Skip to main content
SpringerLink
Log in
Book cover

International Econometric Conference of Vietnam

ECONVN 2018: Econometrics for Financial Applications pp 440–449Cite as

A Convex Combination Method for Quantile Regression with Interval Data

  • Conference paper
  • First Online:

Part of the book series: Studies in Computational Intelligence ((SCI,volume 760))

Abstract

This paper studies a quantile regression under asymmetric Laplace distribution (semi-parametric model) with interval valued data. Generally, the center point of the interval data has been used to represent the sample data for estimated parameter of the model. This paper uses the convex combination method to find the best point to estimate parameter in the quantile regression model. We apply the quantile capital asset pricing model (quantile CAPM) to present the result.

This is a preview of subscription content, 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   259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   329.99
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

Learn about institutional subscriptions

References

  1. Koenker, R., Bassett Jr., G.: Regression quantiles. Econom. J. Econom. Soc. 46(1), 33–50 (1978)

    MathSciNet  MATH  Google Scholar 

  2. Xie, S., Wan, A.T., Zhou, Y.: Quantile regression methods with varying-coefficient models for censored data. Comput. Stat. Data Anal. 88, 154–172 (2015)

    Article  MathSciNet  Google Scholar 

  3. Frumento, P., Bottai, M.: An estimating equation for censored and truncated quantile regression. Comput. Stati. Data Anal. 113, 53–63 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  4. Das, P., Ghosal, S.: Bayesian quantile regression using random B-spline series prior. Comput. Stat. Data Anal. 109, 121–143 (2017)

    Article  MathSciNet  Google Scholar 

  5. Galvao, A.F., Kato, K.: Smoothed quantile regression for panel data. J. Econom. 193(1), 92–112 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  6. Wu, Q., Yao, W.: Mixtures of quantile regressions. Comput. Stat. Data Anal. 93, 162–176 (2016)

    Article  MathSciNet  Google Scholar 

  7. Fagundes, R.A., De Souza, R.M., Cysneiros, F.J.A.: Interval kernel regression. Neurocomputing 128, 371–388 (2014)

    Article  Google Scholar 

  8. Koenker, R., Machado, J.A.: Goodness of fit and related inference processes for quantile regression. J. Am. Stat. Assoc. 94(448), 1296–1310 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  9. Tanaka, H., Uejima, S., Asai, K.: Linear regression analysis with fuzzy model. IEEE Trans. Syst. Man Cybern. 12(6), 903–907 (1982)

    Article  MATH  Google Scholar 

  10. William, F.: Capital asset prices: a theory of market equilibrium under conditions of risk. J. Financ. 19(3), 425–442 (1964)

    Google Scholar 

  11. Billard, L., Diday, E., Regression analysis for interval-valued data. In: Data Analysis, Classification, and the Related Methods, pp. 369–374. Springer (2000)

    Google Scholar 

  12. Moore, R.E., Kearfott, R.B., Cloud, M.J.: Introduction to Interval Analysis, pp. 7–18. SIAM, Philadelphia (2009)

    Book  MATH  Google Scholar 

  13. Nguyen, H.T., Kreinovich, V., Wu, B., Xiang, G.: Computing Statistics Under Interval and Fuzzy Uncertainty. Studies in Computational Intelligence, vol. 393. Springer, Heidelberg (2012)

    MATH  Google Scholar 

  14. Billard, L., Diday, E.: Symbolic regression analysis. In: Classification, Clustering, and Data Analysis, pp. 281–288. Springer (2002)

    Google Scholar 

  15. Neto, E.A.L., Carvalho, F.A.T.: Centre and range method for fitting a linear regression model to symbolic interval data. Comput. Stat. Data Anal. 52, 1500–1515 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  16. Maia, A.L.S., de Carvalho, F.D.A., Ludermir, T.B.: Forecasting models for interval-valued time series. Neurocomputing 71(16), 3344–3352 (2008)

    Article  Google Scholar 

  17. Domingues, M.A.O., Souza, R.M.C.R., Cysneiros, F.J.A.: A robust method for linear regression of symbolic interval data. Pattern Recogn. Lett. 31, 1991–1996 (2010)

    Article  Google Scholar 

  18. Fagundes, R.A., de Souza, R.M., Soares, Y.M.: Quantile regression of interval-valued data. In: Proceeding of the 2016 23rd International IEEE Conference on Pattern Recognition, pp. 2586–2591

    Google Scholar 

  19. Autchariyapanitkul, K., Chanaim, S., Sriboonchitta, S.: Quantile regression under asymmetric Laplace distribution in capital asset pricing model. In: Econometrics of Risk, pp. 219–231. Springer (2015)

    Google Scholar 

  20. Autchariyapanitkul, K., Chanaim, S., Sriboonchitta, S.: Evaluation of portfolio returns in Fama-French model using quantile regression under asymmetric Laplace distribution. In: Econometrics of Risk, pp. 233–244. Springer (2015)

    Google Scholar 

  21. John, L.: The valuation of risk assets and selection of risky investments in stock. Rev. Econ. Stat. 47(1), 13–37 (1965)

    Article  Google Scholar 

  22. Chanaim, S., Sriboonchitta, S., Rungruang, C.: A convex combination method for linear regression with interval data. In: Proceeding of the 2016 5th the International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM2016), pp. 469-480. Springer

    Google Scholar 

Download references

Acknowledgement

The authors thank Prof. Dr. Vladik Kreinovich for giving comments on manuscript. We are grateful for financial support from Center of Excellence in Econometrics, Faculty of Economics and Graduate School, Chiang Mai University.

Author information

Authors and Affiliations

  1. Center of Excellence in Econometrics, Chiang Mai University, Chiang Mai, 50200, Thailand

    Somsak Chanaim, Chatchai Khiewngamdee & Songsak Sriboonchitta

  2. Faculty of Economics, Chiang Mai University, Chiang Mai, 50200, Thailand

    Somsak Chanaim & Songsak Sriboonchitta

  3. Department of Agricultural Economy and Development, Faculty of Agriculture, Chiang Mai University, Chiang Mai, 50200, Thailand

    Chatchai Khiewngamdee

  4. Faculty of Commerce and Management, Prince of Songkla University, Trang Campus, Trang, 92000, Thailand

    Chongkolnee Rungruang

Authors
  1. Somsak Chanaim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chatchai Khiewngamdee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Songsak Sriboonchitta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chongkolnee Rungruang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Somsak Chanaim .

Editor information

Editors and Affiliations

  1. Banking University HCMC, Ho Chi Minh City, Vietnam

    Ly H. Anh

  2. Banking University HCMC, Ho Chi Minh City, Vietnam

    Le Si Dong

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

    Vladik Kreinovich

  4. Banking University HCMC, Ho Chi Minh City, Vietnam

    Nguyen Ngoc Thach

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chanaim, S., Khiewngamdee, C., Sriboonchitta, S., Rungruang, C. (2018). A Convex Combination Method for Quantile Regression with Interval Data. In: Anh, L., Dong, L., Kreinovich, V., Thach, N. (eds) Econometrics for Financial Applications. ECONVN 2018. Studies in Computational Intelligence, vol 760. Springer, Cham. https://doi.org/10.1007/978-3-319-73150-6_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-73150-6_35

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73149-0

  • Online ISBN: 978-3-319-73150-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation