Estimating Promotion Effects Using Big Data: A Partially Profiled LASSO Model With Endogeneity Correction

Sun, Luping; Zheng, Xiaona; Jin, Ying; Jiang, Minghua; Wang, Hansheng

doi:10.2139/ssrn.3289112

Cited by 1 publication

(1 citation statement)

References 62 publications

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“…To achieve the optimal operational decisions with total provision for risk, two issues are critical, namely data collection and risk estimation. Considering the huge amount of data available with the rapid advances of information technology, it has become important and feasible to examine risk by using data analytics (Koyuncugil & Ozgulbas, 2012;Chen, Yang, Wang, & Tang, 2015;Choi, Chan, & Yue, 2017;Kou, Chao, Peng, Alsaadi, & Herrera-Viedma, 2019;Sun, Zheng, Jin, Jiang, & Wang, 2019;Wang & Wu, 2020). Credit default events are rare and thus the probability of large losses of credit portfolio is small.…”

Section: Introductionmentioning

confidence: 99%

A Study on Operational Risk and Credit Portfolio Risk Estimation Using Data Analytics*

et al. 2020

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In this article we consider operational risk and use data analytics to estimate the credit portfolio risk. Specifically, we consider situations in which managers need to make the optimal operational decision on total provision for risk to hedge against the potential risk in the entire supply chain. We build a new structural credit model integrated with data analytics to analyze the joint default risk of credit portfolio. Our model enables the decision maker to better assess the risk of a supply chain, so that they could determine the optimal operational decisions with total provision for risk, and react in a timely manner to economic and environmental changes. We propose an efficient simulation method to estimate the default probability of the credit portfolio with the risk factors having the multivariate t-copula. Moreover, we develop a three-step importance sampling (IS) method for the t-copula credit portfolio risk measurement model to achieve an accurate estimation of the tail probability of the credit portfolio loss distribution. We apply the Levenberg-Marquardt algorithm to estimate the mean-shift vector of the systematic risk factors after the probability measure change. Besides, we empirically * The authors would like to thank editors and three referees for valuable suggestions and helpful comments.

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