September 27: Risk
Model Management of the Future
Conceptually, a good model is free of biases and robust in the face of uncertainty. In reality, executing a model risk management framework can be complex to manage people, processes, and systems effectively. You’ll look at frequently encountered challenges and potential remedies for traditional models, and learn how to include AI and machine learning algorithms into the model risk management framework.
The Toolbox for Model Risk Managers and Model Validators
As the variety and velocity of model developments increase, and constraints around the explainability and responsible use of machine learning models emerge, model risk managers and model validation practitioners need a suite of tools to meet regulatory, operational, and business needs. In this talk, you’ll discover tools that support the automation, documentation, and traceability of model governance and model validation processes that MathWorks has developed for model risk management.
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 the 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.
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.
The Secret to Automation and Lineage: MathWorks Model Inventory
The heart of any robust model risk management framework is the model inventory. However, many inventory systems are proving unable to keep up with the increased demands from regulators because they are effectively databases with a well-designed interface and lack the traceability and automation needs of a modern system.
While linking and drilling down on models, data, and documents is often the starting point for oversight discussions, the areas that are often overlooked are document management, interoperability, workflow automation, and customization through advanced analytics. These key areas provide enhanced cost reduction to the business by accelerating regulatory approval and eliminating inefficiencies.
In this session, you’ll see a live demonstration of the MathWorks Model Inventory, showcasing how several large global banks that adopted this solution addressed key challenges around model lineage, governance, automation, and review.
September 28: Deployment and Cloud
Panel Discussion on ModelOps in Quant Finance
Modeling and analytics can provide tremendous value to organizations across all industries and disciplines, and financial services is no exception. Financial institutions are investing significant time and capital to build modern analytics capabilities. While several of these institutions have teams with skills to build highly complex models, they face significant challenges in moving these models to production and getting buy-in from key stakeholders including business owners, internal compliance teams, and regulators.
In this panel session, learn how an agile ModelOps infrastructure can help organizations take their models from design to deployment in a cost-effective and timely manner. By effectively monitoring model performance to determine proactive actions, organizations can satisfy the needs of all stakeholders involved across the model lifecycle.
Modeling the Component-Based Analysis of Infrastructure Projects
Governments around the world try to attract private investors to infrastructure projects because public coffers cannot cover the costs and may not be suited for financing modern infrastructure solutions.
Investors finance infrastructure mostly through fixed-income debt instruments. It is easier to find private investors for infrastructure sectors like telecoms, airports, and ports. Government support is needed for regulated utilities and social infrastructure, such as educational and medical facilities and sewage and solid waste management.
Learn about a tool implemented in MATLAB® for simulating cash flows across a variety of scenarios that support decisions for investing in infrastructure projects by the World Bank.
Scalable Data Science Pipelines with QuSandbox and the MATLAB Online Server
With complexity in data science pipelines growing, organizations are redesigning tooling and infrastructure to build agile processes, sandboxes for experimentation, and integrations with multiple tools to meet the needs of distributed teams. In addition, the cloud has made high-performant, scalable, and elastic computing accessible to data scientists and quant modelers without needing to plan elaborate hardware and software setups.
In this talk, you’ll learn about QuSandbox, a rapid prototyping platform that makes access to data, modeling tools, and compute infrastructure accessible to modelers for building large-scale quant and data science applications in the cloud. QuSandbox supports multiple data integrations and modeling tools including the MATLAB Online™ server to enable quants and data scientists to learn by doing in a sandbox environment. You’ll hear about a recent use case where team of quants learned to build full-fledged data pipelines with the QuSandbox and prime the environment for analysis on the MATLAB Online server.
You’ll also see a case study where data from EDGAR was scraped, cleaned, and annotated and a sentiment analysis model was built using the MATLAB Online server. We will also illustrate how Amazon S3 was used for data staging and how MLFlow was used for tracking experiments and how the entire data pipeline was orchestrated using the QuSandbox.
Adopting MLOps at HSBC
Discover how HSBC focused on implementing a MLOps process within their global risk analytics team. You’ll hear a summary of the history of model development practices and the pitfalls encountered along the way, and also explore:
- The need for change and a new focus on engineering
- The implementation challenge and story so far
- The push to cloud and unleashing the possibilities of scale
Running MATLAB in Docker Containers
Containers are the universal building block for cloud and computing solutions that scale and adapt as needed. Learn about the basics of obtaining, building, running, and licensing MATLAB® in a Docker container.
