with governments and insurers


Module 7: Decarbonization and disruption

  • The oil and gas sector

    • market trends

    • The potential impacts of a disruptive transition

    • Ensure an orderly transition

    • Analysis of climatic scenarios of the messy transition

  • Utilities and Power Generation Sector

    • market trends

    • The potential impacts of a disruptive transition

    • Ensure an orderly transition

    • Analysis of climatic scenarios of the messy transition

  • Metals and mining sector (industrial)

    • market trends

    • The potential impacts of a disruptive transition

    • Ensure an orderly transition

    • Analysis of climatic scenarios of the messy transition

  • Agricultural sector

    • market trends

    • The potential impacts of a disruptive transition

    • Ensure an orderly transition

    • Analysis of climatic scenarios of the messy transition

Module 8: Climate transition scenarios

  • Analysis of climate scenarios in the financial sector

    • Analysis of scenarios before and after the global financial crisis

    • Climate scenario analysis

  • Climate scenarios

    • Introduction to Integrated Assessment Models (IAM)

    • Where do AMIs come from?

    • Advantages and Limitations of IAMs

  • Key assumptions

    • What do the IAM scenarios show?

    • What socioeconomic and policy assumptions do IAMs make?

    • What technological assumptions do IAMs make?

    • Elimination and overshoot of carbon dioxide

    • Many routes up to 15 ° C

  • Sectoral perspectives of climate scenarios

    • Regional, sectoral and technological coverage in IAM

    • Future energy mix at IAM

    • Understand sector-specific impacts

  • Bank assessments of climate scenarios (case studies)

    • Summary of UNEP FI transition risk approach used for bank case studies

    • Case studies and perspectives of the Bank on climate scenarios



Module 9: Climate Impact on Credit Rating


  • Analysis of Business Models of Corporate Credit Rating:

    • Moody's

    • Z-score

    • S&P

  • Main Financial Ratios

  • Data treatment

  • Univariate Analysis

    • Beta transformation

  • Selection of Variable Blocks

    • Principal component analysis

  • Qualitative Variables

  • Default definition

  • Temporal horizon

  • Multivariate models

    • Logistic regression

    • Multinomial Regression

  • Weight of qualitative and quantitative factors

  • Consistency Tests

  • PD Estimation and Calibration

  • Definition and creation of Master Scale

  • PD Mapping to Master Scale

  • Impact of climate risk

  • Methodology to integrate climate risk

  • Climate Risk Factors

  • Exercise 1: Univariate Analysis with Financial Ratios in Excel

  • Exercise 2: Analysis of Principal Components in SAS

  • Exercise 3: Multivariate Model in SAS

  • Exercise 4: Consistency Test in Excel

  • Exercise 5: Qualitative and Quantitative Factors of the Rating

  • Exercise 6: PD estimation and mapping to Master Scale

  • Exercise 7: Credit rating and climate risk

Module 10: PD and LGD Estimation


  • Default definition

  • PD PIT and PD TTC

  • Adjustments for transition risks of climate change in the PD

  • Adjustments for physical risks of climate change in the PD

  • Deficiency treatment and margin of conservatism (Moc)

