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featured sessions
thierry roncalli, amundi
junior maih, norges bank
elre oldewage, mathworks
agenda
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welcome
welcome
welcome
thierry roncalli, amundi
thierry roncalli, amundi
keynote: sustainable finance with matlab
join thierry roncalli, head of quantitative research at amundi, as he explores the practical applications of matlab® in teaching sustainable finance. this presentation covers key topics like carbon taxes, portfolio decarbonization, and stress testing. thierry will also highlight exciting results from his recent research and provide a sneak peek into his forthcoming book, handbook of sustainable finance (march 2025).
thierry roncalli
thierry roncalli is head of quantitative research at amundi and an adjunct professor of economics at the université d’évry paris-saclay. prior to joining amundi in november 2016, he was head of research and development at lyxor asset management. thierry has been a member of the scientific advisory board of the amf, the french securities, and financial markets regulator since february 2017. he is the author of numerous academic articles in scientific journals and has published several books on risk and asset management. his new book, handbook of sustainable finance, will be published in march 2025.
paul peeling, mathworks
paul peeling, mathworks
innovations in the delivery of ai-powered financial services
explore the latest advancements for accelerating the delivery of financial services, and discover how cloud-connected and cloud-hosted software, including private and public cloud options, can bring ai-powered financial services to market. in the midst of the hype, ai, deep learning, and large language models are rarely acceptable for production use, unless their development and implementation has been carried out in a transparent, explainable and reproducible manner.
see how a modelops platform with data governance, continuous integration, and testing is built from reference architectures to leverage the growing plethora of technologies in a coherent, reproducible, and transparent workflow. this will include:
- matlab® in jupyterhub and data science platforms.
- verification, documentation, and management of deep learning networks.
- deployment and monitoring of containerized models.
paul peeling
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.
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elre oldewage, mathworks
elre oldewage, mathworks
navigating climate finance: software solutions for climate risk management
in the evolving landscape of climate finance, the need to incorporate climate impacts into risk management strategies presents significant challenges and opportunities. this presentation leverages empirical data and real-world experiences to explore the intricacies of managing climate-related financial risks through software applications. explore workflows around geolocating physical assets, hazard identification, financial impact estimation, and the integration of climate risk into operational frameworks. by examining case studies, this talk demonstrates the pivotal role of software solutions in transparent, scalable, and reproduceable climate risk management.
elre oldewage
elre oldewage is an application engineer at mathworks specializing in machine learning, finance, and climate risk. she joined mathworks in 2023 after completing her ph.d., which focused on robustness in machine learning and how second-order optimization can be used to improve model training. elre holds an m.sc. in computer science from the university of pretoria and a ph.d. in engineering from the university of cambridge.
pablo garcía estébanez, banco sabadell
pablo garcía estébanez, banco sabadell
counterparty risk assessment with two steps: monte carlo and parallel computing
in this presentation, discover a novel approach to counterparty risk assessment using a two-step process: monte carlo simulations and parallel computing. this method leverages monte carlo simulations to model and quantify risk under various scenarios, followed by parallel computing to efficiently process and analyze large datasets. learn how this integrated approach enhances the accuracy and speed of risk assessments, enabling more robust financial decision making. see practical examples and insights drawn from extensive experience in quantitative analysis and financial modeling.
pablo garcía estébanez
pablo garcía estébanez is a quantitative risk analyst at banco sabadell with over 20 years of experience in the automotive and financial industries. his work focuses on high-performance control algorithms, model estimation, and the application of advanced data science techniques. pablo holds a master of science in electrical and electronics engineering from centralesupélec and a master of science in industrial engineering from universidad politécnica de madrid. pablo has completed specializations in probabilistic graphical models from stanford university and machine learning and reinforcement learning in finance from nyu tandon school of engineering.
