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    政大機構典藏 > 商學院 > 統計學系 > 專書/專書篇章 >  Item 140.119/111671
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/111671


    Title: A vg-ngarch model for impacts of extreme events on stock returns
    Authors: 陳麗霞
    Kao, Lie-Jane
    Chen, Li-Shya
    Lee, Cheng-Few
    Contributors: 統計系
    Date: 2015-01
    Issue Date: 2017-08-08 16:40:37 (UTC+8)
    Abstract: This article compares two types of GARCH models, namely, the VG-NGARCH and the GARCH-jump model with autoregressive conditional jump intensity, i.e., the GARJI model, to make inferences on the log of stock returns when there are irregular substantial price fluctuations. The VG-NGARCH model imposes a nonlinear asymmetric structure on the conditional shape parameters in a variance-gamma process, which describes the arrival rates for news with different degrees of influence on price movements and provides an ex ante probability for the occurrence of large price movements. On the other hand, the GARJI model, a mixed GARCH-jump model proposed by Chan and Maheu (Journal of Business & Economic Statistics 20:377-389, 2002), adopts two independent autoregressive processes to model the variances corresponding to moderate and large price movements, respectively. An empirical study using daily stock prices of four major banks, namely, Bank of America, J.P. Morgan Chase, Citigroup, and Wells Fargo, from 2006 to 2009 is performed to compare the two models. The goodness of fit of the VG-NGARCH model vs. the GARJI model is demonstrated. © Springer Science+Business Media New York 2015.
    Relation: Handbook of Financial Econometrics and Statistics, 2263-2279
    Data Type: book/chapter
    DOI 連結: http://dx.doi.org/10.1007/978-1-4614-7750-1_82
    DOI: 10.1007/978-1-4614-7750-1_82
    Appears in Collections:[統計學系] 專書/專書篇章

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