Fall 2023

Fall 2023

SEM217: Haim Bar, University of Connecticut: On Graphical Models and Convex Geometry

Tuesday, November 28th @ 11:00-12:30 PM

We introduce a mixture-model of beta distributions to identify significant correlations among P predictors when P is large. The method relies on theorems in convex geometry, which we use to show how to control the error rate of edge detection in graphical models. Our ‘betaMix’ method does not require any assumptions about the network structure, nor does it assume that the network is sparse. The results hold for a wide class of data generating distributions that include light-tailed and heavy-tailed spherically symmetric distributions.

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SEM217: Lynne Burks, One Concern: Resilience Analytics to Measure Physical Risk at Scale

Tuesday, November 7th @ 11:00-12:30 PM, 648 Evans Hall

One Concern has built a Digital Twin of the world around us, revealing hidden risks across the built and natural environments posed by natural disasters, extreme weather, and climate change. We have leveraged a mix of physics-based and machine learning models to predict the impacts of disasters not just on buildings, but on the communities and infrastructure on which they depend. In this presentation, we will discuss the underlying models of One Concern’s products that are used to predict business downtime across multiple...

SEM217: No seminar this week

Tuesday, November 14th

SEM217: Happy Thanksgiving: No seminar this week

Tuesday, November 21st

SEM217: Samim Ghamami, U.S. Securities and Exchange Commission, DERA: Skin in the Game: Risk Analysis of Central Counterparties

Tuesday, October 31st @ 11:00-12:30 PM

This paper introduces an incentive compatibility framework to analyze agency problems linked to central counterparty (CCP) risk management. Our framework, which is based on a modern approach to extreme value theory, is used to design CCP skin-in-the-game (SITG). We show that under inadequate SITG levels, members are more exposed to default losses than CCPs. The resulting risk management incentive distortions could be mitigated by using the proposed SITG formulations. Our analysis addresses investor-owned and member-owned CCPs, we also analyze...

SEM217: Baeho Kim, Korea University Business School: Conditional Tail Sampling for General Marked Point Processes

Tuesday, October 24th @ 11:00-12:30 PM, 648 Evans Hall [ZOOM]

This study develops a simple but innovative simulation technique that can be employed in simulating a broad range of marked point processes, conditional on a tail event of interest. Our proposed conditional tail sampling algorithm guarantees that every simulated path hits the tail event with probability one, leading to an efficient estimation of tail probabilities and expected random quantities under the condition of...

SEM217: Alec Kercheval, Florida State University: Portfolio Selection via Strategy-Specific Eigenvector Shrinkage

Tuesday, October 17th @ 11:00-12:30 PM, 648 Evans Hall [ZOOM]

Portfolio managers need to estimate risk for many assets simultaneously with a limited number of useful observations. The standard approach is to do this using factor models, which reduce the number of variables that need to be estimated in the resulting structured covariance matrix. Even in a one-factor setting, there remains the open problem of finding a good estimate for the leading eigenvector – usually called...

SEM217: Robert Anderson, UC Berkeley & CDAR: General Equilibrium Theory for Climate Change

Tuesday, October 10th @ 11:00-12:30 PM, 648 Evans Hall

We propose two general equilibrium models, quota equilibrium and emission tax equilibrium. The government specifies quotas or taxes on emissions, then refrains from further action. Quota equilibrium exists; the allocation of emission property rights strongly impacts the distribution of welfare. If the only externality arises from total net emissions, quota equilibrium is constrained Pareto Optimal. Every quota equilibrium can be realized as an emission tax equilibrium and vice versa. However, for certain tax rates, emission tax...

SEM217: Emmanouil Platanakis, University of Bath: When Bayes-Stein Meets Machine Learning: A Generalized Approach for Portfolio Optimization

Tuesday, October 3rd @ 11:00-12:30 PM

The Bayes-Stein model is widely used to tackle parameter uncertainty in the classical Markowitz mean-variance portfolio optimization framework. In practice, however, it suffers from estimation errors and often fails to outperform the naive 1/N asset allocation rule. To address this, we develop a generalized counterpart that leverages machine learning (ML) techniques to estimate some core model parameters. Specifically, we propose a time-dependent weighted Elastic Net (TW-ENet) approach predicting expected asset returns, a hybrid double selective...