Hostname: page-component-8448b6f56d-qsmjn Total loading time: 0 Render date: 2024-04-15T05:39:44.078Z Has data issue: false hasContentIssue false

Almost first-order stochastic dominance by distorted expectations

Published online by Cambridge University Press:  12 August 2022

Jianping Yang
Affiliation:
Department of Mathematical Sciences, School of Science, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China. E-mails: yangjp@zstu.edu.cn; ztian980117@163.com
Tian Zhou
Affiliation:
Department of Mathematical Sciences, School of Science, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China. E-mails: yangjp@zstu.edu.cn; ztian980117@163.com
Weiwei Zhuang
Affiliation:
International Institute of Finance, School of Management, University of Science and Technology of China, Hefei, Anhui 230026, China. E-mail: weizh@ustc.edu.cn

Abstract

Almost stochastic dominance has been receiving a great amount of attention in the financial and economic literatures. In this paper, we characterize the properties of almost first-order stochastic dominance (AFSD) via distorted expectations and investigate the conditions under which AFSD is preserved under a distortion transform. The main results are also applied to establish stochastic comparisons of order statistics and receiver operating characteristic curves via AFSD.

Type
Research Article
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Atkinson, A.B. (2008). More on the measurement of inequality. Journal of Economic Inequality 6(3): 277283.CrossRefGoogle Scholar
Balbás, A., Garrido, J., & Mayoral, S. (2009). Properties of distortion risk measures. Methodology & Computing in Applied Probability 11(3): 385399.CrossRefGoogle Scholar
Bali, T.G., Demirtas, K.O., Levy, H., & Wolf, A. (2009). Bonds versus stocks: Investors’ age and risk taking. Journal of Monetary Economics 56(6): 817830.CrossRefGoogle Scholar
Boland, P.J., Hu, T., Shaked, M., & Shanthikumar, J.G. (2002). Stochastic ordering of order statistics II. In M. Dror, P. L'Ecuyer, & F. Szidarovszky (eds), Modeling uncertainty: An examination of stochastic theory, methods, and applications. Boston: Kluwer Academic Publishers, pp. 607–623.CrossRefGoogle Scholar
Denuit, M., Dhaene, J., Goovaerts, M., & Kaas, R. (2005). Actuarial theory for dependent risks: Measures, orders and models. West Sussex: John Wiley & Sons, Ltd.CrossRefGoogle Scholar
Donaldson, D. & Weymark, J.A. (1983). Ethically flexible Gini indices for income distributions in continuum. Journal of Economic Theory 29(2): 353358.CrossRefGoogle Scholar
Fawcett, T. (2005). An introduction to ROC analysis. Pattern Recognition Letters 27(8): 861874.CrossRefGoogle Scholar
Föllmer, H. & Schied, A (2016). Stochastic finance: An introduction in discrete time. 4th ed. Berlin: Walter de Gruyter.CrossRefGoogle Scholar
Gigliarano, C., Figini, S., & Muliere, P. (2014). Making classifier performance comparisons when ROC curves intersect. Computational Statistics and Data Analysis 77: 300312.CrossRefGoogle Scholar
Guo, X., Zhu, X., Wong, W.-K., & Zhu, L. (2013). A note on almost stochastic dominance. Economics Letters 121: 252256.CrossRefGoogle Scholar
Guo, X., Post, T., Wong, W.-K., & Zhu, L. (2014). Moment conditions for almost stochastic dominance. Economics Letters 124: 163167.CrossRefGoogle Scholar
Guo, D., Hu, Y., Wang, S., & Zhao, L. (2016). Comparing risks with reference points: A stochastic dominance approach. Insurance: Mathematics and Economics 70: 105116.Google Scholar
