Seminars for 2018-2019
We won’t hold Seminars between May and October 2019. Please consider attending one of our conferences!
Monday, October 8, 2018 – Eckhart 133, 4:30-5:30pm. This seminar is organized jointly by the Stevanovich Center and the UC Department of Statistics
Towards personalized computer simulation of breast cancer treatment: a multi-scale pharmacokinetic and pharmacodynamic model informed by multi-type patient data
Thursday, October 18, 2018 – 11am-noon – Stevanovich Center Library
Professor of Commerce, Organizations and Entrepreneurship, Professor of Economics, Brown University
Wald Tests When Restrictions Are Locally Singular
Wednesday, October 24, 2018 – 2:30pm-3:30pm – Stevanovich Center Library
Edward Bernstein Distinguished Professor of Economics and Adjunct Professor of Finance, Kenan-Flagler Business School, University of North Carolina at Chapel Hill
Artificial Intelligence Alter Egos
Thursday, November 8, 2018 – 11am to noon – Stevanovich Center Library
Professor in Financial Econometrics, Business School, Imperial College London, UK
Beyond the Bound: Pricing Assets with Misspecified Stochastic Discount Factors
Thursday, November 15, 2018 – 1:20pm to 2:20pm – Booth School of Business (Harper Center, room HC3B) – This seminar is jointly organized by the Booth School of Business and the Stevanovich Center.
Professor of Economics, Indiana University, Bloomington
Origins of Monetary Policy Shifts: A New Approach to Regime Switching in DSGE Models
We examine monetary policy shifts by taking a new approach to regime switching in a small scale monetary DSGE model with threshold-type switching in the monetary policy rule. The policy response to inflation is allowed to switch endogenously between two regimes, hawkish and dovish, depending on whether a latent regime factor crosses a threshold level. Endogeneity stems from the historical impacts of structural shocks driving the economy on the regime factor. We quantify the endogenous feedback from each structural shock to the regime factor to understand the sources of the observed policy shifts. This new channel sheds new light on the interaction between policy changes and measured economic behavior. We develop a computationally efficient filtering algorithm for state-space models with time-varying transition probabilities that handles classical regression models as a special case. We apply this filter to estimate our DSGE model using the U.S. data and find strong evidence of endogeneity in the monetary policy shifts.
Thursday, November 29, 2018 – 10am to 11am – Stevanovich Center Library
Michael Barnett, 2018 Stevanovich Fellow
PhD student in the joint program in Financial Economics, University of Chicago
A Run on Oil: Climate Policy, Stranded Assets, and Asset Prices
Thursday, November 29, 2018 – 11 am to noon – Stevanovich Center Library
Dachuan Chen, 2018 Stevanovich Fellow
PhD student in Business Administration, University of Illinois at Chicago.
The Five Trolls under the Bridge: Principal Component Analysis with Asynchronous and Noisy High Frequency Data
Thursday, January 17, 2019 – 11am-noon – Stevanovich Center Library
Senior Financial Economist and Research Advisor, Federal Reserve Bank of Chicago
Core and Crust: Consumer Prices and the Term Structure of Interest Rates
Thursday, February 7, 2019 – 11am-noon – Stevanovich Center Library
Assistant Professor, Financial and Business Analytics, Columbia University Data Science Institute
Semi-parametric factor models for non-stationary time series
Our previous approach to fitting dynamic non-stationary factor models to multivariate time series is based on the principal components of the time-varying spectral-density matrix. This approach allows the spectral matrix to be smoothly time-varying, which imposes very little structure on the moments of the underlying process. However, the estimation delivers time-varying filters that are high-dimensional and two-sided. Moreover, the estimation of the spectral matrix strongly depends on the chosen bandwidths for smoothing over frequency and time. As an alternative, we introduce a novel semi-parametric approach in which only part of the model is allowed to be time-varying. More precisely, the small-dimensional latent factors admit a dynamic representation with time-varying parameters while the high-dimensional loadings are time-invariant.