  • PD estimation

    • Model development

    • Data requirements

    • Risk drivers and rating criteria

    • Processing of external ratings

    • Rating philosophy

    • Treatment of Pools

  • PD calibration

    • Data requirements

    • Calculation of one-year default rate

    • Calculation and use of observed average default rate

    • Long-run average default rate

    • Long-run average default rate calibration

  • LGD estimation

    • Methodologies for PD estimation

    • Data requirements

    • Recoveries from collaterals

    • Model development

    • Risk drivers

    • Collateral eligibility

    • Collateral inclusion

  • LGD Calibration

    • Definition of economic loss and realized loss

    • Treatment of commissions, interest and other withdrawals after default

    • Discount rate

    • Direct and indirect costs

    • Long-run average LGD

    • Calibration of the estimates to long-run average LGD

  • IFRS 9

    • Estimation of Lifetime PD

    • Estimation of Lifetime LGD



Module 11: Structural Models of PD


  • Merton's model

  • Physical Probability of Default

  • Black-Scholes-Merton model

  • Black–Cox model

  • Vasicek–Kealhofer model

  • CDS Pricing

  • Curves in liquidity and non-liquidity conditions

  • CDS Implied EDF

  • CDS Spreads

  • Fair Value Spread

  • CDS Spread in Sovereigns

  • DD Default Distance

  • Impact of climate change

  • Coal Price Sensitivity

  • Exercise 8: Estimation of CDS Spread and PD

  • Exercise 9: Estimate of EDF and DD adjusted for climate change


Module 12: LGD in LDP portfolios


  • Treatment of LGD in Low Default portfolio (LDP) portfolios

  • Problems in (LDP) portfolios

  • Market LGD Approach

  • Expert decision trees for modeling recovery

  • Linear and options approach:

  • Definition: LGD, RR and CRR

  • Treatment of collaterals

  • Linear approach to estimate LGD

  • Black-Sholes Options Approach to estimate LGD

  • Implied Market LGD Approach

  • Defaultable Bond

  • Implied LGD on CDS Spread

  • PD-LGD Models

    • The structural Merton LGD

    • Vasicek LGD

    • The Frye-Jacobs LGD

  • Exercise 10: Calibration and Optimization of Implied LGD in Solver and VBA

  • Exercise 11: The structural Merton LGD model


Module 15: Time series of climate change and projections

  • Financial and macroeconomic series in stress testing

  • Econometric Models

    • ARIMA models

    • ARCH models

    • GARCH models

  • Machine Learning Models

    • Supported Vector Machine

    • neural networks

    • deep learning

    • Recurrent Neural Networks RNN

  • Model Validation

    • Data processing

    • Non-Stationary Series

    • Dickey-Fuller test

    • Cointegration Tests

    • non-normality tests

    • heteroscedasticity

    • Outliers

    • autocorrelation

  • Backtesting of time series

    • Validation of machine learning models

    • Train test split

    • K-fold cross-validation

    • Walk-forward validation

  • Exercise 12: Non-stationary and cointegration series

  • Exercise 14: ARCH climate change modeling

  • Exercise 15: Facebook Prophet modeling climate change

  • Exercise 16: Machine Learning LSTM modeling of climate change

  • Exercise 17: Bakctesting machine learning time series


Module 16: Macroeconomic Scenarios 

  • IFRS 9 Macroeconomic Scenarios

  • climate scenarios

  • Converting Climate Scenarios to Macroeconomic Scenarios

  • Analysis of scenarios in EBA

  • Design of adverse scenarios

  • Financial and economic shocks

  • Important macroeconomic variables

  • Structural macroeconomic models

  • Bayesian VaR

  • balance models

    • Dynamic Stochastic General Equilibrium (DSGE)

  • Non-equilibrium models

    • Sensitivity Analysis

  • ​Integrated assessment model (IAM)

  • Computable general equilibrium (CGE)

  • Overlapping generation

  • input-output

  • agent-based

  • Scenario analysis

  • Expert judgment in stage design

  • Scenario severity score

  • scenario validation

  • Exercise 18: Advanced model of BVaR and DSGE macroeconomic scenarios

  • Exercise 19: Converting climate scenarios to macroeconomic scenarios

Module 17: Measurement and validation of Stress Testing Net Charge-Off

  • Stress Testing Net Charge-Off

    • Temporal horizon

    • Multi-period approach

    • Data required

    • Failed balance or penalty

    • Selection of Macroeconomic scenarios

    • Climate change scenarios

    • Charge Off

    • Net Charge Off

    • Losses on new impaired assets

    • Losses on old impaired assets

    • Net charge-off forecasting

  • Multivariate time series

    • Vector Autoregressive (VAR)

    • Vector Error Correction (VEC) Models

  • Machine Learning Models

    • Multivariate adaptive regression spline (MARS)