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christian dzierzon, hannover re
christian dzierzon, hannover re
automation of an internal model based on matlab core calculations
the internal model of an insurance company models stochastic cash flows to predict figures such as required solvency capital for a one-year time horizon. growing over the past twenty years from rather simple to more complex and sophisticated, hannover re’s internal model was based on numerous manual processes until 2018, when they started an automation project. as matlab® usage has steadily increased over the years, it was only natural to build an automated version based on matlab as a programming language. in this presentation, learn how hannover re managed to keep flexibility, reduce run time and manual steps, avoid redundancies, and meet regulatory and compliance requirements.
christian dzierzon
christian dzierzon is head of the model development team at hannover re, where he has been since 2007. over his tenure at hannover re, he has worked on various projects focusing on financial modeling, risk management, and process automation. christian studied mathematics and physics at the university of bremen and has a ph.d. in category theory.
martin tarlie, gmo
martin tarlie, gmo
wealth and asset management platform development with matlab web app server
nebo wealth is an award-winning, open architecture asset management platform that represents a leap forward in goals-based investing. our pioneering approach to asset allocation and portfolio design—built on a big idea that risk is not volatility but “not having what you need, when you need it”—delivers the personalization that clients expect with the scale and efficiency that advisors demand. in this presentation, dr. martin tarlie will motivate the problem, introduce some of the key ideas, and illustrate how gmo has used matlab® to create both a web-based application and a scalable api-enabled platform.
martin tarlie
dr. tarlie is a member of gmo’s asset allocation team and serves as the nebo product lead. prior to re-joining gmo in 2018, he was a managing director at qma. he previously worked on gmo’s global equity team from 2007 to 2014. prior to that he worked at breakwater trading and at marlin capital corp as a fundamental equity analyst and the director of research. dr. tarlie earned his bachelor's degree in physics from the university of michigan, his phd in theoretical condensed matter physics from the university of illinois at urbana-champaign, and his mba from the university of chicago. he was also a postdoctoral research fellow at the james franck institute at the university of chicago and is a cfa charterholder.
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welcome
welcome
welcome
alexander diethert, mathworks
alexander diethert, mathworks
keynote: harnessing advanced financial modeling technologies with matlab
financial modeling stands as the bedrock of decision making in the finance industry, yet it is undergoing transformative changes in today's fast-paced and complex environment. rapid advancements in mathematical methods, heightened regulatory demands, the transformative potential of cloud computing, the explosion of accessible data sources, and the rise of (generative) ai are revolutionizing the field. these developments make the work of financial professionals both exhilarating and challenging.
modern modeling tools must harness these technologies in a user-friendly, transparent, and customizable manner. in this presentation, see how practitioners leverage matlab® to tap into a comprehensive ecosystem of data and tools for swift and robust modeling. explore compelling examples, including econometric modeling tools, climate scenarios, geophysical data sources, and collaboration in multilanguage setups. discover how matlab empowers financial professionals to stay ahead in an ever-evolving landscape.
alexander diethert
dr. alexander diethert is a senior financial application engineer at mathworks in munich, germany. he leads a team of technical experts who empower analysts, economists, and it people around the world to optimize their use of mathworks technology. prior to joining mathworks, alexander worked as a consultant in the financial services area. he holds a diploma in mathematics and a ph.d. in physics.
junior maih, norges bank
junior maih, norges bank
rise toolbox: advancing regime-switching models for macroeconomic analysis
the rise toolbox is designed for solving, estimating, and analyzing nonlinear regime-switching dsge models. regime-switching models are essential for addressing the complexities of economic environments, such as the zero lower bound, financial crises, and high inflation periods. see how rise accommodates various macroeconomic models, including dsge vars, panel vars, and optimal policy frameworks. gain insights into solving these models using rise, the benefits of regime-switching for economic forecasting, and real-world applications in policy analysis and simulation.
junior maih
junior maih is the lead author of the rise toolbox for regime-switching models and is a senior economist and special adviser at norges bank. alongside the rise toolbox, he contributes to developing toolboxes for solving and estimating structural macroeconomic models, notably dynare. he has previously worked at the international monetary fund in the research department's modeling division. junior received his ph.d. in economics from the university of oslo and is an associate professor at the norwegian business school (bi).