September 29: AI, ESG, and Climate Modeling
Building a Responsible AI Pipeline
Digital transformation has exponentially increased with the shift to remote work during the pandemic, and when coupled with AI adoption as a strategic initiative across organizations, has created an environment of rapid change and great potential for innovation. With this move to digitize, automate, and integrate AI into financial workflows, organizations are struggling to maintain a robust and responsible modeling pipeline that is agile in meeting increasingly shorter and more demanding business timelines, leaving shorter intervals for research and innovation. In this talk, you’ll hear an overview of the area of “responsible AI” and best practices for building an agile and responsible AI pipeline to deliver research to production efficiently.
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.
Natural Language Processing for Finance with Transformer Models
Natural language processing (NLP) is a rapidly growing area of interest in the financial services industry as quants, risk managers, and financial analysts are all interested in deriving new alpha and insights from speech and text data. Common NLP models do not consider context-specific vocabulary, but transformer models that are pretrained to recognize financial jargon and nuance, such as FinBERT, can provide far better results.
In this session, you will learn how MATLAB® adds value and simplifies various fine-tuning tasks with apps such as Experiment Manager, Classification Learner, and Deep Network Designer.
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.
Modeling the Impact of Transition and Physical Climate Risks on a Portfolio of Mortgages
The 2015 “Paris Agreement” places a binding obligation on the world’s governments to “make finance flows consistent with a pathway towards low greenhouse gas emissions and climate-resilient development.” Regulators, customers, investors, and other stakeholders are driving financial institutions to do their part to transition to a low-carbon economy and manage exposure to climate-related risks. They’re using new data sources and developing new types of models, often leveraging methods from other scientific and engineering fields. Practitioners need software that provides a broad range of modeling functionality, flexible interfaces, rich visualization capabilities, collaboration, and review features to keep up with the pace of change in this area.
Learn how MATLAB® can get you started modeling both physical and transition climate risks. In a live demonstration, you will learn how to:
- Visualize flooding risk within a city (physical risk)
- Understand the impact of policies aimed at increasing the energy efficiency of buildings (transition risk)
- Model the impact of these risks on a portfolio of mortgages
September 30: Academic Practitioners
Developing Financial Thinking in Academia and Industry
Technological and regulatory changes create pressure for the financial services sector to evolve. Companies are responding to trends such as cloud computing, artificial intelligence, and climate change. The workforce is upskilling to keep pace with these trends.
As an educator or employer, how are you training students and employees to tackle present and future business needs? In this talk, you’ll explore the typical challenges customers encounter in developing financial thinking and resources they use to their advantage.
Quantitative Asset Management and Machine Learning for Institutional Investing
Using examples from his courses at Columbia University and his book, Quantitative Asset Management, Michael Robbins applies machine learning and factor investing to asset allocation. He demonstrates how leading institutions manage multibillion-dollar portfolios and includes real-world details like currency controls, market impact, and taxes. Learn about the entire investing process, from designing goals to planning, research, implementation, testing, and management.
Assessing the Role of Investors in the Realization of Climate Mitigation Pathways
Financial institutions face growing demand from investors and regulators to assess climate risk on their portfolios. Financial authorities recommend basing these assessments on NGFS scenarios (future trajectories of carbon prices and production output across the high- and low-carbon sectors of the economy, depending on future introductions of climate policies).
Even with these assessments, several challenges remain to integrate climate risk into risk management. The circularity between materialization of risk and risk perception can cause uncertainty about which scenario may occur. While risk is endogenous to finance in other contexts, in climate risk it impairs the use of standard risk assessment tools.
This talk is a collaboration with scientists of the IAM community involved in the development of NGFS scenarios and explains how a new framework can provide scenarios that assess the role of investors in the realizations of climate mitigation pathways.
Advanced Topics in Macro and Finance to Deal with Big Data
The big data era is creating a lot of exciting opportunities for new developments in econometrics, economics, and finance. The recent improvements in computer science and the internet are making data collection easier. Meanwhile, the analysis of large data sets poses methodological challenges (for example, high-frequency observations, unstructured data, and new large datasets) where MATLAB® can help and support the researchers. In this talk, explore the recent advancements in macroeconometrics and empirical financial analysis to deal with big data. See how machine learning techniques can be implemented in MATLAB to estimate macro and financial data for horserace forecasting and structural analysis.
Swap Volatility Dynamics and the Transmission of Systemic Risk in Hong Kong
Explore alternative measures of systemic risk for the financial sector based on forecast error variance decomposition as well as covariance-at-risk. You’ll see how to assess the relative importance of divergences in the implied volatility measures for swap-options for government securities issued in Hong Kong and the United States. In previous work on the mainland Chinese banking system, the effect of global and US economic uncertainty indices was transmitted to mainland China through divergences between the onshore CNY exchange rate and the offshore CNH exchange rate. Since Hong Kong has a hard peg with the dollar, discover how divergences in swap volatility dynamics affect the transmission of uncertainty indices to the Hong Kong financial system.
Adjunct Lecturer, Columbia University
Ben Steiner is chief-of-staff for Global Fixed Income at BNP Paribas Asset Management. He focuses on business management and strategic initiatives that help deliver long-term, sustainable returns for clients. Earlier in his career, Ben was head of model development, portfolio manager, and quant researcher at investment managers and quantitative hedge funds. Ben serves on the Board of Directors of the Society of Quantitative Analysts (SQA), and also is a part-time lecturer at Columbia University.
Malin Ortenblad is a consultant in healthcare. She has expertise in healthcare strategy consulting including machine learning for cancer research. Malin graduated from Columbia University with a master’s in business analytics.
Yao Shang is a data scientist for one of the Big Four accounting firms. She has worked with wealth managers at Morgan Stanley using deep learning techniques. Yao graduated from Columbia University with a master’s in operations research and holds several degrees from Rensselaer Polytechnic Institute.
Richard Wang has worked as a data analyst in industry and at banks including Citi and CITIC. He holds a Bachelor of Science in business and managerial economics from University of California, Davis, and will graduate from Columbia University with a master’s in management science.
Frank De Jonghe
Frank De Jonghe is a leader in quantitative analytics at a large consultancy, and is a visiting professor at UGent, Belgium, where he teaches quantitative risk management.
Paul Peeling is a principal technical consultant, focusing on the financial services industry in modeling credit and market risk. He also has expertise in signal processing, software design, and application development in MATLAB and big data for enterprise-scale data analytics.
Heng Z. Chen is responsible for supporting CCAR/DFAST loss forecast modeling and the group economic capital modeling in operational risks management at HSBC. 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.
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.
Ian McKenna leads a team that focuses on managing model risk for the financial services industry. Ian is responsible for the global adoption of the MATLAB model risk platform and oversees new business development, strategy, and analytics for the division. He has 10 years of experience in computational finance building applications focusing on asset allocation, risk management, time series forecasting, big data, and predictive modeling. His extensive hands-on experience is now used to develop and deploy risk management frameworks for the first and second lines of defense. Ian holds a Ph.D. from Northwestern University and a B.S. from the University of Florida in materials science and engineering with a minor in business administration.
Arpit Narain leads the financial solutions strategy at MathWorks. He is responsible for the expansion of quant modeling and AI- and machine-learning–based financial solutions business across sustainability/ESG, climate-related risk, trusted AI, financial risk and compliance, model risk, trading, and investment management.
Arpit has 13 years of experience in financial quant modeling and artificial intelligence space, leading projects for top investment banks, commercial banks, asset management companies, hedge funds, insurance firms, and regulators across the Americas, Europe, and APAC. He also established and led the Quant Risk Modeling & Derivatives Valuations practice at KPMG Global Services.
Oleh Khalayim is a monitoring and evaluation officer at the World Bank Group in Washington, D.C., and prior to that worked as a strategy officer at IFC. He also led a division at the Ministry of Economy, and taught at Kyiv-Mohyla Business School in Ukraine.
Oleh worked with the World Bank Treasury to develop a tool based on MATLAB® to analyze infrastructure project investability improvement.
Oleh earned a bachelor’s degree at the Ivan Franko National University of Lviv and a master’s degree at Harvard University.
