Skip to main content
SpringerLink
Log in

Tail Risk Measures and Portfolio Selection

  • Chapter
  • First Online:
Behavioral Predictive Modeling in Economics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 897))

  • 774 Accesses

Abstract

Since Markowitz [13] propose the mean-variance efficient portfolio selection method it has been one of the frequently used approach to the portfolio optimization problem. However, as we know, this approach has critical draw backs such as unstable assets weights and poor forecasting performance due to the estimation error. In this study, we propose an improved portfolio selection rules using various distortion functions. Our approach can make up for the pessimism of economic agents which is important for decision making. We illustrate the procedure by four well-known datasets. We also evaluate the performance of proposed and many other portfolio strategies to compare the in- and out-of-sample value at risk, conditional value at risk and Sharpe ratio. Empirical studies show that the proposed portfolio strategy outperforms many other strategies for most of evaluation measures.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    All datasets are obtained from Kenneth French’s homepage.

References

  1. Acerbi, C., Simonetti, P.: Portfolio optimization with spectral measures of risk. Rot S Man (2002)

    Google Scholar 

  2. Acerbi, C., Tasche, D.: Expected shortfall: a natural coherent alternative to value at risk. Econ. Notes 31, 379–388 (2002)

    Article  Google Scholar 

  3. Artzner, P., Delbaen, F., Eber, J.M., Heath, D.: Coherent measures of risk. Math. Financ. 9, 203–228 (1999)

    Article  MathSciNet  Google Scholar 

  4. Bassett, G.W., Koenker, R., Kordas, G.: Pessimistic portfolio allocation and choquet expected utility. J. Financ. Econ. 2, 477–492 (2004)

    Google Scholar 

  5. Copeland, T.E., Weston, J.F.: Financial Theory and Corporate Policy. Pearson Addison Wesley, Boston (1998)

    Google Scholar 

  6. DeGiorgi, E.: Reward risk portfolio selection and dtochastic dominance. J. Bank. Financ. 29, 895–926 (2005)

    Article  Google Scholar 

  7. Delage, E., Ye, Y.: Distributionally robust optimization under moment uncertainty with application to data-driven problems. Oper. Res. 58, 595–612 (2010)

    Article  MathSciNet  Google Scholar 

  8. Duffie, D., Pan, J.: An overview of value at risk. J. Deriv. 4, 7–49 (1997)

    Article  Google Scholar 

  9. Föllmer, H., Schied, A.: Stochastic Finance: An Introduction in Discrete Time. Walter de Gruyter, Berlin (2004)

    Book  Google Scholar 

  10. Ghaoui, L.E., Oks, M., Oustry, F.: Worst-case value-at-pisk and robust portfolio optimization: a conic programming approach. Oper. Res. 51, 543–556 (2003)

    Article  MathSciNet  Google Scholar 

  11. Jorion, P.: Value at Risk: The New Benchmark for Managing Financial Risk. Irwin, Chicago (1997)

    Google Scholar 

  12. Koenker, R., Bassett, G.: Regression quantiles. Econometrica 46, 33–50 (1978)

    Article  MathSciNet  Google Scholar 

  13. Markowitz, H.: Portfolio selection. J. Financ. 7, 77–91 (1952)

    Google Scholar 

  14. Rockafellar, R.T., Uryasev, S.: Optimization of conditional value-at-risk. J. Risk 2, 21–41 (2000)

    Article  Google Scholar 

  15. Ruszczynski, A., Shapiro, A.: Optimization of convex risk functions. Math. Oper. Res. 31, 433–452 (2006)

    Article  MathSciNet  Google Scholar 

  16. Scherer, B.: Portfolio resampling: review and critique. Financ. Anal. J. 58, 98–109 (2002)

    Article  Google Scholar 

  17. Wang, S.S.: A class of distortion operators for pricing financial and insurance risks. J. Risk Insur. 67, 15–36 (2000)

    Article  Google Scholar 

  18. Wang, S.S., Young, V.R., Panjer, H.H.: Axiomatic characterization of insurance prices. Insur. Math. Econ. 21, 173–183 (1997)

    Article  MathSciNet  Google Scholar 

  19. Wirch, J.L., Hardy, M.: Distortion risk measures: coherence and stochastic dominance. Insur. Math. Econ. 32, 168 (2003)

    Google Scholar 

  20. Wozabal, D.: Robustifying convex risk measures for linear portfolios: a nonparametric approach. Oper. Res. 62, 1302–1315 (2014)

    Article  MathSciNet  Google Scholar 

  21. Yaari, M.E.: The dual theory of choice under risk. Econometrica 55, 95–115 (1987)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgment

This research was supported by the Chung-Ang University research grant in 2020.

Author information

Authors and Affiliations

  1. Shandong University, 27 Shanda Nanlu, Jinan, China

    Young C. Joo

  2. Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, Korea

    Sung Y. Park

Authors
  1. Young C. Joo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sung Y. Park
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sung Y. Park .

Editor information

Editors and Affiliations

  1. Faculty of Economics, Chiang Mai University, Chiang Mai, Thailand

    Songsak Sriboonchitta

  2. Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA

    Vladik Kreinovich

  3. Faculty of Economics, Center of Excellence in Econometrics, Chiang Mai University, Chiang Mai, Thailand

    Woraphon Yamaka

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Joo, Y.C., Park, S.Y. (2021). Tail Risk Measures and Portfolio Selection. In: Sriboonchitta, S., Kreinovich, V., Yamaka, W. (eds) Behavioral Predictive Modeling in Economics. Studies in Computational Intelligence, vol 897. Springer, Cham. https://doi.org/10.1007/978-3-030-49728-6_7

Download citation

Publish with us

Policies and ethics

Search

Navigation