Seminars for 2018-2019

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

What prompts or prevents you from attending our seminars?

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.

Yoosoon Chang

Professor of Economics, Indiana University, Bloomington

State Space Models with Endogenous Regime Switching

This article studies the estimation of state space models whose parameters are switching endogenously between two regimes, depending on whether an autoregressive latent factor crosses some threshold level. Endogeneity stems from the sustained impacts of transition innovations on the latent factor, absent from which our model reduces to one with exogenous Markov switching. Due to the flexible form of state space representation, this class of models is vastly broad, including classical regression models and the popular dynamic stochastic general equilibrium (DSGE) models as special cases. We develop a computationally efficient filtering algorithm to estimate the nonlinear model. Calculations are greatly simplified by appropriate augmentation of the transition equation and exploiting the conditionally linear and Gaussian structure. The algorithm is shown to be accurate in approximating both the likelihood function and filtered state variables. We also apply the filter to estimate a small-scale DSGE model with Bayesian methods, and find that the Bayes factor strongly favors the endogenous switching version of the model over the exogenous case. Overall, our approach provides a greater scope for understanding the complex interaction between regime switching and measured economic behavior.

Paper coauthored with Junior Maih (Norges Bank and BI Norwegian Business School) and Fei Tan (Saint Louis University and Center for Economic Behavior and Decision-Making, Zhejiang University of Finance and Economics).

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

I study the dynamic implications of uncertain climate policy on macroeconomic outcomes and asset prices. Focusing particularly on the oil sector, I find that accounting for uncertain climate policy in an otherwise standard climate-economic model with oil extraction generates a run on oil, meaning oil firms accelerate extraction as climate change increases and oil reserves decrease due to the risk of future climate policy actions stranding oil reserves. Furthermore, the risk of uncertain climate policy and the run on oil it causes leads to a downward shift and dynamic decrease in the oil spot price and value of oil firms compared to the setting without uncertain policy. Ignoring the impact of uncertain climate policy would therefore lead to an overvaluation of the oil sector or “carbon bubble.” Empirical evidence suggests observable market outcomes are consistent with the model predictions about the effects of uncertain climate policy.

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

We develop a principal component analysis (PCA) for high frequency data. As in Northern fairly tales, there are trolls waiting for the explorer. The first three trolls are market microstructure noise, asynchronous sampling times, and edge effects in estimators. To get around these, a robust estimator of spot covariance matrix is developed based on the Smoothed TSRV (Mykland et al. (2017)). The fourth troll is how to pass from estimated time-varying covariance matrix to PCA. Under finite dimensionality, we develop this methodology through the estimation of realized spectral functions. Rates of convergence and central limit theory, as well as an estimator of standard error, are established. The fifth troll is high dimension on top of high frequency, where we also develop PCA. With the help of a new identity concerning the spot principal orthogonal complement, the high-dimensional rates of convergence have been studied by freeing several strong assumptions in classical PCA. As an application, we show that our first principal component (PC) potentially outperforms the S&P 100 market index, while three of the next four PCs are cointegrated with two of the Fama-French non-market factors.

Past 2018-19 Seminars


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

Arnoldo Frigessi

Professor, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Norway
Towards personalized computer simulation of breast cancer treatment: a multi-scale pharmacokinetic and pharmacodynamic model informed by multi-type patient data

Mathematical modeling and simulation have emerged as a potentially powerful, time- and cost effective approach to personalized cancer treatment. In order to predict the effect of a therapeutic regimen for an individual patient, it is necessary to initialize and to parametrize the model so to mirror exactly this patient’s tumor. I will present a comprehensive approach to model and simulate a breast tumor treated by two different chemotherapies in combination and not.  In the multi-scale model we represent individual tumor and normal cells, with their cell cycle and others intracellular processes (depending on key molecular characteristics), the formation of blood vessels and their disruption, extracellular processes, as the diffusion of oxygen, drugs and important molecules (including VEGF which modulates vascular dynamics) . The model is informed by data estimated from routinely acquired measurements of the patient’s tumor, including histopathology, imaging, and molecular profiling. We implemented a computer system which simulates a cross-section of the tumor under a 12-week therapy regimen. We show how the model is able to reproduce patients from a clinical trial, both responders and not. We show by scenario simulation, that other drug regimens might have led to a different outcome. Approximate Bayesian Computation (ABC) is used to estimate some of the parameters.


