Huarui Jing
The University of the South
Huarui Jing
Assistant Professor of Finance
Department of Economics and Finance
The University of the South
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Teaching
The University of the South
FINC 301 Investment
FINC 305 Financial Modeling
FINC 410 Advanced Security Analysis
FINC 310 Real Estate Finance
FINC 302 Derivatives and Fixed Income Securities
University of Connecticut
ECON 3413 Financial Economics
ECON 2311 Econometrics I
PP 5331 Quantitative Methods for Public Policy
ECON 2301 Mathematical Economics
Research
Jing, Huarui, "Distributional Robustness Optimization in Asset Pricing Model", under review.
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Abstract: The robustness study has been discussed for a long time in macro models and can help us study the model failure problems. While the traditional robustness study approach chooses non-data driven based uncertainty set for input variables. Therefore the robustness level is insensitive to different model structures or different data inputs. Distributional Robustness Optimization (DRO), originally developed in the field of machine learning, helps study the uncertainty shifted from data generating distribution. The bridge between DRO and macro models contributes in a data driven set up robustness control. This paper implements DRO in information stochastic discount factor (I-SDF) based macro finance models (Ghosh, Julliard, and Taylor (2016 & 2019)).The DRO study of the I-SDF models improved the traditional robustness study from two aspects: the degree of robustness is studied in a data driven set up; the inference coverage of the macro finance model's robustness level has been discussed for the first time. The DRO worst case estimated parameters along with the objective function boundaries show important robustness control information.
Cao, X., Wang, Z., Jing, H., Folta, T., Delmar, F., and Zhu R., "Analyzing Big Data in Entrepreneurship: Replicating and Extending Evans and Leighton (1989)", Academy of Management Annual Meeting Proceedings 2022.
Jing, H., Kao, C., and Zhang, Z., "High-dimensional Distributionally Robust Minimumvariance Portfoliio Estimation", Mathematics 2023, 11(5), 1272.
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We propose a high dimensional distributionally robust mean-variance portfolio estimation procedure. The estimator is formulated as the solution to a minimax problem with a Wasserstein metric-based ambiguity set. We provide a one-stop data-driven solution to implement our method. Based on our simulation results and empirical study using monthly S&P 100 index constituent stock returns, our proposed portfolio has the best out-of-sample performance in terms of the least risk compared to the deterministic mean-variance portfolio and the equal weights portfolio when the required returns are set to be the same.
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Abstract: Analyzing big data requires the use of more powerful big data analytical techniques. The risk of not employing such techniques is that we misunderstand the true relationships in fundamental issues. In the study, we replicate and extend Evans and Leighton (1989) to revisit the entrepreneurial entry problem using a traditional logistic regression and one type of big data techniques – random forests. Through comparing the discrepant findings of these two models, we assert the benefits of using contemporary approaches to handle big data in revisiting fundamental questions in entrepreneurship.
Jing, Huarui, "The Robustness Study of Sieve Estimation on Asset Pricing Model"
Abstract: Christensen (2017) proposes the Perron-Frobenius sieve estimation model to study the stochastic discount factor (SDF) by assuming the intertemporal substitution rate (EIS) is 1. In this paper, I generate several economies with non-unit EIS to study the robustness of sieve estimation. The main empirical application steps and the SDF decomposition have been replicated when EIS is not 1. The focus of this paper is the robustness study based on the estimation surface for the two most relevant parameters in the Perron-Frobenius sieve model: the time discount factor and the risk aversion parameter. From what I find in the robustness study, estimating the time discount factor is robust but estimating the risk aversion parameter is not. The reasons for the failure to estimate the risk aversion parameter are the limitation of the consumption based asset pricing model and the Perron-Frobenius sieve model setup for value function estimation.