Uploaded June 2025 | Updated September 2026, 1 week ago
How can we better assess risk in evolving, uncertain environments? In this talk, Luhao Zhang presents a *new class of multi-period convex risk measures* that address a major limitation in traditional approaches like Conditional Value-at-Risk: the lack of *dynamic decomposition.*
This innovative framework evaluates the *worst-case expectation* across all possible stochastic processes, penalizing deviations from a nominal process using both *likelihood ratio* and *outcome-based* metrics. Crucially, it can be reformulated as a *dynamic program,* enabling more efficient, recursive risk assessment over time.
Key insights:
* Why dynamic decomposition matters in risk modeling
* How the proposed method improves computational efficiency and interpretability
* Practical implications for finance, operations, and decision-making under uncertainty
Perfect for researchers, practitioners, and students in *quantitative finance, operations research, and stochastic optimization.*
🔔 Subscribe for more insights on the future of data modeling. Keep up-to-date on SIAM/BFS Webinars at wiki.siam.org/siag-fm/index.php/Current_events#Forthcoming_Talks
Watch *previous SIAM FME webinars* at youtube.com/playlist?list=PLf_ipOSbWC85WhSODpb_AHqtraqSkTVZR
Learn more about *SIAM Activity Group on Financial Mathematics and Engineering* at siam.org/get-involved/connect-with-a-community/activity-groups/financial-mathematics-and-engineering
#LuhaoZhang #RiskManagement #ConvexRiskMeasures #DynamicProgramming #Finance #StochasticProcesses #OperationsResearch
How can we better assess risk in evolving, uncertain environments? In this talk, Luhao Zhang presents a *new class of multi-period convex risk measures* that address a major limitation in traditional approaches like Conditional Value-at-Risk: the lack of *dynamic decomposition.*
This innovative framework evaluates the *worst-case expectation* across all possible stochastic processes, penalizing deviations from a nominal process using both *likelihood ratio* and *outcome-based* metrics. Crucially, it can be reformulated as a *dynamic program,* enabling more efficient, recursive risk assessment over time.
Key insights:
* Why dynamic decomposition matters in risk modeling
* How the proposed method improves computational efficiency and interpretability
* Practical implications for finance, operations, and decision-making under uncertainty
Perfect for researchers, practitioners, and students in *quantitative finance, operations research, and stochastic optimization.*
🔔 Subscribe for more insights on the future of data modeling. Keep up-to-date on SIAM/BFS Webinars at wiki.siam.org/siag-fm/index.php/Current_events#Forthcoming_Talks
Watch *previous SIAM FME webinars* at youtube.com/playlist?list=PLf_ipOSbWC85WhSODpb_AHqtraqSkTVZR
Learn more about *SIAM Activity Group on Financial Mathematics and Engineering* at siam.org/get-involved/connect-with-a-community/activity-groups/financial-mathematics-and-engineering
#LuhaoZhang #RiskManagement #ConvexRiskMeasures #DynamicProgramming #Finance #StochasticProcesses #OperationsResearch
