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Test for tail index constancy of GARCH innovations based on conditional volatility

Author

Listed:
  • Moosup Kim

    (APEC Climate Center)

  • Sangyeol Lee

    (Seoul National University)

Abstract

This study considers the problem of testing whether the tail index of the GARCH innovations undergoes a change according to the values of conditional volatilities. Special attention is paid to power-transformed and threshold generalized autoregressive conditional heteroscedasticity processes that can accommodate the GARCH family. We show that the proposed test asymptotically follows a functional of a standard Brownian motion under some regularity conditions. To evaluate our method, we carry out a simulation study and real data analysis using the return series of the Google stock price and DowJones index.

Suggested Citation

  • Moosup Kim & Sangyeol Lee, 2019. "Test for tail index constancy of GARCH innovations based on conditional volatility," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(4), pages 947-981, August.
    • Handle: RePEc:spr:aistmt:v:71:y:2019:i:4:d:10.1007_s10463-018-0669-6
    DOI: 10.1007/s10463-018-0669-6
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    References listed on IDEAS

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    Cited by:

    1. Ananda Chatterjee & Hrisav Bhowmick & Jaydip Sen, 2022. "Stock Volatility Prediction using Time Series and Deep Learning Approach," Papers 2210.02126, arXiv.org.
    2. Jaydip Sen & Sidra Mehtab & Abhishek Dutta, 2021. "Volatility Modeling of Stocks from Selected Sectors of the Indian Economy Using GARCH," Papers 2105.13898, arXiv.org.

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