IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i13p3325-d378119.html
   My bibliography  Save this article

Application of IRT Models to Selection of Bidding Paths in Financial Transmission Rights Auction: U.S. New England

Author

Listed:
  • Peter Y. Jang

    (Department of Industrial, Manufacturing, and Systems Engineering, Texas Tech University, Lubbock, TX 79409, USA)

  • Kwanghee Jung

    (Department of Educational Psychology and Leadership, Texas Tech University, Lubbock, TX 79409, USA)

  • Mario G. Beruvides

    (Department of Industrial, Manufacturing, and Systems Engineering, Texas Tech University, Lubbock, TX 79409, USA)

Abstract

This paper explores a way to apply Item Response Theory (IRT), one of the popular statistical methodologies in measurement and psychometrics, to evaluate Financial Transmission Rights (FTR) paths in the U.S. electricity market. FTR is an energy derivative product to hedge congestion cost risks inherent in constrained transmission lines. In New England, with about 1200 pricing locations, the theoretical combinations of FTR paths amount to 1.4 million in prevailing flows alone. With capital constraints, it is imperative that FTR market participants build the capability to evaluate FTR paths to bid on. IRT provides a framework of how well tests work, and how individual items work on tests, estimating respondents’ latent abilities, and individual item parameters. IRT is utilized to analyze historical electricity data of 2019 for a daily congestion cost of eight customer load zones and one hub in the U.S., New England, for the evaluation of FTR paths. In the analysis, an item represents an FTR path, while item difficulty, item discrimination, and a latent trait variable for the path correspond to the path profitability, risk level, and daily congestion ability, respectively. This paper explores the experimental procedures by which IRT, a psychometric tool, may also be applicable in complex energy markets, providing a consistent and standardized analytical framework to address the issues of selection and prioritization among multiple opportunities. FTR path evaluation is conducted in three steps to determine bid priority paths in FTR auctions: parameter significance tests, ranking on path profitability and risk level, and weighting scores of individual rankings on the two criteria.

Suggested Citation

  • Peter Y. Jang & Kwanghee Jung & Mario G. Beruvides, 2020. "Application of IRT Models to Selection of Bidding Paths in Financial Transmission Rights Auction: U.S. New England," Energies, MDPI, vol. 13(13), pages 1-13, June.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:13:p:3325-:d:378119
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/13/3325/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/13/3325/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Melissa A. Z. Knoll & Carrie R. Houts, 2012. "The Financial Knowledge Scale: An Application of Item Response Theory to the Assessment of Financial Literacy," Journal of Consumer Affairs, Wiley Blackwell, vol. 46(3), pages 381-410, September.
    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. Thomas A. Hanson, 2022. "Family Communication, Privacy Orientation, & Financial Literacy: A Survey of U.S. College Students," JRFM, MDPI, vol. 15(11), pages 1-13, November.
    2. Rob Ranyard & Simon McNair & Gianni Nicolini & Darren Duxbury, 2020. "An item response theory approach to constructing and evaluating brief and in‐depth financial literacy scales," Journal of Consumer Affairs, Wiley Blackwell, vol. 54(3), pages 1121-1156, September.
    3. Vieira, Kelmara Mendes & Potrich, Ani Caroline Grigion & Bressan, Aureliano Angel, 2020. "A proposal of a financial knowledge scale based on item response theory," Journal of Behavioral and Experimental Finance, Elsevier, vol. 28(C).
    4. Maya KATENOVA & Sang HOON LEE, 2020. "A comparative study of financial literacy, retirement planning and delinquency in payment: the Kazakhstan case Abstract: Financial knowledge is assumed to help people in making good choices in their f," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 11, pages 273-292, June.
    5. Paola Bongini & Paolo Trivellato & Mariangela Zenga, 2015. "Business Students and Financial Literacy: When Will the Gender Gap Fade away?," Journal of Financial Management, Markets and Institutions, Società editrice il Mulino, issue 1, pages 13-30, June.
    6. Ester Muñoz-Céspedes & Raquel Ibar-Alonso & Sara de Lorenzo Ros, 2021. "Financial Literacy and Sustainable Consumer Behavior," Sustainability, MDPI, vol. 13(16), pages 1-21, August.
    7. Dominik M. Piehlmaier, 2022. "Overconfidence and the adoption of robo-advice: why overconfident investors drive the expansion of automated financial advice," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-24, December.
    8. Donald J. Lacombe & Nasima Khatun, 2023. "What are the determinants of financial well‐being? A Bayesian LASSO approach," American Journal of Economics and Sociology, Wiley Blackwell, vol. 82(1), pages 43-59, January.
    9. Gilles E. Gignac & Elizabeth Ooi, 2022. "Measurement error in research on financial literacy: How much error is there and how does it influence effect size estimates?," Journal of Consumer Affairs, Wiley Blackwell, vol. 56(2), pages 938-956, June.
    10. Tsun‐Feng Chiang, 2021. "Financial capability and investment management of Chinese households: An application of hybrid item response theory," Journal of Consumer Affairs, Wiley Blackwell, vol. 55(4), pages 1442-1463, December.
    11. Irina Kunovskaya & Brenda Cude & Natalia Alexeev, 2014. "Evaluation of a Financial Literacy Test Using Classical Test Theory and Item Response Theory," Journal of Family and Economic Issues, Springer, vol. 35(4), pages 516-531, December.
    12. Ling Peng & Geng Cui & Yuho Chung & Chunyu Li, 2019. "A multi-facet item response theory approach to improve customer satisfaction using online product ratings," Journal of the Academy of Marketing Science, Springer, vol. 47(5), pages 960-976, September.
    13. Salim Moussa, 2016. "A two-step item response theory procedure for a better measurement of marketing constructs," Journal of Marketing Analytics, Palgrave Macmillan, vol. 4(1), pages 28-50, March.
    14. Oberrauch, Luis & Kaiser, Tim, 2022. "Cognitive ability, financial literacy, and narrow bracketing in time-preference elicitation," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 98(C).
    15. Maximilian D. Schmeiser & Jason S. Seligman, 2013. "Using the Right Yardstick: Assessing Financial Literacy Measures by Way of Financial Well-Being," Journal of Consumer Affairs, Wiley Blackwell, vol. 47(2), pages 243-262, July.
    16. Elizabeth Ooi, 2020. "Give mind to the gap: Measuring gender differences in financial knowledge," Journal of Consumer Affairs, Wiley Blackwell, vol. 54(3), pages 931-950, September.
    17. Fernando Oliveira Tavares & Eulália Santos, 2020. "Financial Literacy Perception Scale for the Portuguese Population," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 67(2), pages 277-290, June.
    18. Marc Oliver Rieger, 2020. "How to Measure Financial Literacy?," JRFM, MDPI, vol. 13(12), pages 1-14, December.
    19. Andrzej Cwynar & Beata Świecka & Kamil Filipek & Robert Porzak, 2022. "Consumers' knowledge of cashless payments: Development, validation, and usability of a measurement scale," Journal of Consumer Affairs, Wiley Blackwell, vol. 56(2), pages 640-665, June.
    20. Callis, Zoe & Gerrans, Paul & Walker, Dana L. & Gignac, Gilles E., 2023. "The association between intelligence and financial literacy: A conceptual and meta-analytic review," Intelligence, Elsevier, vol. 100(C).

    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:gam:jeners:v:13:y:2020:i:13:p:3325-:d:378119. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.