Measure of riskiness based on RDEU model

doi:10.52396/JUST-2021-0012

Journal of University of Science and Technology of China ›› 2021, Vol. 51 ›› Issue (1): 65-74.DOI: 10.52396/JUST-2021-0012

• Management Science and Engineering • Previous Articles Next Articles

Measure of riskiness based on RDEU model

Guo Chuanfeng¹, Du Xinze², Wu Qinyu³, Mao Tiantian^2*

1. School of Data Science, University of Science and Technology of China, Hefei 230026, China;
2. School of Management, University of Science and Technology of China, Hefei 230026, China

Online:2021-01-31 Published:2021-05-27
Contact: *E-mail: tmao@ustc.edu.cn
About author:Guo Chuanfeng is currently a graduate student under the tutelage of Prof. Mao Tiantian at University of Science and Technology of China. Her research interests focus on Risk measurement and practical application.
Du Xinze received a bachelor's degree in statistics from the University of Science and Technology of China and continued his studies. He is now a PhD in Applied Mathematics from the University of Southern California.
Wu Qinyu is a PhD candidate in the School of Management, University of Science and Technology of China, and his research direction is risk management and mathematical finance.
Mao Tiantian (corresponding author) is an associate professor at University of Science and Technology of China (USTC). In 2012, she obtained PhD in Science from the University of Science and Technology of China. In May of the same year, she entered the Department of Statistics and Finance of the School of Management for postdoctoral work. Her research fields include risk measurement, risk management, random dominance and extreme value theory, etc.

Abstract

Abstract: Motivated by References[3,4], we introduce a new measure of riskiness based on the rank-dependent expected utility (RDEU) model. The new measure of riskiness is a generalized class of risk measures which includes the economic index of riskiness of Reference[3] and the operational measure of riskiness of Reference[4] as special cases. We probe into the basic properties as a measure of riskiness such as monotonicity, positive homogeneity and subadditivity. We study its applications in comparative risk aversion as well. In addition, we present a simulation to illustrate the results.

Key words: measure of riskiness, RDEU model, distortion function

CLC Number:

TU459

GUO Chuanfeng, DU Xinze, WU Qinyu, MAO Tiantian. Measure of riskiness based on RDEU model[J]. Journal of University of Science and Technology of China, 2021, 51(1): 65-74.

References

[1] Artzner P, Delbaen F, Eber J M, et al. Coherent measures of risk. Mathematical Finance, 1999, 9(3): 203-228.
[2] Dowd K. Measuring Market Risk. Chichester: John Wiley & Sons, 2002.
[3] Aumann R J, Serrano R. An economic index of riskiness. Journal of Political Economy, 2008, 116(5): 810-836.
[4] Foster D P, Hart S. An operational measure of riskiness. Journal of Political Economy, 2009, 117(5): 785-814.
[5] Quiggin J. A theory of anticipated utility. Journal of Economic Behavior & Organization, 1982, 3(4): 323-343.
[6] Yaari M E. The dual theory of choice under risk. Econometrica, 1987, 55: 95-115.
[7] Mao T, Cai J. Risk measures based on behavioural economics theory. Finance and Stochastics, 2018, 22(2): 367-393.
[8] Diecidue E, Wakker P P. On the intuition of rank-dependent utility. Journal of Risk and Uncertainty, 2001, 23(3): 281-298.
[9] Merkle M, Marinescu D, Merkle M M R, et al. Lebegue-Stieljes integral and Young's inequality. Applicable Analysis and Discrete Mathematics, 2014, 8: 60-72.
[10] Wang S. Insurance pricing and increased limits ratemaking by proportional hazards transforms. Insurance: Mathematics and Economics, 1995, 17(1): 43-54.
[11] Wang S. Premium calculation by transforming the layer premium density. ASTIN Bulletin, 1996, 26: 71-92.
[12] Wang S. A class of distortion operators for pricing financial and insurance risks. Journal of Risk and Insurance, 2000, 67: 15-36.
[13] Caballe J, Esteban J. Stochastic dominance and absolute risk aversion. Social Choice and Welfare, 2007, 28(1): 89-110.
[14] Embrechts P, Wang R. Seven proofs for the subadditivity of expected shortfall. Dependence Modeling, 2015, 3(1): 126-140.
[15] Wirch J L, Hardy M R. Distortion risk measures. coherence and stochastic dominance. Insurance Mathematics and Economics, 2001, 32(1): 168-168.
[16] Riedel F. Dynamic coherent risk measures. Stochastic Processes and their Applications, 2004, 112(22): 185-200.

Measure of riskiness based on RDEU model

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 10

Recommended Articles

Metrics

Comments

[1]	Dabwan Yousef N, Pei Gang, Trevor Hocksun Kwan, Zhao Bing. A novel hybrid solar preheating intercooled gas turbine based on linear Fresnel reflector [J]. Journal of University of Science and Technology of China, 2021, 51(7): 505-520.
[2]	Tong Yao. Multi-valued indicators in DEA in the presence of undesirable outputs: A goal-directed approach [J]. Journal of University of Science and Technology of China, 2021, 51(7): 521-541.
[3]	Kong Bingxian, Shi Xiao, Yang Feng. Effects of rural tourism information quality in social media on tourists' travel intention [J]. Journal of University of Science and Technology of China, 2021, 51(7): 542-561.
[4]	Ling Huaze, Xue Kaiping, Wei David S.L., Li Ruidong. An efficient multi-user multi-keyword fuzzy search scheme over encrypted cloud storage [J]. Journal of University of Science and Technology of China, 2021, 51(7): 562-576.
[5]	Li Tingfang, Wang Xiao. Corporate resistance to pandemic: Evidence from China [J]. Journal of University of Science and Technology of China, 2021, 51(4): 288-298.
[6]	Liu Sen, Zhang Zhizheng, Yu Tao, Chen Zhibo. MOVIE: Mesh oriented video inpainting network [J]. Journal of University of Science and Technology of China, 2021, 51(1): 1-11.
[7]	HU Xinying, HE Yu, SUN Guangzhong. A cognitive diagnostic framework for computer science education based on probability graph model [J]. Journal of University of Science and Technology of China, 2021, 51(1): 12-21.
[8]	LIU Xinghua, BAI Dandan, SUN Baoren, WEN Jiayan, LV Wenjun, LI Kun. Event-triggered sliding mode load frequency control for multi-area interconnected power systems under deception attacks [J]. Journal of University of Science and Technology of China, 2021, 51(1): 33-42.
[9]	HE Haonan, REN Wei, WANG Zuohang, ZHAO Chenyong, WANG Shanyong, MA Fei. Effect of personal carbon trading on EV adoption behavior based on a stochastic Petri net [J]. Journal of University of Science and Technology of China, 2021, 51(1): 53-64.
[10]	LIU Peilin, LU Yuyang, ZHENG Dongchang, HE Linghui, NI Yong∗ . Modified Stoney formula for in-situ lithiation stress measurement in thin-film Si electrode [J]. Journal of University of Science and Technology of China, 2020, 50(12): 1516-1524.