IDEAS home Printed from https://ideas.repec.org/a/pal/risman/v22y2020i3d10.1057_s41283-020-00060-5.html
   My bibliography  Save this article

Singular spectrum analysis for modelling the hard-to-model risk factors

Author

Listed:
  • Andrés Berenguer

    (Market Risk, CIB, Santander Bank)

  • Luis Gandarias

    (Market Risk, CIB, Santander Bank)

  • Álvaro Arévalo

    (Market Risk, CIB, Santander Bank)

Abstract

The modelling of the hard-to-model risks factors is one of the topics of great interest to the financial industry. The industry is spending lots of resources on efforts to account for the hard-to-model risks in their risk management frameworks. Currently, the concept describing these risks is the Risk Not in VaR. In its turn, the newly composed Fundamental Review of the Trading Book text similarly prescribes to classify risk factors that do not have a history of continuously available real prices as non-modellable risk factors. Both entities and financial regulatory authorities have shown great concern in the search for efficient techniques and models that allow for a more accurate estimation of the risks factors linked to the derivatives. An accurate modelling of these risk factors can lead to considerable optimization in the capital charges, but any model assumption must be duly justified and supported by the entities. In this paper, the (Multichannel) Singular Spectrum Analysis for modelling these risk factors is analysed.

Suggested Citation

  • Andrés Berenguer & Luis Gandarias & Álvaro Arévalo, 2020. "Singular spectrum analysis for modelling the hard-to-model risk factors," Risk Management, Palgrave Macmillan, vol. 22(3), pages 178-191, September.
  • Handle: RePEc:pal:risman:v:22:y:2020:i:3:d:10.1057_s41283-020-00060-5
    DOI: 10.1057/s41283-020-00060-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41283-020-00060-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1057/s41283-020-00060-5?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. Md Atikur Rahman Khan & D. S. Poskitt, 2013. "Moment tests for window length selection in singular spectrum analysis of short– and long–memory processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(2), pages 141-155, March.
    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. Ming-Fu Hsu & Chingho Chang & Jhih‐Hong Zeng, 2022. "Automated text mining process for corporate risk analysis and management," Risk Management, Palgrave Macmillan, vol. 24(4), pages 386-419, December.

    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. Arteche, Josu & García-Enríquez, Javier, 2017. "Singular Spectrum Analysis for signal extraction in Stochastic Volatility models," Econometrics and Statistics, Elsevier, vol. 1(C), pages 85-98.
    2. Papailias, Fotis & Thomakos, Dimitrios, 2017. "EXSSA: SSA-based reconstruction of time series via exponential smoothing of covariance eigenvalues," International Journal of Forecasting, Elsevier, vol. 33(1), pages 214-229.

    More about this item

    Keywords

    Banking; Capital; FRTB; Matrix decomposition; Risk; RNIV; Singular spectrum analysis; Time series; VaR;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation

    Statistics

    Access and download statistics

    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:pal:risman:v:22:y:2020:i:3:d:10.1057_s41283-020-00060-5. 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.palgrave.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.