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konstantinos theodoridis, european stability mechanism
konstantinos theodoridis, european stability mechanism
nonlinear dynamics of oil shocks: dynare, matlab, and matlab parallel server
this presentation employs threshold var models to investigate the asymmetric impact of oil supply news shocks, analyzing variations in both the size and direction of the shocks. our findings reveal that large and adverse oil shocks exert a stronger effect on real activity, labor market indicators, and risk variables than small and favorable shocks. interestingly, we observe no asymmetry in the response of prices and monetary policy to oil shocks of different magnitudes and signs. supported by a theoretical framework integrating search and matching frictions with epstein-zin preferences, the study utilizes dynare, matlab®, and matlab parallel server™ to enhance model estimation and provide valuable insights into the nonlinear dynamics of oil shocks.
konstantinos theodoridis
konstantinos theodoridis is a principal economist in the economic and market analysis division at the european stability mechanism . he is also a professor of macroeconomics at the cardiff business school. prior to joining the esm, professor theodoridis held various positions at the bank of england, including head of the model development team in the conjunctural assessments and projections division. professor theodoridis holds an m.sc. degree in international economics and public policy and a ph.d. in economics from cardiff university. he is the author of numerous economic papers and publications focusing on macro-financial issues and monetary policy.
azar sultanov, international monetary fund
zamid aligishiev, international monetary fund
zamid aligishiev, international monetary fund
azar sultanov, international monetary fund
zamid aligishiev, international monetary fund
zamid aligishiev, international monetary fund
the dignad model: applications and a new toolkit
building resilience to natural disasters is imperative due to the rising threats posed by climate change, particularly for vulnerable developing economies. the dignad (debt-investment-growth and natural disasters) model was developed to analyze macro-fiscal implications of climate shocks and the role of economic policies in mitigating associated risks. dignad is a small open economy model that allows users to assess debt sustainability risks linked to major natural disasters while explicitly considering the need to rebuild public infrastructure. its rich general equilibrium structure allows for the construction of counterfactuals as well as broader scenario analysis involving ex-ante policies such as investing in resilient infrastructure, increasing fiscal buffers, and improving public investment efficiency. the recently developed dignad toolkit offers a user-friendly interface, making the model accessible to users with limited experience coding with dynare.
azar sultanov
azar sultanov is a senior economist in the research department at the international monetary fund, where he has worked since may 2022. he specializes in macroeconomics of climate change and adaptation in developing and emerging market economies. prior to joining the imf, he was working at uncdf on remittances and migration with a focus on developing countries. he has also been a visiting lecturer at the city university of london. he worked in a variety of roles in the international bank of azerbaijan and central bank of azerbaijan, and for cesvi, italian humanitarian organization, on various rehabilitation and reconstruction projects in turkey. he holds a ph.d. in economics and an m.sc. in financial economics from the university of london. his doctorate focused on how migration affects economic dynamics and fiscal policy. he also holds a master’s degree in european law from marmara university, and a b.a. in business administration from boğaziçi university, turkey.
zamid aligishiev
zamid aligishiev is an economist in the western hemisphere department of the international monetary fund, where he has worked since june 2023. he specializes in macroeconomics of climate change and supports the resilience and sustainability facility programs. previously, he worked in the imf's research department modeling economic impacts of the covid-19 pandemic and natural disasters in low-income developing countries. earlier in his career, zamid also contributed to macroeconomic surveillance and country risk assessments at the european stability mechanism and consulted with uncdf on financial inclusion and survey design. he holds a ph.d. in economics and an m.sc. in international business economics from the city university of london, and a specialist diploma in banking from the financial university under the government of the russian federation.
