IDEAS home Printed from https://ideas.repec.org/a/eee/ecmode/v80y2019icp383-391.html

Modelling the spreading process of extreme risks via a simple agent-based model: Evidence from the China stock market

Author

Listed:
  • Ji, Jingru
  • Wang, Donghua
  • Xu, Dinghai

Abstract

This paper focuses on investigating financial asset returns' extreme risks, which are defined as the negative log-returns over a certain threshold. A simple agent-based model is constructed to explain the behavior of the market traders when extreme risks occur. We consider both the volatility clustering and the heavy tail characteristics when constructing the model. Empirical study uses the China securities index 300 daily level data and applies the method of simulated moments to estimate the model parameters. The stationarity and ergodicity tests provide evidence that the proposed model is good for estimation and prediction. The goodness-of-fit measures show that our proposed model fits the empirical data well. Our estimated model performs well in out-of-sample Value-at-Risk prediction, which contributes to the risk management.

Suggested Citation

  • Ji, Jingru & Wang, Donghua & Xu, Dinghai, 2019. "Modelling the spreading process of extreme risks via a simple agent-based model: Evidence from the China stock market," Economic Modelling, Elsevier, vol. 80(C), pages 383-391.
    • Handle: RePEc:eee:ecmode:v:80:y:2019:i:c:p:383-391
    DOI: 10.1016/j.econmod.2018.11.022
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0264999318305662
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econmod.2018.11.022?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 look for a different version below or

    for a different version of it.

    Other versions of this item:

    Citations

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


    Cited by:

    1. Zhang, Jinyu & Zhang, Qiaosen & Li, Yong & Wang, Qianchao, 2023. "Sequential Bayesian inference for agent-based models with application to the Chinese business cycle," Economic Modelling, Elsevier, vol. 126(C).
    2. Tubbenhauer, Tobias & Fieberg, Christian & Poddig, Thorsten, 2021. "Multi-agent-based VaR forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 131(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    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:eee:ecmode:v:80:y:2019:i:c:p:383-391. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30411 .

    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.