Hello! I'm Zhao

 

I am a Ph.D. candidate in economics at Duke University.

My research interests are finance, macroeconomics and econometrics.

I will be available for interviews in 2020 ASSA and AFA meetings in San Diego.

You can download my latest CV here.

 
 

EDUCATION

2014-2020

PH.D. IN ECONOMICS

DUKE UNIVERSITY, NC

Committee: Craig Burnside (Chair), Mariano Massimiliano Croce, Lukas Schmid, Federico Bugni

2011-2013

M.PHIL IN STATISTICS

UNIVERSITY OF HONG KONG, HK

2007-2011

B.S. IN MATHEMATICS & STATISTICS

SHANDONG UNIVERSITY, CHINA

RESEARCH

PUBLICATIONS

Recursive allocations and wealth distribution with multiple goods: Existence, survivorship, and dynamics (with Riccardo Colacito and Mariano M. Croce), Quantitative Economics 10.1 (2019): 311-351.​ [Download Here]

On mixture double autoregressive time series models (with Guodong Li, Qianqian Zhu and Wai Keung Li), Journal of Business & Economic Statistics 35.2 (2017): 306-317. [Download Here]

Local Linear-additive Estimation for Multiple Nonparametric Regressions (with Lu Lin and Yunquan Song), Journal of Multivariate Analysis 123 (2014): 252-269. [Download Here]

JOB MARKET PAPER

R&D, Risk Premia and Credit Spreads (Job Market Paper)

Abstract:  Empirical evidence suggests that R&D-intensive firms experience higher expected equity returns, but lower leverage, default rates, and credit spreads, than R&D-nonintensive firms. To provide a unified explanation for these cross-sectional differences, I propose a two-sector production-based dynamic stochastic general equilibrium model in which R&D firms are highly exposed to innovation risks, raising bankruptcy costs. Despite the tax benefits of debt, R&D firms tend to choose low leverage, lowering both default rates and credit spreads; nevertheless, the inherent riskiness of R&D activities dominates, generating high equity returns. The model generates sizable heterogeneity in the quantities of interest between R&D and non-R&D firms, as in the data, and fits the aggregate macroeconomic and asset pricing moments reasonably well.

On Buffered Double Autoregressive Time Series Models (Under Revision) [Download Here]

Abstract: A buffered double autoregressive (BDAR) time series model is proposed in this paper to depict the buffering phenomenon of conditional mean and conditional variance in time series. To build this model, a novel flexible regime switching mechanism is introduced to modify the classical threshold time series model by capturing the stickiness of signal. Besides, considering the inadequacy of traditional models under the lack of information, a signal retrospection is run in this model to provide a more accurate judgment. Moreover, formal proofs suggest strict stationarity and geometric ergodicity of BDAR model under several sufficient conditions. A Gaussian quasi-maximum likelihood estimation (QMLE) is employed and the asymptotic distributions of its estimators are derived. It has been demonstrated that the estimated thresholds of the BDAR model are n-consistent, each of which converges weakly to a functional of a two-sided compound Poisson process. The remaining parameters are square-root-n-consistent and asymptotically normal. Furthermore, a model selection criteria and its asymptotic property have been established. Simulation studies are constructed to evaluate the finite sample performance of QMLE and model selection criteria. Finally, an empirical analysis of Hang Seng Index (HSI) using BDAR model reveals the asymmetry of investors' preference over losses and gains as well as the asymmetry of volatility structure.

Good Uncertainty, Bad Uncertainty and the Aggregate Effects: a Regime-Switching DSGE Approach

Recovering Time Varying Risk Premia in High Frequency Econometrics

A Unified Model for Puzzles in International Finance

WORKING PAPER

WORK IN PROGRESS

 
 

TEACHING

2019

Head Teaching Assistant

Time Series Econometrics (Master's Course)

Department of Economics, Duke University

2017

Head Teaching Assistant

Methods in Macro-Finance (Finance Ph.D. Core Course)

Kenan-Flagler School of Business, University of North Carolina Chapel Hill

2015-2016

Head Teaching Assistant

Econometrics I & II (Econ Ph.D. Core courses)

Department of Economics, Duke University

2013

Head Teaching Assistant

Statistics in Economics and Finance (Master's Course)

Department of Statistics and Actuarial Science, University of Hong Kong

2012

Teaching Assistant

Business Statistics (Undergraduate Core Course)

Department of Statistics and Actuarial Science, University of Hong Kong

2012

Teaching Assistant

Statistics and Probability II (Undergraduate Core Course)

Department of Statistics and Actuarial Science, University of Hong Kong

2011

College Teaching Certificate

University of Hong Kong