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monika sabolova, ice
girish narula, ice
marshall alphonso, mathworks
girish narula, ice
marshall alphonso, mathworks
monika sabolova, ice
girish narula, ice
marshall alphonso, mathworks
girish narula, ice
marshall alphonso, mathworks
is decarbonization gaining momentum? exploring ice climate data insights
this session examines whether global decarbonization efforts are progressing by analyzing the latest trends using ice's climate transition finance data. monika sabolova and girish narula from ice, together with marshall alphonso from mathworks, will present key findings on carbon emissions, intensities, and net-zero alignment across corporate portfolios. learn how this data is effectively processed and visualized using matlab®, providing actionable insights for professionals focused on advancing sustainability initiatives.
monika sabolova
monika sabolova is a climate product engagement manager at ice, where she specializes in the integration of climate transition finance data into investment strategies. with a strong background in financial markets, fixed income, and sustainable finance, she previously held roles at alliancebernstein and legg mason, focusing on responsible investing and product management. monika holds a master's degree in international business from esei international business school.
girish narula
girish narula is the head of sustainable finance at ice, where he leads the climate transition finance team, focusing on integrating climate risk analytics into the financial sector. previously, he served as the ceo of urgentem, a leading provider of carbon emission data and climate risk analytics. with over 20 years of experience in risk management, financial analysis, and strategic business planning, girish has worked across geographies including india, the us, the uk, and africa. he holds an m.b.a. from the university of oxford and a b.tech. in aerospace engineering from the indian institute of technology, kharagpur.
marshall alphonso
marshall alphonso, senior engineer at mathworks, specializes in quantitative finance and is currently the global lead engineer for five top banks. he has more than 10 years of experience training clients at more than 250 companies including top hedge funds, banks, and other financial institutions around the world.
his prior experience includes design of artificial intelligent systems and advanced statistical signal processing algorithms in real-time communication and geostationary satellite systems. as advisor to the cro of mckinsey & co. investment office, marshall was responsible for the design and implementation of the fund liquidity framework, stress testing framework, and a multitude of quantitative risk and investment tools, enabling evaluation of exposures for risk and attribution.
he holds a b.s. in electrical engineering and mathematics from purdue university and an m.s. in electrical engineering from george mason university.
dr. yuchen dong, mathworks
dr. yuchen dong, mathworks
physics-informed neural networks for option pricing
the financial industry is increasingly using machine learning and ai to enhance its methodologies. among these advancements, physics-informed neural networks (pinns) offer a way to incorporate domain-specific knowledge into neural network training, making them effective for solving partial differential equations (pdes) in finance.
this presentation demonstrates how pinns can be applied to the black-scholes model for option pricing. using matlab® and deep learning toolbox™, we will show how to set up neural networks interactively and customize loss functions to address financial problems with greater precision.
dr. yuchen dong
dr. yuchen dong is a senior application engineer at mathworks with a focus on the financial services industry. his areas of expertise include financial instruments, portfolio optimization, and risk management. before joining mathworks, dr. dong worked as a derivatives valuation analyst. he holds a ph.d. in mathematical sciences and a master's degree in financial mathematics.
end of event
end of event
end of event
sustainable finance with matlab
join thierry roncalli, head of quantitative research at amundi, as he explores the practical applications of matlab® in teaching sustainable finance. this presentation covers key topics like carbon taxes, portfolio decarbonization, and stress testing. thierry will also highlight exciting results from his recent research and provide a sneak peek into his forthcoming book, handbook of sustainable finance (march 2025).
thierry roncalli
amundi
thierry roncalli is head of quantitative research at amundi and an adjunct professor of economics at the université d’évry paris-saclay. prior to joining amundi in november 2016, he was head of research and development at lyxor asset management. thierry has been a member of the scientific advisory board of the amf, the french securities, and financial markets regulator since february 2017. he is the author of numerous academic articles in scientific journals and has published several books on risk and asset management. his new book, handbook of sustainable finance, will be published in march 2025.
rise toolbox: advancing regime-switching models for macroeconomic analysis
the rise toolbox is designed for solving, estimating, and analyzing nonlinear regime-switching dsge models. regime-switching models are essential for addressing the complexities of economic environments, such as the zero lower bound, financial crises, and high inflation periods. see how rise accommodates various macroeconomic models, including dsge vars, panel vars, and optimal policy frameworks. gain insights into solving these models using rise, the benefits of regime-switching for economic forecasting, and real-world applications in policy analysis and simulation.
