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Forecasting the Daily Time‐Varying Beta of European Banks During the Crisis Period: Comparison Between GARCH Models and the Kalman Filter

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  • Yuanyuan Zhang
  • Taufiq Choudhry

Abstract

This intention of this paper is to empirically forecast the daily betas of a few European banks by means of four generalized autoregressive conditional heteroscedasticity (GARCH) models and the Kalman filter method during the pre‐global financial crisis period and the crisis period. The four GARCH models employed are BEKK GARCH, DCC GARCH, DCC‐MIDAS GARCH and Gaussian‐copula GARCH. The data consist of daily stock prices from 2001 to 2013 from two large banks each from Austria, Belgium, Greece, Holland, Ireland, Italy, Portugal and Spain. We apply the rolling forecasting method and the model confidence sets (MCS) to compare the daily forecasting ability of the five models during one month of the pre‐crisis (January 2007) and the crisis (January 2013) periods. Based on the MCS results, the BEKK proves the best model in the January 2007 period, and the Kalman filter overly outperforms the other models during the January 2013 period. Results have implications regarding the choice of model during different periods by practitioners and academics. Copyright © 2016 John Wiley & Sons, Ltd.

Suggested Citation

  • Yuanyuan Zhang & Taufiq Choudhry, 2017. "Forecasting the Daily Time‐Varying Beta of European Banks During the Crisis Period: Comparison Between GARCH Models and the Kalman Filter," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(8), pages 956-973, December.
    • Handle: RePEc:wly:jforec:v:36:y:2017:i:8:p:956-973
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    Cited by:

    1. Yousaf, Imran & Yarovaya, Larisa, 2022. "Static and dynamic connectedness between NFTs, Defi and other assets: Portfolio implication," Global Finance Journal, Elsevier, vol. 53(C).
    2. Yousaf, Imran & Arfaoui, Nadia & Gubareva, Mariya, 2024. "Spillovers and hedging effectiveness between oil and US equity sectors: Evidence from the COVID pre- and post-vaccination phases," Research in International Business and Finance, Elsevier, vol. 69(C).
    3. Serdar Neslihanoglu, 2021. "Linearity extensions of the market model: a case of the top 10 cryptocurrency prices during the pre-COVID-19 and COVID-19 periods," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-27, December.
    4. Wang, Ze & Gao, Xiangyun & An, Haizhong & Tang, Renwu & Sun, Qingru, 2020. "Identifying influential energy stocks based on spillover network," International Review of Financial Analysis, Elsevier, vol. 68(C).
    5. Zhang, Weiping & Zhuang, Xintian & Wu, Dongmei, 2020. "Spatial connectedness of volatility spillovers in G20 stock markets: Based on block models analysis," Finance Research Letters, Elsevier, vol. 34(C).
    6. Yousaf, Imran & Yarovaya, Larisa, 2022. "Spillovers between the Islamic gold-backed cryptocurrencies and equity markets during the COVID-19: A sectorial analysis," Pacific-Basin Finance Journal, Elsevier, vol. 71(C).

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