IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v330y2023i1d10.1007_s10479-022-04573-y.html
   My bibliography  Save this article

Ranking econometric techniques using geometrical Benefit of Doubt

Author

Listed:
  • Konstantinos Petridis

    (University of Macedonia)

  • Nikolaos E. Petridis

    (University of Macedonia)

  • Fouad Ben Abdelaziz

    (Neoma Business School)

  • Hatem Masri

    (University of Bahrain)

Abstract

A large part of economies around the world rely on stock markets. To predict stock prices or commodities, econometric techniques are used. Analysts choose the suitable econometric technique according to error measures which may create confusion, especially in the case where there is no preference amongst error measures. Therefore, there is no unique score to rank econometric techniques based on multiple error measures. To bridge this gap, we propose, a MCDM like methodology [geometrical Benefit of Doubt (BoD)] that considers econometric techniques as alternatives and error measures as criteria. A real application with 194 econometric techniques and 8 error measures is presented. The efficiency scores derived from geometrical BoD model provide better discrimination and experiemental results indicate that ARMA and ARCH models are ranked higher. The proposed geometrical BoD model is compared to similar geometrical MCDM formulations.

Suggested Citation

  • Konstantinos Petridis & Nikolaos E. Petridis & Fouad Ben Abdelaziz & Hatem Masri, 2023. "Ranking econometric techniques using geometrical Benefit of Doubt," Annals of Operations Research, Springer, vol. 330(1), pages 411-430, November.
    • Handle: RePEc:spr:annopr:v:330:y:2023:i:1:d:10.1007_s10479-022-04573-y
    DOI: 10.1007/s10479-022-04573-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-022-04573-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-022-04573-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Thomakos, Dimitrios D. & Guerard, John Jr., 2004. "Naive, ARIMA, nonparametric, transfer function and VAR models: A comparison of forecasting performance," International Journal of Forecasting, Elsevier, vol. 20(1), pages 53-67.
    2. P. Zhou & B. Ang & D. Zhou, 2010. "Weighting and Aggregation in Composite Indicator Construction: a Multiplicative Optimization Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 96(1), pages 169-181, March.
    3. B. Hollingsworth & P. Smith, 2003. "Use of ratios in data envelopment analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 10(11), pages 733-735.
    4. Gregoriou, Greg N. & Sedzro, Komlan & Zhu, Joe, 2005. "Hedge fund performance appraisal using data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 164(2), pages 555-571, July.
    5. Roger Lord & Remmert Koekkoek & Dick Van Dijk, 2010. "A comparison of biased simulation schemes for stochastic volatility models," Quantitative Finance, Taylor & Francis Journals, vol. 10(2), pages 177-194.
    6. Yokuma, J. Thomas & Armstrong, J. Scott, 1995. "Beyond accuracy: Comparison of criteria used to select forecasting methods," International Journal of Forecasting, Elsevier, vol. 11(4), pages 591-597, December.
    7. Van Puyenbroeck, Tom & Rogge, Nicky, 2017. "Geometric mean quantity index numbers with Benefit-of-the-Doubt weights," European Journal of Operational Research, Elsevier, vol. 256(3), pages 1004-1014.
    8. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    9. Färe, Rolf & Karagiannis, Giannis, 2014. "Benefit-of-the-doubt aggregation and the diet problem," Omega, Elsevier, vol. 47(C), pages 33-35.
    10. Verbunt, Pim & Rogge, Nicky, 2018. "Geometric composite indicators with compromise Benefit-of-the-Doubt weights," European Journal of Operational Research, Elsevier, vol. 264(1), pages 388-401.
    11. Cristina Bernini & Andrea Guizzardi & Giovanni Angelini, 2013. "DEA-Like Model and Common Weights Approach for the Construction of a Subjective Community Well-Being Indicator," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 114(2), pages 405-424, November.
    12. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    13. Collopy, Fred & Armstrong, J. Scott, 1992. "Expert opinions about extrapolation and the mystery of the overlooked discontinuities," International Journal of Forecasting, Elsevier, vol. 8(4), pages 575-582, December.
