AI and Quantum
CRISK: Quantifying the Expected Capital Shortfall in a Climate Stress Scenario
In this conference presentation, we examine a market-based methodology to assess banks' resilience to climate-related risks. This methodology is based on the work done by the economists at the Federal Reserve Bank of New York and other academics, including the Nobel Prize winner Bob Engle, and has been published in the Fed staff working paper. Additionally, a large Asian central bank replicated the methodology for their geography and published the results.
The methodology examines the climate-related risk exposure of large global banks using a novel metric called CRISK, which represents the expected capital shortfall in a climate stress scenario. It also introduces climate risk factors and measures banks' stock return sensitivity—referred to as climate beta—towards these factors.
This presentation highlights the significance of this approach in assessing and managing climate-related risks at the financial institution level and emphasizes its potential in enhancing the financial sector's resilience to climate change.
Michael Robbins is a professor at Columbia University, where he teaches quantitative investing, including graduate classes in global tactical asset allocation (GTAA) and environmental, social, and governance (ESG) investing. He has been the chief investment officer of six large investment firms, including a bank with more than eight million clients. He has managed pensions, endowments, and family offices, and was the chief risk officer for the state of Utah’s systems.
Michael specializes in governance, asset allocation, and manager search and selection—including investment and operational due diligence—and has extensive experience in complex structures and operations.
Michael’s new book, Quantitative Asset Management, was published by McGraw Hill.
Arpit Narain is responsible for quant modeling and artificial intelligence business solutions strategy for the climate risk & ESG, financial risk, model risk, trading, and investment management areas at MathWorks. He has 16 years of experience in the quant finance and AI space, leading large projects for top global banks, asset managers, hedge funds, and regulators across the Americas, Europe, and Asia-Pacific.
Prior to joining MathWorks, Arpit was a director at KPMG’s New York quant practice, where he co-established the traded risk business and led significant client engagements on model risk, derivatives pricing, and portfolio analytics. He also established and led the quant modeling & derivatives valuations practice at KPMG Global Services. He is a visiting professor at the quant finance master‘s programs at NYU and Rutgers.
Arpit holds an electronics engineering degree, along with CFA, FRM, and CQF designations.
Nonlinear Confidence Bands Computation in MATLAB
The global dynamic stochastic general equilibrium model for forecasting main macroeconomic variables like GDP, inflation, and unemployment is nonlinear. There is a crucial need to compute confidence bands around the projections in order to establish the uncertainty about them, detect escalated up/down risks, and calculate useful statistics like recession and deflation probabilities.
Confidence intervals are calculated by drawing samples from the estimated distributions of exogenous shock terms and each time solving the system of equations using a nonlinear solver in MATLAB®. The standard method, Monte Carlo sampling, is not practical due to the enormous number of drawings needed. We opted for a more structured way of drawing the shocks in order to more evenly sweep the high dimensional space—Latin hypercube sampling–which we have implemented in MATLAB. This sampling technique implies a faster convergence; in other words, a smaller number of simulations is needed to obtain good estimates of the confidence bands.
Furthermore, we do use distributed computing in MATLAB over a cluster of servers to speed up the process even further. System solution and calculations are sent to more than 100 workers on a cluster of several servers, then results are collected and compiled, which enables the whole calculation to be completed overnight instead of taking months if none of these methods were involved.
International Monetary Fund
Kadir Tanyeri is a senior computational economics expert in the IMF’s Information Technology department. He works with the Research department and various other departments, regions, and desks to design, develop, maintain, support, and train macroeconomic models. Mr. Tanyeri has a Ph.D. in stochastic modeling from the George Washington University.
Keynote: Transformational Technologies: Empowering Financial Professionals with MATLAB
In this keynote presentation, we explore the transformative impact of MATLAB® in the field of quantitative finance. We focus on four critical areas that have revolutionized workflows for finance professionals, leading to increased productivity and innovation.
First, MATLAB's advanced automation capabilities have streamlined manual processes, saving valuable time for financial experts. By automating repetitive tasks, professionals can now focus on strategic decision-making and in-depth research, resulting in more insightful and data-driven outcomes.
Second, MATLAB has democratized model development and deployment by integrating low-code and no-code workflows. This shift allows non-programmers to actively participate in the quantitative finance domain, fostering a more inclusive and collaborative environment. The diverse expertise of individuals from various backgrounds contributes to novel financial solutions.
Next, the seamless integration of AI and quantum technologies in MATLAB enables finance engineers to develop intelligent algorithms and solve complex financial problems rapidly. Leveraging these transformative technologies empowers professionals to achieve breakthroughs in risk assessment, asset management, and predictive analytics, paving the way for new dimensions of financial success.
Finally, the incorporation of ModelOps within MATLAB has revolutionized model deployment and monitoring. Continuous monitoring and adaptive management ensure that financial models remain dynamic and effective, even in changing market conditions, bolstering overall financial resilience.
These transformational technologies have ushered in a new era of productivity and innovation in quantitative finance, with MATLAB playing a central role as an enabler of progress. As the financial world continues to evolve, these advancements will empower finance professionals, shaping the future of the industry and unlocking new possibilities for success.
With over 17 years of experience at MathWorks, David Willingham is currently leading the quantitative finance team in North America. Previously, he served as principal deep learning product manager, collaborating with users and developers to build out and deliver on MathWorks AI product strategy. Prior to that, David spent a decade as a senior applications engineer in data analytics, providing technical guidance to users, particularly in the financial, energy, and mining sectors in Australia. His expertise includes machine learning, deep learning, data mining, optimization, statistics, and more. David is passionate about empowering others and is motivated by the achievements of those around him. His dedication to AI and computational finance drives his continuous pursuit of innovative solutions in this dynamic field.
Modeling the Impact of Climate Change on Insured Losses in France
Caisse Centrale de Réassurance (CCR) is a public reinsurer operating in France. CCR provides coverage against earthquakes, drought (clay shrinkage and swelling), floods, storm surge, and tropical storms, as well as terrorism and civil nuclear liability. Within CCR, the R&D Modeling department develops hazard, vulnerability, and damage models that allow CCR to understand the risk exposure of the French territory and estimate the insured losses from catastrophic events.
In this talk, learn how CCR uses climate simulations provided by Météo-France along with its own catastrophe models to assess how hazards and insured losses in France will evolve in 2050. CCR and Météo-France use constant climate simulations: for a chosen target year (2000 or 2050) and a chosen IPCC scenario (current climate, RCP 4.5, or RCP 8.5), they compute 400 repetitions of the target year at an eight kilometer spatial resolution and an hourly temporal resolution. This original methodology allows CCR to estimate return periods for extreme events for a given target year and compare their annual probability of occurrence in a changing climate.
Caisse Centrale de Réassurance
Léa Boittin is a catastrophe modeler within the R&D Modeling department of CCR, focusing on the computation of insured losses. Before joining CCR, she worked at RMS in London. Léa holds a Ph.D. in applied mathematics from Sorbonne Université and a double degree in engineering from École Centrale Paris and Politecnico di Milano.
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