Thursday, October 18, 2018 – 11am-noon – Stevanovich Center Library

Eric Renault

Professor of Commerce, Organizations and Entrepreneurship, Professor of Economics, Brown University

Wald Tests When Restrictions Are Locally Singular
This paper provides an exhaustive characterization of the asymptotic null distribution of Wald-type statistics for testing restrictions given by polynomial functions – which may involve asymptotic singularities – when the limiting distribution of the parameter estimators is absolutely continuous (e.g., Gaussian). In addition to the well-known finite-sample non-invariance, there  is also an asymptotic non-invariance (non-pivotality): with standard critical values, the test may either under-reject or over-reject, and may even diverge under the null hypothesis. The asymptotic distributions of the test statistic can vary under the null hypothesis and depend on the true unknown parameter value. All these situations are possible in testing restrictions which arise in the statistical and econometric literature, e.g. for examining the specification of ARMA models, causality at different horizons, indirect effects, zero determinant hypotheses on matrices of coefficients, and many other situations when singularity in the restrictions cannot be excluded. We provide the limit distribution and general bounds for the single restriction case. For multiple restrictions, we give a necessary and sufficient condition for the existence of a limiting distribution and the form of the limit distribution whenever it exists.
Authors: Jean-Marie Dufour, McGill University; Eric Renault, Brown University; Victoria Zinde-Walsh, McGill University

Wednesday, October 24, 2018 – 2:30pm-3:30pm – Stevanovich Center Library

Eric Ghysels

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

Robo-advising is a fast-growing application of financial technology (FinTech) solutions to asset and wealth management.  We assess benefits from robo-advising using a unique data set covering brokerage accounts for a large cross-section of investors over a long time span, namely more than 10 years and more than 20,000 individuals using their investor-specific characteristics. We study robo-investors which shadow the individuals in our data set. They track each individual’s portfolio over the past two years and make allocation decisions either based on a Markowitz mean-variance scheme or a 1/N rule.  We call these robo-investors Artificial Intelligence Alter Egos, and study their portfolio return behavior vis-\`a-vis each of the individuals they shadow. We study the investor characteristics which determine who stands to gain from robo-advising. In addition, we also introduce the notion of robust robots, which take into account the potential for fragile beliefs.

Thursday, November 8, 2018 – 11am to noon – Stevanovich Center Library

Paolo Zaffaroni

Professor in Financial Econometrics, Business School, Imperial College London, UK

Beyond the Bound: Pricing Assets with Misspecified Stochastic Discount Factors

We show how, given a misspecified stochastic discount factor (SDF), one can con- struct an admissible SDF, namely an SDF that prices assets correctly. We characterize misspecification using the extended Arbitrage Pricing Theory (APT) developed in Raponi, Uppal and Zaffaroni (2017), which allows not just for small but also large pricing errors that are pervasive (related to factors). We show how the pricing errors implied by the extended APT can be exploited to develop a theory that provides the correction required to a given SDF in order to obtain an admissible SDF that is robust to model misspecification. We show that the corrected SDF is on the mean-variance efficient frontier, and thus satisfies the Hansen and Jagannathan (1991) bound exactly. For the case where the number of assets, N, is asymptotically large, we obtain results that are even stronger, in contrast to the existing literature that requires N to be small. For large N, we show that the component of the SDF, corresponding to the missing pervasive factors, recovers exactly the contribution of such missing factors to the admissible SDF without requiring one to identify which or how many factors are missing. Estimation of our admissible SDF does not suffer from the curse of dimensionality that typically arises when N is large because of the structure imposed by the extended APT.

Joint paper with  Raman Uppal and Irina Zviadadze

Skip to toolbar