junior maih
norges bank
junior maih is the lead author of the rise toolbox for regime-switching models and is a senior economist and special adviser at norges bank. alongside the rise toolbox, he contributes to developing toolboxes for solving and estimating structural macroeconomic models, notably dynare. he has previously worked at the international monetary fund in the research department's modeling division. junior received his ph.d. in economics from the university of oslo and is an associate professor at the norwegian business school (bi).
harnessing advanced financial modeling technologies with matlab
financial modeling stands as the bedrock of decision making in the finance industry, yet it is undergoing transformative changes in today's fast-paced and complex environment. rapid advancements in mathematical methods, heightened regulatory demands, the transformative potential of cloud computing, the explosion of accessible data sources, and the rise of (generative) ai are revolutionizing the field. these developments make the work of financial professionals both exhilarating and challenging.
modern modeling tools must harness these technologies in a user-friendly, transparent, and customizable manner. in this presentation, see how practitioners leverage matlab® to tap into a comprehensive ecosystem of data and tools for swift and robust modeling. explore compelling examples, including econometric modeling tools, climate scenarios, geophysical data sources, and collaboration in multilanguage setups. discover how matlab empowers financial professionals to stay ahead in an ever-evolving landscape.
alexander diethert
mathworks
dr. alexander diethert is a senior financial application engineer at mathworks in munich, germany. he leads a team of technical experts who empower analysts, economists, and it people around the world to optimize their use of mathworks technology. prior to joining mathworks, alexander worked as a consultant in the financial services area. he holds a diploma in mathematics and a ph.d. in physics.
innovations in the delivery of ai-powered financial services
explore the latest advancements for accelerating the delivery of financial services, and discover how cloud-connected and cloud-hosted software, including private and public cloud options, can bring ai-powered financial services to market. in the midst of the hype, ai, deep learning, and large language models are rarely acceptable for production use, unless their development and implementation has been carried out in a transparent, explainable and reproducible manner.
see how a modelops platform with data governance, continuous integration, and testing is built from reference architectures to leverage the growing plethora of technologies in a coherent, reproducible, and transparent workflow. this will include:
- matlab® in jupyterhub and data science platforms.
- verification, documentation, and management of deep learning networks.
- deployment and monitoring of containerized models.
paul peeling
mathworks
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.
navigating climate finance: software solutions for climate risk management
in the evolving landscape of climate finance, the need to incorporate climate impacts into risk management strategies presents significant challenges and opportunities. this presentation leverages empirical data and real-world experiences to explore the intricacies of managing climate-related financial risks through software applications. explore workflows around geolocating physical assets, hazard identification, financial impact estimation, and the integration of climate risk into operational frameworks. by examining case studies, this talk demonstrates the pivotal role of software solutions in transparent, scalable, and reproduceable climate risk management.
elre oldewage
mathworks
elre oldewage is an application engineer at mathworks specializing in machine learning, finance, and climate risk. she joined mathworks in 2023 after completing her ph.d., which focused on robustness in machine learning and how second-order optimization can be used to improve model training. elre holds an m.sc. in computer science from the university of pretoria and a ph.d. in engineering from the university of cambridge.
physics-informed neural networks for option pricing
the financial industry is increasingly using machine learning and ai to enhance its methodologies. among these advancements, physics-informed neural networks (pinns) offer a way to incorporate domain-specific knowledge into neural network training, making them effective for solving partial differential equations (pdes) in finance.
this presentation demonstrates how pinns can be applied to the black-scholes model for option pricing. using matlab® and deep learning toolbox™, we will show how to set up neural networks interactively and customize loss functions to address financial problems with greater precision.
dr. yuchen dong
mathworks
dr. yuchen dong is a senior application engineer at mathworks with a focus on the financial services industry. his areas of expertise include financial instruments, portfolio optimization, and risk management. before joining mathworks, dr. dong worked as a derivatives valuation analyst. he holds a ph.d. in mathematical sciences and a master's degree in financial mathematics.
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