MATLAB Computational Finance Conference 2021
9月27日至30日|在线的
上午9:00至下午12点东部日光时间(EDT)
MATLAB计算金融会议2021会议汇集了行业专业人员,以展示现实世界中的MATLAB,并通过互动小组讨论和深入的演示提供了从业人员的建议。主题包括AI,可持续投资,气候风险,投资组合和风险管理,云部署以及为金融教育者和研究生设计的新学术轨道。
MATLAB计算金融会议2021会议汇集了行业专业人员,以展示现实世界中的MATLAB,并通过互动小组讨论和深入的演示提供了从业人员的建议。主题包括AI,可持续投资,气候风险,投资组合和风险管理,云部署以及为金融教育者和研究生设计的新学术轨道。
每日会议的重点是相关的行业主题,并包括主题演讲或小组会议。
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.
在汇丰银行(HSBC),Heng Z. Chen负责支持CCAR/DFAST损失预金宝app测建模和运营风险管理中的集体经济资本建模。他还是西北大学的兼职教授。在加入汇丰银行之前,Heng是Discover Financial Services和GE Capital的团队负责人和高级经理。
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.
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.
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.
气候变化构成了由于在过渡到低碳经济期间可能发生的政治,技术,社会和经济格局的转变所带来的财务风险。全球社区最重大的当代挑战之一是需要满足不断增长的能源和粮食需求,同时在温室气体排放和可持续发展方面同时减少。在追求这一目标时,决策者需要做出战略选择来解决这两者身体风险(damage from extreme events such as fires, floods, droughts, and sea-level rise) andtransition risks(政治、经济上重要的转变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.
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.
NadègeLespagnol是Euler Hermes的信用模型集团负责人,在此之前是Deloitte的信用风险咨询合作伙伴。她在金融服务行业拥有超过22年的经验。Nadège还加入了地球的时间,作为合作伙伴,专注于ESG和气候风险。
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