Sri Krishnamurthy, CFA, CAP, is the founder of www.QuantUniversity.com, a data and quantitative analysis company specializing in large-scale machine learning and AI solutions. He is the inventor of QuSandbox, a platform to enable rapid experimentation for AI and machine learning experiments. Prior to starting QuantUniversity, Sri worked at Citigroup, Endeca, and MathWorks. He also serves on the steering committee of QWAFAFEW and is a reviewer of the Journal of Asset Management
Sri earned an M.S. in computer systems engineering and an M.S. in computer science from Northeastern University and an M.B.A. with a focus on investments from Babson College.
Scott Nicholas is a senior product manager in the Online Products area at MathWorks. Scott specializes in platforms: operating systems, architectures, compilers, and containers. He holds a B.A. in computer science from Skidmore College.
Stuart leads product management for Quantitative Finance and Risk Management at MathWorks, and has over 15 years of experience working with banking, buy-side, sell-side, and insurance firms on adopting technology to solve business challenges.
He is interested in the adoption of model-led technology in financial services, working with risk managers, modelers, developers, and business stakeholders on processes and technology solutions for risk management.
Stuart holds the Financial Risk Manager designation from the Global Association of Risk Professionals and an M.B.A. from Carnegie Mellon University. He also earned master’s degrees in chemical and electrical engineering.
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.
Lawrence is a member of the Application Engineering team at MathWorks, assisting customers in the development and deployment of financial applications. Prior to joining MathWorks, he worked as a quantitative developer for an equity index provider, where he contributed to the development of equity strategies replicated by various asset managers and pension funds. Lawrence holds an M.Sc. in financial markets from EDHEC Business School.
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 the Environment, and a Ph.D. in natural resource economics from Oregon State University.
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.
Abhishek Gupta is the manager of education customer success at MathWorks. He leads a technical team that partners with universities to support teaching and research goals across engineering, science, and business disciplines. Focus areas include improving student outcomes and retention, preparing students for industry, incorporating computational and systems thinking in classrooms and online learning, and taking advantage of the latest technology to conduct multidisciplinary research. Prior to this role, he was an application engineer at MathWorks, consulting with customers in industry and academia on machine learning and computational finance. Abhishek graduated with an M.S. in mechanical engineering from Texas A&M University.
Michael Robbins is a professor at Columbia University, where he teaches investing, including graduate classes in global macroeconomic tactical asset allocation (GTAA), asset/liability management (ALM), and environmental, social, and governance (ESG) investing. He specializes in governance, asset allocation, and manager search and selection. Michael has been the chief investment officer of five large investment firms and was the chief risk officer for the state of Utah’s systems. His book Quantitative Asset Management: Factor Investing and Machine Learning for Institutional Investing will be published by McGraw-Hill in 2022.
University of Zurich
Stefano Battiston is an associate professor at the University of Zurich and an associate professor at Ca' Foscari University of Venice. He is a leading scholar in the field of network models of systemic risk and sustainable finance.
He has co-authored more than 50 publications in journals such as PNAS, Nature Communications, Nature Climate Change, and Management Science. His scientific background in complex systems, combined with 15 years of research in economics and finance, puts him in a unique position to understand policy issues both from a quantitative and holistic perspective.
He has also been the coordinator of the EU Future Emerging Technologies projects SIMPOL and DOLFINS, investigating how to improve financial stability and channel finance towards sustainability in a networked economy.
University College Dublin
Alessia Paccagnini is a tenured academic at University College Dublin, Smurfit Business School, where she is the academic director of the Master’s in Quantitative Finance and co-chair of the Women in STEM Committee. She is also a research associate at the Centre for Applied Macroeconomic Analysis (CAMA), scientific communication manager of the EU-Funded H2020 COST Action Fintech and Artificial Intelligence in Finance, and member of the editorial board of Forecasting. Alessia earned her bachelor’s, master’s, and Ph.D. degrees at Bocconi University.
Paul McNelis has held the Robert Bendheim Chair in Financial and Economic Policy at the Gabelli School of Business, Fordham University, since 2005. Prior to that, he was a member of the Economics Department at Georgetown University in Washington, D.C. Paul’s research has been in computational macroeconomics, nonlinear econometrics, and methods of machine learning for finance.
Matt Barnes is a project manager at HSBC. He has worked in the financial sector for over 25 years as a technologist and has also worked in partnership with MathWorks in support of wholesale capital models adopted globally across the bank.
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