    14. Pagan, Adrian R. & Schwert, G. William, 1990. "Alternative models for conditional stock volatility," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 267-290.
    15. R. F. Engle & A. J. Patton, 2001. "What good is a volatility model?," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 237-245.
    16. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    17. Pong, Shiuyan & Shackleton, Mark B. & Taylor, Stephen J. & Xu, Xinzhong, 2004. "Forecasting currency volatility: A comparison of implied volatilities and AR(FI)MA models," Journal of Banking & Finance, Elsevier, vol. 28(10), pages 2541-2563, October.
    18. Chris Tofallis, 2014. "On constructing a composite indicator with multiplicative aggregation and the avoidance of zero weights in DEA," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(5), pages 791-792, May.
    19. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    20. Charnes, A. & Cooper, W. W. & Golany, B. & Seiford, L. & Stutz, J., 1985. "Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 91-107.
    21. Stephen J Taylor, 2007. "Modelling Financial Time Series," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 6578.
    22. Jun Yu & Renate Meyer, 2006. "Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison," Econometric Reviews, Taylor & Francis Journals, vol. 25(2-3), pages 361-384.
    23. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    24. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    25. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    26. Morita, Hiroshi & Hirokawa, Koichiro & Zhu, Joe, 2005. "A slack-based measure of efficiency in context-dependent data envelopment analysis," Omega, Elsevier, vol. 33(4), pages 357-362, August.
    27. Pezzo, Rosanna & Uberti, Mariacristina, 2006. "Approaches to forecasting volatility: Models and their performances for emerging equity markets," Chaos, Solitons & Fractals, Elsevier, vol. 29(3), pages 556-565.
    28. Rogge, Nicky & De Jaeger, Simon & Lavigne, Carolien, 2017. "Waste Performance of NUTS 2-regions in the EU: A Conditional Directional Distance Benefit-of-the-Doubt Model," Ecological Economics, Elsevier, vol. 139(C), pages 19-32.
    29. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    30. B. Xu & J. Ouenniche, 2011. "A multidimensional framework for performance evaluation of forecasting models: context-dependent DEA," Applied Financial Economics, Taylor & Francis Journals, vol. 21(24), pages 1873-1890, December.
    31. Dima Alberg & Haim Shalit & Rami Yosef, 2008. "Estimating stock market volatility using asymmetric GARCH models," Applied Financial Economics, Taylor & Francis Journals, vol. 18(15), pages 1201-1208.
    32. Rogge, Nicky, 2018. "Composite indicators as generalized benefit-of-the-doubt weighted averages," European Journal of Operational Research, Elsevier, vol. 267(1), pages 381-392.
    33. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    34. Sfetsos, A., 2000. "A comparison of various forecasting techniques applied to mean hourly wind speed time series," Renewable Energy, Elsevier, vol. 21(1), pages 23-35.
    35. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    36. Bera, Anil K & Higgins, Matthew L, 1993. "ARCH Models: Properties, Estimation and Testing," Journal of Economic Surveys, Wiley Blackwell, vol. 7(4), pages 305-366, December.
    37. Engle, Robert F & Lilien, David M & Robins, Russell P, 1987. "Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model," Econometrica, Econometric Society, vol. 55(2), pages 391-407, March.
    38. Emrouznejad, Ali & Rostami-Tabar, Bahman & Petridis, Konstantinos, 2016. "A novel ranking procedure for forecasting approaches using Data Envelopment Analysis," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 235-243.
    39. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    40. Doumpos, Michalis & Figueira, José Rui, 2019. "A multicriteria outranking approach for modeling corporate credit ratings: An application of the Electre Tri-nC method," Omega, Elsevier, vol. 82(C), pages 166-180.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mehmet Sahiner, 2022. "Forecasting volatility in Asian financial markets: evidence from recursive and rolling window methods," SN Business & Economics, Springer, vol. 2(10), pages 1-74, October.
