A Risk-Averse Newsvendor Model under CVaR Decision Criterion

Operations Research, Forthcoming

19 Pages Posted: 22 Jan 2009

See all articles by Frank (Youhua) Chen

Frank (Youhua) Chen

City Univ. of Hong Kong - College of Business, Dept of Management Sciences

Minghui Xu

Wuhan University - School of Economics and Management

George Zhang

affiliation not provided to SSRN

Date Written: January 22, 2009

Abstract

The classical risk-neutral newsvendor problem is to decide the order quantity to maximize the one period expected pro.t under a given demand distribution. In this paper we consider a risk-averse newsvendor with a stochastic price-dependent demand. We use the Conditional Value-at-Risk (CVaR), a risk measure commonly used in finance, as the decision criterion. The aim of our study is to investigate the optimal pricing and inventory decisions in such a setting. For both additive and multiplicative demand models, we provide sufficient conditions for the uniqueness and the existence of the optimal policy. Performing comparative statics shows the monotonicity properties and other characteristics of the optimal pricing and ordering decisions. We also made comparisons between our results and those of risk-neutral newsvendor. Numerical examples are presented to gain more insights about the risk-averse decision making behavior.

Keywords: supply chain, newsvendor model with pricing, risk aversion, conditional value-at-risk, monotonicity

Suggested Citation

Chen, Frank (Youhua) and Xu, Minghui and Zhang, George, A Risk-Averse Newsvendor Model under CVaR Decision Criterion (January 22, 2009). Operations Research, Forthcoming , Available at SSRN: https://ssrn.com/abstract=1331304

Frank (Youhua) Chen (Contact Author)

City Univ. of Hong Kong - College of Business, Dept of Management Sciences ( email )

Kowloon Tong
Hong Kong

HOME PAGE: http://www.cb.cityu.edu.hk/staff/youhchen

Minghui Xu

Wuhan University - School of Economics and Management ( email )

Wuhan, Hu-bei 430072
China

George Zhang

affiliation not provided to SSRN ( email )

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