Operational Risk Capital Modeling for Extreme Loss Events
Operational risk modeling using the parametric models can lead to a counterintuitive estimate of value at risk at 99.9% as economic capital due to extreme events. To address this issue, a flexible semi-nonparametric (SNP) model is introduced using the change of variables technique to enrich the family of distributions that can be used for modeling extreme events. The SNP models are proven to have the same maximum domain of attraction (MDA) as the parametric kernels, and it follows that the SNP models are consistent with extreme value theory and peaks over threshold method but with different shape and scale parameters. By using the simulated data sets generated from a mixture of distributions with varying body-tail thresholds, the SNP models in the Fréchet and Gumbel MDAs fit the data sets by increasing the number of model parameters, resulting in similar quantile estimates at 99.9%. When applied to an actual operational risk loss data set from a major international bank, the SNP models yield economic capital estimates 2 to 2.5 times as large as the single largest loss event and exhibit a reasonable stability towards the change of loss history in the scenario analysis.
At HSBC, Heng Z. Chen is responsible for supporting CCAR/DFAST loss forecast modeling and the group economic capital modeling in operational risks management. He is also an adjunct professor at Northwestern University. Prior to joining HSBC, Heng was a team lead and senior manager at Discover Financial Services and GE Capital.
Heng received his two M.S. degrees from the University of California at Davis, and holds a Ph.D. degree from The Ohio State University.
Optimizing Portfolios for Net Zero with Real Assets
Nuveen Real Assets is pioneering a portfolio optimization framework to help investors maximize risk-adjusted returns alongside carbon outcomes, such as net zero emissions by 2050. The framework adapts the standard mean-variance portfolio optimization model to include a third dimension—carbon emissions—alongside traditional risk and return inputs. This tool is able to select portfolio structures for any possible level of emissions that is desired, such as net-zero or even net-negative emissions. The solution is a set of “carbon efficient frontiers,” with each frontier representing an optimal portfolio that maximizes return for a given level of risk and net carbon emissions. Understanding these potential trade-offs and the portfolio benefits of nature-based climate solutions, as well as quantifying risk return and carbon in a unified framework, can help inform asset allocation and portfolio design for net zero.
Gwen Busby is the head of research and strategy at GreenWood Resources, an investment specialist of Nuveen. Gwen focuses on timberland, forest product market analysis, and specialized econometric and stochastic modeling. Prior to joining GreenWood Resources, Gwen worked as an assistant professor of natural resource economics and quantitative methods at Virginia Tech and as a senior scientist at the University of Virginia. Gwen graduated with a B.A. in economics from Middlebury College, an M.E.Sc. from the Yale School of of the Environment, and a Ph.D. in natural resource economics from Oregon State University.
Combining Human and Computer Intelligence in Asset Allocation
In this talk, learn how systematic investing brings about the new face of wealth management, marrying human and computer intelligence.
Discuss market analysis using data science, risk analysis before extreme market events, and portfolio construction under cutting-edge optimization methods. Discover real-life trading and portfolio management from the perspective of the technology and scientific-oriented professional. You’ll also see how the set of modules that integrate the investment process are produced and ensembled in MATLAB®, showing the end-to-end capabilities of the different toolboxes used to offer a complete solution to wealth advisors and discretionary institutional portfolio managers.
Emilio Llorente is founding partner and head of investments at Recognition AMS. He has more than 20 years of experience in the multi-asset management industry, is a designated expert on machine learning methods applied to markets, and is a pioneer in the application of machine learning, genetic algorithms, high performance computing, and man-machine collaboration technology in portfolio management. Emilio has a B.A. in economics from Universidad de Oviedo, a master’s in finance from Universidad Pontificia de Comillas, ICADE, and an M.Sc. in Artificial Intelligence from the University of Edinburgh. He is a certified member of the Global Association of Risk Professionals.
Using Energy-Economic Models for Climate-Related Financial Impact Analysis
Climate change poses financial risks that arise from shifts in the political, technological, social, and economic landscape that are likely to occur during the transition to a low-carbon economy. One of the global community’s most significant contemporary challenges is the need to satisfy growing energy and food demand while simultaneously achieving very significant reductions in the greenhouse gas emissions and sustainable development. In pursuing this goal, decision makers need to make strategic choices that address both physical risks (damage from extreme events such as fires, floods, droughts, and sea-level rise) and transition risks (financially consequential shifts in political, technological, social, and economic landscapes in the transition to a low‑carbon future). Energy-economic models can be used to support decision makers in quantifying these risks by integrating across systems, sectors, and scales. Learn about a framework for addressing climate-related financial risks where scenario analysis plays a key role in climate risk management.
Dr. Sergey Paltsev is a director of the MIT Energy-at-Scale Center, a senior research scientist at MIT Energy Initiative, and a deputy director of the MIT Joint Program on the Science and Policy of Global Change. He is the lead modeler in charge of the MIT Economic Projection and Policy Analysis (EPPA) model of the world economy. Sergey is the author of more than 100 peer-reviewed publications in scientific journals and books on energy economics, climate policy, transport, advanced energy technologies, and international trade. Sergey was also a lead author of the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) and was the recipient of the 2012 Pyke Johnson Award.
Using MATLAB to Move to the Next Generation of GRADE Model
Euler Hermes (EH) is the leading B2B credit risk business of the Allianz Group, helping customers protect themselves from bad debt.
EH has a strategic objective to centralize all credit assessment model calibration data, model design, and model monitoring processes in one common modeling platform, helping to meet the regulatory requirement for reconciliation and transparency for all credit assessment models. EH’s proprietary GRADE model is a probability of default (PD) model used in both the underwriting process and in the allocation of risk capital.
In 2020, EH launched a transformation project to migrate all credit risk models from a legacy infrastructure to MATLAB® running on AWS®. EH used components of the MATLAB Model Risk Management solution to develop and maintain the full suite of credit risk models, which are based on fuzzy logic approaches as well as tree-based algorithms.
In this presentation, learn how EH has built a new model design architecture with MATLAB and AWS that will allow many improvements in the process of building and testing future models.
Nadège Lespagnol is group head of credit models at Euler Hermes and prior to that was a credit risk advisory partner at Deloitte. She has over 22 years of experience in the financial services industry. Nadège has also joined Time for the Planet as a partner to focus on ESG and climate risk.
- Model Management of the Future
- The Toolbox for Model Risk Managers and Model Validators
- Operational Risk Capital Modeling for Extreme Loss Events
- Using MATLAB to Move to the Next Generation of GRADE Model
- The Secret to Automation and Lineage: MathWorks Model Inventory
Deplyment and Cloud
- Panel Discussion on ModelOps in Quant Finance
- Modeling the Component-Based Analysis of Infrastructure Projects
- Scalable Data Science Pipelines with QuSandbox and the MATLAB Online Server
- Adopting MLOps at HSBC
- Running MATLAB in Docker Containers
AI, ESG, and Climate Modeling
- Building a Responsible AI Pipeline
- Combining Human and Computer Intelligence in Asset Allocation
- Natural Language Processing for Finance with Transformer Models
- Using Energy-Economic Models for Climate-Related Financial Impact Analysis
- Modeling the Impact of Transition and Physical Climate Risks on a Portfolio of Mortgages
- Developing Financial Thinking in Academia and Industry
- Quantitative Asset Management and Machine Learning for Institutional Investing
- Assessing the Role of Investors in the Realization of Climate Mitigation Pathways
- Advanced Topics in Macro and Finance to Deal with Big Data
- Swap Volatility Dynamics and the Transmission of Systemic Risk in Hong Kong
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