  • Stress test validation

    • performance metrics

    • Out of sample

    • Generalized Cross Validation – GCV

    • Squared Correlation - SC

    • Root Mean Squared Error – RMSE

    • Cumulative Percentage Error – CPE

    • Aikaike Information Criterion - AIC

    • backtesting

    • Temporal horizon

    • Magnitude of the error

  • Exercise 20: VAR stress testing model

  • Exercise 21: VEC stress testing model

  • Exercise 22: MARS stress testing model

  • Exercise 23: Validation and backtesting of VAR, VEC and MARS models

Module 18: Stress Testing in Credit Risk

  • Climatic Stress Testing

  • Climate Scenarios

  • Converting Climate Scenarios to Macroeconomic Scenarios

  • PD Stress Testing

    • Credit Portfolio View

    • Multiyear Approach ASRF

    • Reverse Stress Testing

    • Rescaling

    • Cox regression

  • Stress Testing of the Transition Matrix

    • Approach Credit Portfolio View

    • credit cycle index

    • Use of Credimetrics

    • Multifactor Extension

  • LGD Stress Testing

    • LGD Downturn: Mixed Distribution Approach

    • PD/LGD Multiyear Approach modeling

    • Frye-Jacobs PD/LGD modeling

    • Stress test and simulation of PD and LGD

  • ECL IFRS 9 Stress Testing

    • Transition matrix S1,S2 and S3

    • Changes in the stock of provisions

    • Changes in the stock of provisions of exposures S1

    • Changes in the stock of provisions of exposures S2

    • Changes in the stock of provisions of S3 exposures

  • Model risk in stress testing

  • Uncertainty in model specification

  • Uncertainty in the selected sample

  • Uncertainty in the scenarios

  • Mean Deviation (MD)

  • Exercise 24: Stress testing PD credit portfolio views approach and climate risk integration

  • Exercise 25: PD and LGD estimation Multiyear approach

  • Exercise 26: Stress Test LGD adjusted for climate risk

  • Exercise 27: Stress Test LGD, projection and simulation

  • Exercise 28: Stress Test of Transition Matrices


Module 19: Stress Testing Corporate Credit Risk


  • Temporal horizon

  • Data required

  • Main Macroeconomic variables

  • Impact on P&L, RWA and Capital

  • ASRF model

  • Creditmetrics model

  • Using Transition Matrices

  • Use of the credit cycle index

  • Default forecasting

  • Stress Test Methodology for corporate portfolios

  • Impact on RWA and Capital

  • Exercise 29: Stress Testing of corporate portfolio provisions using transition matrix and ASRF model in SAS, R and Excel


Module 20: Climate-Related Stress Tests and Scenarios

  • Introduction to Climate Change and Financial Risks

    • Overview of climate change and its global impact

    • Introduction to financial risks associated with climate change: physical and transition risks

    • The role of stress testing and scenario analysis in financial risk management

  • Fundamentals of Stress Testing and Scenario Analysis

    • Stress testing and scenario analysis: definitions, objectives, and methodologies

    • Designing effective stress tests and scenarios: considerations and best practices

    • Introduction to regulatory frameworks and guidelines (e.g., TCFD, Basel Accord)

  • Climate Risk Factors and Data Sources

    • Identifying and quantifying climate risk factors: acute and chronic physical risks, policy and legal risks, technology risks, market and reputation risks

    • Data sources and tools for climate risk assessment: climate models, satellite data, industry reports

  • Building Climate-Related Stress Tests and Scenarios

    • Developing climate change scenarios: RCP and SSP pathways, NGFS scenarios

    • Tailoring stress tests for credit risk portfolios

    • Practical session: Using statistical software for scenario development

  • Analyzing and Interpreting Results

    • Techniques for analyzing outcomes of stress tests and scenarios

    • Measuring potential impacts on credit risk metrics: PD, LGD, EAD

    • Case studies: Impact assessment of selected climate scenarios on different portfolio types

  • Strategic Implications and Decision Making

    • Integrating results into strategic planning and risk management frameworks

    • Developing mitigation and adaptation strategies for identified risks

    • Communication and reporting of stress testing outcomes to stakeholders

  • Regulatory Compliance and Future Trends

    • Navigating through climate-related regulatory requirements and expectations

    • Future trends in climate risk assessment and management

  • Exercise 30: Building Climate-Related Stress Tests and Scenarios

  • Exercise 31: Impacts on credit risk metrics on PD and LGD

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