    2. Prateek Sharma & Vipul _, 2015. "Forecasting stock index volatility with GARCH models: international evidence," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 32(4), pages 445-463, October.
    3. Köksal, Bülent, 2009. "A Comparison of Conditional Volatility Estimators for the ISE National 100 Index Returns," MPRA Paper 30510, University Library of Munich, Germany.
    4. Xekalaki, Evdokia & Degiannakis, Stavros, 2005. "Evaluating volatility forecasts in option pricing in the context of a simulated options market," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 611-629, April.
    5. BAUWENS, Luc & HAFNER, Christian & LAURENT, Sébastien, 2011. "Volatility models," LIDAM Discussion Papers CORE 2011058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
      • Bauwens, L. & Hafner C. & Laurent, S., 2011. "Volatility Models," LIDAM Discussion Papers ISBA 2011044, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
      • Bauwens, L. & Hafner, C. & Laurent, S., 2012. "Volatility Models," LIDAM Reprints ISBA 2012028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    6. Milton Abdul Thorlie & Lixin Song & Muhammad Amin & Xiaoguang Wang, 2015. "Modeling and forecasting of stock index volatility with APARCH models under ordered restriction," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 69(3), pages 329-356, August.
    7. Hansen, Peter Reinhard & Lunde, Asger, 2006. "Consistent ranking of volatility models," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 97-121.
    8. Charles, Amélie, 2010. "The day-of-the-week effects on the volatility: The role of the asymmetry," European Journal of Operational Research, Elsevier, vol. 202(1), pages 143-152, April.
    9. Nikolaos A. Kyriazis, 2021. "A Survey on Volatility Fluctuations in the Decentralized Cryptocurrency Financial Assets," JRFM, MDPI, vol. 14(7), pages 1-46, June.
    10. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    11. Guidi, Francesco, 2008. "Volatility and Long Term Relations in Equity Markets: Empirical Evidence from Germany, Switzerland, and the UK," MPRA Paper 11535, University Library of Munich, Germany.
    12. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, July.
    13. El Jebari, Ouael & Hakmaoui, Abdelati, 2018. "GARCH Family Models vs EWMA: Which is the Best Model to Forecast Volatility of the Moroccan Stock Exchange Market? || Modelos de la familia GARCH vs EWMA: ¿cuál es el mejor modelo para pronosticar la ," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 26(1), pages 237-249, Diciembre.
    14. Vipul Kumar Singh, 2013. "Effectiveness of volatility models in option pricing: evidence from recent financial upheavals," Journal of Advances in Management Research, Emerald Group Publishing Limited, vol. 10(3), pages 352-375, October.
    15. Liang Peng & Rainer Schulz, 2013. "Does the Diversification Potential of Securitized Real Estate Vary Over Time and Should Investors Care?," The Journal of Real Estate Finance and Economics, Springer, vol. 47(2), pages 310-340, August.
    16. Roy Cerqueti & Massimiliano Giacalone & Raffaele Mattera, 2020. "Skewed non-Gaussian GARCH models for cryptocurrencies volatility modelling," Papers 2004.11674, arXiv.org.
    17. Shekar Bose & Hafizur Rahman, 2022. "Are News Effects Necessarily Asymmetric? Evidence from Bangladesh Stock Market," SAGE Open, , vol. 12(4), pages 21582440221, October.
    18. Awartani, Basel M.A. & Corradi, Valentina, 2005. "Predicting the volatility of the S&P-500 stock index via GARCH models: the role of asymmetries," International Journal of Forecasting, Elsevier, vol. 21(1), pages 167-183.
    19. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2005. "Volatility forecasting," CFS Working Paper Series 2005/08, Center for Financial Studies (CFS).
    20. Abdinardo Moreira Barreto Oliveira & Anandadeep Mandal & Gabriel J. Power, 2024. "Impact of COVID-19 on Stock Indices Volatility: Long-Memory Persistence, Structural Breaks, or Both?," Annals of Data Science, Springer, vol. 11(2), pages 619-646, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:330:y:2023:i:1:d:10.1007_s10479-022-04573-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.