Seminars for 2019-2020


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Unless specified, seminars are held on Thursdays, from 11am to noon, Stevanovich Center Library, 5727 S. University Ave.


Past 2019-20 Seminars

Thursday, October 10 and October 24, 2019- 11am-noon – Stevanovich Center Library

Shige Peng

Professor of Mathematics, Shandong University, China, and academician of the Chinese Academy of Sciences, visiting Faculty at the Stevanovich Center for Financial Mathematics.

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Problem of Utility Maximization in a Framework of G-Brownian Motion
G-Brownian motion is defined as a continuous stochastic process with stable and independent increments under a framework of sublinear expectation, called G-expectation. It is a natural and robust generalization of the classical framework of Brownian motion under probability distribution uncertainty, known as ambiguity or Knightian uncertainty. A crucially important advantage of the stochastic calculus in this new framework is that many risky quantities dangerously neglected by probability measures P can be safely ‘detected’ and robustly quantified. In this talk the problem of utility maximization problem under a framework of G-Brownian motion is investigated.  We also discuss the corresponding dynamic programming principle and numerical realizations.  

Thursday, October 17, 2019 – 11am-noon – Stevanovich Center Library

Anne Lundgaard Hansen

Ph.D. Candidate, Department of Economics at the University of Copenhagen and Danmarks Nationalbank

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Macroeconomic Determinants of Yield Curve Volatility

I show that the relationship between the U.S. Treasury yield curve and the macroeconomy changes over time. The share of yield curve volatility explained by macro variables ranges between 0-50 pct from 1971 to 2019. Macro shares of variation in 10-year short-rate expectations and term premia range between 0-76 and 0-69 pct. I establish these results in a novel term structure model with time-varying second moments and variance risk premia. The model uncovers new knowledge in the intersection of macro and finance. First, macro shares of short-term yield variation are large during the 1970s-80s, decrease during the Great Moderation, and increase after the Great Recession. Second, investors increasingly anchor short-rate expectations to macroeconomic fundamentals. Third, deflation fears increase term premia during the Great Recession. Fourth, large variance risk premia compensate investors for macroeconomic uncertainty. Finally, I show that macro variables do not explain the yield curve inversion in the Spring 2019.

Thursday, November 7, 2019 – Stevanovich Center Library – Please Note Special Time: 10:30 AM-11:30 AM

Tarun Ramadorai

Professor of Financial Economics at Imperial College London

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Reference Points in the Housing Market

Using comprehensive and granular Danish data, we revisit the determinants of decisions to list, and listing premia in the housing market. Nominal losses and down-payment constraints both affect the gap between listing prices and hedonic valuations; we discover that these determinants have interactive effects on household behavior. To explain these facts, we adopt a structural approach — sellers optimize expected utility from property sale, subject to down-payment constraints, and taking as given the “concave demand” of buyers (the probability of sale more steeply declines with positive listing premia than it rises with negative premia). A model with reference-dependent — but not necessarily loss-averse — preferences combined with penalties associated with down-payment constraints fits best, but cannot fully explain the new facts that we uncover.

The paper can be found here.

Thursday, November 14, 2019 – 11am-noon – Stevanovich Center Library

Hongcen Wei

Ph.D. Candidate, Kenneth C. Griffin Department of Economics at the University of Chicago and 2019 Stevanovich Fellow

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The Effects of Financial Deregulation on Wage Inequality

This paper investigates how financial deregulation affects wage inequality and how the shift within the finance industry transmits into the labor market, both empirically and theoretically. I utilize staggered implementations of the interstate branching deregulation across US states from 1994 to 2005 as quasi-natural experiments. I find that this financial deregulation increases wage inequality. The increase effect is persistent over time and heterogeneous across dimensions of deregulation. I empirically show the transmission mechanism of the “finance facilitator”: financial deregulation facilitates (preexisting but financially constrained) skill-biased technical change in the labor market. First, within the finance industry, financial deregulation partially substitutes local community banks with national banks, which provide cheaper and more credit. Then, this positive credit supply shock loosens firms’ financial constraints, more for the firms that are young, small, or more profitable. Finally, these previously financially constrained firms scale up by hiring more skilled than unskilled workers. This further shifts skill composition and wage distribution of the labor market, resulting in higher wage inequality. To illustrate the mechanism theoretically, I endogenize financial constraints and capital-skill complementarity within a span-of-control model. I show that financial deregulation enables previously financially constrained firms to shift towards their optimal production scales and thus towards higher relative demand for skilled workers. Such a shift increases both relative wages and relative employment of skilled workers and consequently drives up inequality.

Thursday, November 21, 2019 – Stevanovich Center Library – NEW TIME: 11:30 am-12:30 pm

Stefano Pegoraro

Ph.D. Candidate, Kenneth C. Griffin Department of Economics at the University of Chicago and 2019 Stevanovich Fellow

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Flows and Performance with Optimal Money Management Contracts

Previous literature documents that mutual funds’ flows increase more than linearly with realized performance. I show this convex flow-performance relationship is consistent with a dynamic contracting model in which investors learn about the fund manager’s skill. Flows become more sensitivity to performance when the manager faces stronger incentives from the optimal compensation contract. With learning, the manager’s incentives become stronger after good performance, so that a more skilled manager exerts more effort, although the relation between past performance and incentives becomes weaker over the manager’s tenure. My model therefore predicts that flows become more sensitive to current performance after a history of good past performance, but less so for managers with longer tenure. I test these statistical predictions in mutual fund data and find empirical support for the theory. By incorporating an explicit incentive contract for the manager, my model explains common compensation practices in the money management industry, such as convex pay-for-performance schemes and deferred compensation.

Thursday, December 5, 2019 – 11am-noon – Stevanovich Center Library

Richard Yongrui Chen

Ph.D. Candidate, Department of Statistics, University of Chicago and 2019 Stevanovich Fellow

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Particles and Waves: Inference of Volatility Matrix Functionals Using Noisy and Asynchronous High-frequency Data

This talk will introduce new nonparametric frameworks with theoretical guarantees for smooth functionals of volatility matrix in an asymptotic regime relevant to the rapidly growing TAQ database, from two distinct viewpoints. The first leads to a method that enables simultaneous handling of noise and jumps by time-domain smoothing and truncation. Second-order expansion reveals explicit nonlinearity biases and a pathway to bias correction. The second utilizes harmonic analysis and is spectral in nature. This spectral framework is advantageous in that it harnesses the power of Fourier transform to handle missing data and asynchronous observations without any artificial time alignment nor data imputation. These new methodologies extend previous applications of volatility matrix functionals, including principal component analysis, generalized method of moments, continuous-time linear regression et cetera, to large-scale high-frequency datasets of which microstructure noise and asynchronicity are prevailing features.