In particular, we consider two specifications for the latent factors. In the first model, the latent factors are locally stationary AR processes. The time-varying parameters are approximated by local polynomials and estimated by maximizing the likelihood locally. In the second model, the volatility of the common latent factors is decomposed into the product of two distinct components. The first component reflects short-run volatility dynamics that we model as factor GARCH processes. The second component captures long-run risks, modeled as an ‘evolutionary’ (or slowly evolving) function of time.
We provide asymptotic theory, simulation results and applications to real data.
Thursday, February 21, 2019 – 11am-noon – Stevanovich Center Library
Zelter Family Professor of Economics and Professor of Finance, Duke University
Risk Price Variation: The Missing Half of the Cross-Section of Expected Returns
The Law of One Price is a bedrock of asset pricing theory and empirics. Yet real-world frictions can violate the Law by generating unequal compensation across assets for the same risk exposures. We develop new methods for cross-sectional asset pricing with unobserved heterogeneity in compensation for risk. We extend k-means clustering to group assets by risk prices and introduce a formal test for whether differences in risk premia across market segments are too large to occur by chance. Using portfolios of US stocks, international stocks, and assets from multiple classes, we find significant evidence of cross-sectional variation in risk prices for all 135 combinations of test assets, factor models, and time periods. Variation in risk prices is as important as variation in risk exposures for explaining the cross-section of expected returns.
Thursday, March 7, 2019 – 11am-noon – Stevanovich Center Library
Harry G. Guthmann Professor of Finance and Co-Director of the Financial Institutions and Markets Research Center, Kellogg School of Management at Northwestern University
We propose a new methodology to estimate arbitrage portfolios by utilizing information contained in firm characteristics for both abnormal returns and factor loadings. The methodology gives maximal weight to risk-based interpretations of characteristic predictive power before any attribution to abnormal returns. We apply the methodology in simulated factor economies and on a large panel of U.S. stock returns from 1965–2014. The methodology works well in simulation and in out-of-sample portfolios of U.S. stocks. Empirically, we find the arbitrage portfolio has (statistically and economically) significant alphas relative to several popular asset pricing models and annualized Sharpe ratios ranging from 0.67 to 1.12. Data-mining-driven alphas imply that performance of the strategy should decline after the discovery of pricing anomalies. However, we find that the abnormal returns on the arbitrage portfolio do not decrease significantly over time.
Thursday, April 4, 2019 – 11am-noon – Stevanovich Center Library
Associate Professor in Economics, University of Oxford, Aarhus University, CREATES and St. Hilda’s College
Power in High Dimensional Testing Problems
Fan et al. (2015) recently introduced a remarkable method for increasing asymptotic power of tests in high-dimensional testing problems. If applicable to a given test, their power enhancement principle leads to an improved test that has the same asymptotic size, uniformly non-inferior asymptotic power, and is consistent against a strictly broader range of alternatives than the initially given test. We study under which conditions this method can be applied and show the following: In asymptotic regimes where the dimensionality of the parameter space is fixed as sample size increases, there often exist tests that can not be further improved with the power enhancement principle. However, when the dimensionality of the parameter space increases sufficiently slowly with sample size and a marginal local asymptotic normality (LAN) condition is satisfied, every test with asymptotic size smaller than one can be improved with the power enhancement principle. While the marginal LAN condition alone does not allow one to extend the latter statement to all rates at which the dimensionality increases with sample size, we give sufficient conditions under which this is the case.
Thursday, April 18, 2019 – 11am-noon – Stevanovich Center Library
Professor of Economics, Center for Monetary and Financial Studies – Research Fellow, Center for Economic Policy Research – Senior Research Associate, London School of Economics, Financial Markets Group
Empirical Evaluation of Over-specified Asset Pricing Models
Asset pricing models with potentially too many risk factors are increasingly common in empirical work. Unfortunately, they can yield misleading statistical inferences. Unlike other studies focusing on the properties of standard estimators and tests, we estimate the sets of SDFs and risk prices compatible with the asset pricing restrictions of a given model. We also propose tests to detect problematic situations with economically meaningless SDFs uncorrelated to the test assets. We confirm the empirical relevance of our proposed estimators and tests with Yogo’s (2006) linearized version of the consumption CAPM, and provide Monte Carlo evidence on their reliability in finite samples.
Jointly with Elena Manresa and Francisco Peñaranda