Packet - Economics - Johns Hopkins University

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PUBLICATIONS Journal Articles “The Evolutionary Game Illustration of the Global Impacts of European Integration,” Selected by 36th St. Gallen Wings of Excellence Award, Switzerland, May, 2006. 12 Essays, “New Media”, China International Publishing Group, 2004−2005 Book Chapters “China Sustainable Development Report”, with D. Yang, et. al., Science Press, 2007. “National Health Report”, with D. Yang, et. al., Science Press, 2008. “International Trade of China”, with J. Zhang, et. al., China Machine Press, 2007. DISTINCTIONS Fellowship, Johns Hopkins University, Department of Economics, 2007–2012. University Outstanding Graduate Scholarship, Ministry of Education, 2006. BaoGang Steel National Education Award, BaoGang Foundation, China, 2006. Excellent Academic Award, The Central University for Nationalities, 2002-2006. Excellent Student Leader Award, The Central University for Nationalities, 2004. National English Contest for College Students, Second Place, 2005 TEACHING EXPERIENCE Teaching Assistant to Professor Jonathan Wright, Investments, 2009, 2012. Teaching Assistant to Professor Olivier Jeanne, Global Financial Crisis, 2010. Teaching AssistanttoProfessorLouisMaccini,Elements of Macroeconomics,2011. Teaching AssistanttoProfessorBruceHamilton,Elements of Microeconomics,2008. REFERENCES Professor Jonathan H. Wright, Johns Hopkins University e-mail: wrightj@jhu.edu, Phone: (410) 516-5728 Professor Richard Spady, Johns Hopkins University e-mail: rspady@jhu.edu, Phone: (410) 516-7601 Professor Laurence M. Ball, Johns Hopkins University e-mail: lball@jhu.edu, Phone: (410) 516-7605 Dr. Xiaohui Hou, Senior Economist, World Bank e-mail: xhou@worldbank.org, Phone: (202) 473-7773
Essays on Real-Time Density Forecasting Yizhen Zhao 1 Forecasting the Stock Return Distribution Using Macro-Finance Variables (Job Market Paper) This paper evaluates combinations of density forecasts for S&P 500 returns. The contribution of the paper is to use a new set of density forecasts in the combination exercise, including density forecasts based on quantile regressions using macro-finance variables, along with various standard EGARCH and stochastic volatility specifications. The combined predictive densities are constructed to maximize the log predictive likelihood. In all, 30 density forecasting models areconsideredinanout-of-sampleforecastingexercise. Usingmacro-financevariablesisfoundto helpsubstantiallyinprediction; thebestforecastsareobtainedwhenall30modelsarecombined. The proposed density forecasts are also shown to be useful to an investor with a CRRA utility function who is trying to construct an optimal portfolio, regardless of whether short-sales are allowed or not. Depending on the investor’s utility function, using the proposed density forecasts yields a certainty equivalent return that is up to 0.35% per month higher than can be obtained with the EGARCH model with a fat-tailed specification. 2 Real-Time Density Forecasts of Output and Inflation via Quantile Regression (International Journal of Forecasting: Revise and Resubmit, Leading Journal in Forecasting: Impact Factor 1.485, Five-year impact factor 2.450) Thispapercomparesthedensityforecastingperformanceofquantileregressionsinreal-timewith that of two conventional density forecast methods, using U.S. inflation and output growth data from 1959 to 2010. This paper also examines whether augmenting the autoregression with the first few principal components (factors) from a large dataset is helpful for density forecasting, in which case, factor selection rules using high dimensional regression are compared. The density forecasts using quantile regressions are found to be significantly more accurate than the two conventional density forecasts. Including factors extracted from macroeconomic indicators in the quantile density forecast also generally improves density forecast accuracy. The Least Angle Regression is found to be the best method of selecting density forecasting factors. 3 High-Frequency Measurement of the Non-Gaussian Macroeconomic Dynamics (In Progress) The unavailability of macroeconomic variables at higher frequency often leads economic analysis to be conducted at low frequency. However, policy makers, making decisions in real time, always need accurate and timely estimates of the state of economic activity. This paper uses a non- Gaussian state-space model to extract high-frequency business conditions from a variety of stock and flow data observed at mixed frequencies. The model permits non-Gaussian disturbances for the observation equation and hence can better capture the non-Gaussian shocks to the economy. The likelihood function is computed by particle filter methods. The distribution of innovations is approximated by a mixture of normal distributions.
Page 1 and 2: Dear Colleague: THE JOHNS HOPKINS U
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