IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i5p783-d761542.html
   My bibliography  Save this article

Revisiting the Copula-Based Trading Method Using the Laplace Marginal Distribution Function

Author

Listed:
  • Tayyebeh Nadaf

    (Department of Mathematics, Hamedan Branch, Islamic Azad University, Hamedan 1584743311, Iran)

  • Taher Lotfi

    (Department of Mathematics, Hamedan Branch, Islamic Azad University, Hamedan 1584743311, Iran)

  • Stanford Shateyi

    (Department of Mathematics and Applied Mathematics, School of Mathematical and Natural Sciences, University of Venda, P. Bag X5050, Thohoyandou 0950, South Africa)

Abstract

Pairs trading under the copula approach is revisited in this paper. It is well known that financial returns arising from indices in markets may not follow the features of normal distribution and may exhibit asymmetry or fatter tails, in particular. Due to this, the Laplace distribution is employed in this work to fit the marginal distribution function, which will then be employed in a copula function. In fact, a multivariate copula function is constructed on two indices (based on the Laplace marginal distribution), enabling us to obtain the associated probabilities required for the process of pairs trade and creating an efficient tool for trading.

Suggested Citation

  • Tayyebeh Nadaf & Taher Lotfi & Stanford Shateyi, 2022. "Revisiting the Copula-Based Trading Method Using the Laplace Marginal Distribution Function," Mathematics, MDPI, vol. 10(5), pages 1-9, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:5:p:783-:d:761542
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/5/783/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/5/783/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. James W. Taylor, 2019. "Forecasting Value at Risk and Expected Shortfall Using a Semiparametric Approach Based on the Asymmetric Laplace Distribution," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(1), pages 121-133, January.
    2. Paravee Maneejuk & Woraphon Yamaka, 2021. "The Role of Economic Contagion in the Inward Investment of Emerging Economies: The Dynamic Conditional Copula Approach," Mathematics, MDPI, vol. 9(20), pages 1-23, October.
    3. Nicholas L. Georgakopoulos, 2018. "Illustrating Finance Policy with Mathematica," Quantitative Perspectives on Behavioral Economics and Finance, Palgrave Macmillan, number 978-3-319-95372-4, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jiting Tang & Fuyu Hu & Yimeng Liu & Weiping Wang & Saini Yang, 2022. "High-Resolution Hazard Assessment for Tropical Cyclone-Induced Wind and Precipitation: An Analytical Framework and Application," Sustainability, MDPI, vol. 14(21), pages 1-18, October.
    2. He, Fuli & Yarahmadi, Ali & Soleymani, Fazlollah, 2024. "Investigation of multivariate pairs trading under copula approach with mixture distribution," Applied Mathematics and Computation, Elsevier, vol. 472(C).

    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. Malik Zaka Ullah & Fouad Othman Mallawi & Mir Asma & Stanford Shateyi, 2022. "On the Conditional Value at Risk Based on the Laplace Distribution with Application in GARCH Model," Mathematics, MDPI, vol. 10(16), pages 1-13, August.
    2. Qifa Xu & Lu Chen & Cuixia Jiang & Yezheng Liu, 2022. "Forecasting expected shortfall and value at risk with a joint elicitable mixed data sampling model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 407-421, April.
    3. Dimitriadis, Timo & Schnaitmann, Julie, 2021. "Forecast encompassing tests for the expected shortfall," International Journal of Forecasting, Elsevier, vol. 37(2), pages 604-621.
    4. Chao Wang & Richard Gerlach, 2021. "A Bayesian realized threshold measurement GARCH framework for financial tail risk forecasting," Papers 2106.00288, arXiv.org, revised Oct 2022.
    5. Gerlach, Richard & Wang, Chao, 2020. "Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting, incorporating realized measures," International Journal of Forecasting, Elsevier, vol. 36(2), pages 489-506.
    6. Saissi Hassani, Samir & Dionne, Georges, 2021. "The New International Regulation of Market Risk: Roles of VaR and CVaR in Model Validation," Working Papers 21-1, HEC Montreal, Canada Research Chair in Risk Management.
    7. Fazlollah Soleymani & Andrey Itkin, 2019. "Pricing foreign exchange options under stochastic volatility and interest rates using an RBF--FD method," Papers 1903.00937, arXiv.org.
    8. Kanga, Désiré & Soumaré, Issouf & Amenounvé, Edoh, 2023. "Can corporate financing through the stock market create systemic risk? Evidence from the BRVM securities market," Emerging Markets Review, Elsevier, vol. 55(C).
    9. Takaaki Koike & Cathy W. S. Chen & Edward M. H. Lin, 2024. "Forecasting and Backtesting Gradient Allocations of Expected Shortfall," Papers 2401.11701, arXiv.org, revised Jun 2024.
    10. Timo Dimitriadis & Xiaochun Liu & Julie Schnaitmann, 2020. "Encompassing Tests for Value at Risk and Expected Shortfall Multi-Step Forecasts based on Inference on the Boundary," Papers 2009.07341, arXiv.org.
    11. Guillen, Montserrat & Bermúdez, Lluís & Pitarque, Albert, 2021. "Joint generalized quantile and conditional tail expectation regression for insurance risk analysis," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 1-8.
    12. Rehman, Mobeen Ur & Owusu Junior, Peterson & Ahmad, Nasir & Vo, Xuan Vinh, 2022. "Time-varying risk analysis for commodity futures," Resources Policy, Elsevier, vol. 78(C).
    13. Giampiero Gallo & Ostap Okhrin & Giuseppe Storti, 2024. "Dynamic tail risk forecasting: what do realized skewness and kurtosis add?," Papers 2409.13516, arXiv.org.
    14. Li, Chenxing & Zhang, Zehua & Zhao, Ran, 2024. "Volatility or higher moments: Which is more important in return density forecasts of stochastic volatility model?," Finance Research Letters, Elsevier, vol. 67(PB).
    15. Fazlollah Soleymani & Mahdi Vasighi, 2022. "Efficient portfolio construction by means of CVaR and k‐means++ clustering analysis: Evidence from the NYSE," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3679-3693, July.
    16. Huang, Yirong & Luo, Yi, 2024. "Forecasting conditional volatility based on hybrid GARCH-type models with long memory, regime switching, leverage effect and heavy-tail: Further evidence from equity market," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    17. Giuseppe Storti & Chao Wang, 2023. "Modeling uncertainty in financial tail risk: A forecast combination and weighted quantile approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1648-1663, November.
    18. Alexander Arimond & Damian Borth & Andreas Hoepner & Michael Klawunn & Stefan Weisheit, 2020. "Neural Networks and Value at Risk," Papers 2005.01686, arXiv.org, revised May 2020.
    19. Rita Pimentel & Morten Risstad & Sjur Westgaard, 2022. "Predicting interest rate distributions using PCA & quantile regression," Digital Finance, Springer, vol. 4(4), pages 291-311, December.
    20. Nguyen, Hoang & Javed, Farrukh, 2023. "Dynamic relationship between Stock and Bond returns: A GAS MIDAS copula approach," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 272-292.

    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:jmathe:v:10:y:2022:i:5:p:783-:d:761542. 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.