Spring 2023

SEM217: Hubeyb Gurdogan, CDAR: A propagation model to quantify business interruption losses in supply chain networks

Tuesday, January 24th @ 11:00-12:30 PM (ZOOM)

Today's supply chains are global, highly interconnected, and increasingly digital. These three attributes of supply chains compound the effects of disruptions in production. For a company comprising many factories, a disruption in production at one site can impact production at other locations as well as production at other companies linked through the supply chain. Quantifying the financial impact of business interruption, such as production loss at a factory caused by a natural catastrophe (NatCat) such as an earthquake or hurricane, is challenging. The difficulty in quantification is due to complex risk propagation dynamics and complications related to the allocation of business profit to specific sites of production. Complex risk propagation dynamics reflect product and supplier dependencies and the inter-connectivity of related risks.

The aim of this research is to estimate production losses at company locations to enable the quantification of exposed business interruption values (i.e. potential gross profit/earnings losses) taking into account interdependencies among the company and the supplying partners within its supply chain network. This approach can provide insurers and reinsurers with the required financial metrics to better address these risks. In this paper, after defining the adapted stochastic fully decomposed supply chain network (FDSN), we propose a new methodology to model the production rate potential at each site of production as a stochastic process via a recursive procedure. Finally, we consider the HAZUS Earthquake Model (HAZUS-EM) to estimate downtime and to quantify the impact of business interruption. Business interruption is propagated through the FDSN given an interruption in production in the supply chain network.

SEM217: Stephen Kealhofer and Simon Cenci, Blackstone: A causal approach to test empirical capital structure regularities

Tuesday, January 31st @ 11:00-12:30 PM (ZOOM)

Capital structure theories are often formulated as causal narratives to explain which factors drive financing choices. These narratives are usually examined by estimating cross-sectional relations between leverage and its determinants. However, the limitations of causal inference from observational data are often overlooked. To address this issue, we use structural causal modeling to identify how classic determinants of leverage are causally linked to capital structure and how this causal structure influences the effect-estimation process.  The results provide support for the causal role of variables that measure the potential for information asymmetry concerning firms’ market values. Overall, our work provide a crucial step to connect capital structure theories with their empirical tests beyond simple correlations.

SEM217: David Romer and Christy Romer, UC Berkeley: Inflation and monetary policy

Tuesday, February 7th @ 11:00-12:30 PM (ZOOM)

Drawing on work in progress, we examine the sources of the recent inflation, the Federal Reserve’s response, and the likely implications. We present evidence that the sharp rise in inflation reflects a mix of supply factors (particularly in the early stages) and an overheated economy (particularly later). The overheated economy, in turn, appears to have been caused in part by highly expansionary fiscal policy and the Federal Reserve’s slowness in tightening policy. We show that the sharp tightening of monetary policy since mid-2022 is similar to numerous anti-inflationary shifts in monetary policy since World War II. The evidence from those episodes suggests that little of the impact of the recent tightening has yet occurred, that the impact on real activity is likely to be substantial, and that the impact on inflation is highly uncertain.

SEM217: William Zame

Tuesday, February 14th @ 11:00-12:30 PM

SEM217: Zachary Feinstein, Stevens Institute of Technology: Endogenous Network Valuation Adjustment and the Systemic Term Structure in a Dynamic Interbank Model

Tuesday, February 21st @ 11:00-12:30 PM (ZOOM)

In this talk we introduce an interbank network with stochastic dynamics in order to study the yield curve of bank debt under an endogenous network valuation adjustment. This entails a forward-backward approach in which the future probability of default is required to determine the present value of debt. As a consequence, the systemic model presented herein provides the network valuation adjustment to the term structure for free without additional steps required. Time permitting, we present this problem in two parts: (i) a single maturity setting that closely matches the traditional interbank network literature and (ii) a multiple maturity setting to consider the full term structure. Numerical case studies are presented throughout to demonstrate the financial implications of this systemic risk model.

SEM217: Gerald Garvey, BlackRock

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

SEM217: Jordan Sopze, UC Berkeley

Tuesday, March 7th @ 11:00-12:30 PM

SEM217: Marielle De-Jong

Tuesday, March 14th @ 11:00-12:30 PM

SEM217: Lea Tschan

Tuesday, March 21st @ 11:00-12:30 PM 

Memorial Stadium outside view


Tuesday, April 4th @ 11:00-12:30 PM 

SEM217: Youhong Lee, UC Santa Barbara:

Tuesday, April 11th @ 11:00-12:30 PM 

SEM217: David Buckle,

Tuesday, April 18th @ 11:00-12:30 PM 

SEM217: Ben Davis,

Tuesday, April 25th @ 11:00-12:30 PM 

SEM217: Gary Kazantsev, Bloomberg:

Tuesday, May 2nd @ 11:00-12:30 PM