Hadar, J. & Russell, W. (1969). Rules for ordering uncertain prospects. American Economic Review 59: 2534.Google Scholar
Hanoch, G. & Levy, H. (1969). The efficiency analysis of choice involving risk. The Review of Economic Studies 36(3): 335346.CrossRefGoogle Scholar
Kirmani, S. & Gupta, R. (2001). On the proportional odds model in survival analysis. Annals of the Institute of Statistical Mathematics 53(2): 203216.CrossRefGoogle Scholar
Lando, T., Arab, I., & Oliveira, P.E. (2021). Second-order stochastic comparisons of order statistics. Statistics 55(3): 561579.CrossRefGoogle Scholar
Leshno, M. & Levy, H. (2002). Prefered by all and preferred by most decision makers: Almost stochastic dominance. Management Science 48: 10741085.CrossRefGoogle Scholar
Levy, M. (2012). Almost stochastic dominance and efficient investment sets. American Journal of Operations Research 2: 313321.CrossRefGoogle Scholar
Levy, H. (2016). Stochastic dominance, 3rd ed. New York: Springer.CrossRefGoogle Scholar
Levy, H. & Wiener, Z. (1998). Stochastic dominance and prospect dominance with subjective weighting functions. Journal of Risk and Uncertainty 16(2): 147163.CrossRefGoogle Scholar
Levy, H., Leshno, M., & Leibovitch, B. (2010). Economically relevant preferences for all observed epsilon. Annals of Operations Research 176: 153178.CrossRefGoogle Scholar
Lusted, L. (1971). Signal detectability and medical decision-making. Science 171(3977): 12171219.CrossRefGoogle ScholarPubMed
Muliere, P. & Scarsini, M. (1989). A note on stochastic dominance and inequality measures. Journal of Economic Theory 49(2): 314323.CrossRefGoogle Scholar
Müller, A. & Stoyan, D. (2002). Comparison methods for stochastic models and risks. Chichester, UK: John Wiley & Sons.Google Scholar
Müller, A., Scarsini, M., Tsetlin, I., & Winkler, R. (2017). Between first- and second-order stochatic dominance. Management Science 63: 29332974.CrossRefGoogle Scholar
Müller, A., Scarsini, M., Tsetlin, I., & Winkler, R. (2021). Ranking distributions when only means and variances are known. Operations Research. doi:10.1287/opre.2020.2072Google Scholar
Navarro, J., Ruiz, J.M., & Aguila, Y.D. (2008). Characterizations and ordering properties based on log-odds functions. Statistics 42(4): 313328.CrossRefGoogle Scholar
Rothschild, M. & Stiglitz, J.E. (1970). Increasing risk: I. A definition. Journal of Economic Theory 2: 225243.CrossRefGoogle Scholar
Sankaran, P.G. & Jayakumar, K. (2008). On proportional odds models. Statistical Papers 49(4): 779789.CrossRefGoogle Scholar
Shaked, M. & Shanthikumar, J.G. (2007). Stochastic orders. New York: Springer.CrossRefGoogle Scholar
Tzeng, L.Y., Huang, R.J., & Shih, P.T. (2013). Revisiting almost second-degree stochastic dominance. Management Science 59: 12501254.CrossRefGoogle Scholar
Wang, S. (1995). Insurance pricing and increased limits ratemaking by propositional hazards transforms. Insurance: Mathematics and Economics 17: 4354.Google Scholar
Wang, S. (2000). A class of distortion operators for pricing financial and insurance risk. Journal of Risk and Insurance 67: 1536.CrossRefGoogle Scholar
Wang, S.S. & Young, V.R. (1998). Ordering risks: Expected utility theory versus Yaari's dual theory of risk. Insurance Mathematics and Economics 22(2): 145161.CrossRefGoogle Scholar
Yaari, M.E. (1987). The dual theory of choice under risk. Economerica 9: 5115.Google Scholar
Zimmer, W.J., Wang, Y., & Pathak, P.K. (1998). Log-odds rate and monotone log-odds rate distributions. Journal of Quality Technology 30(4): 376385.CrossRefGoogle Scholar