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Analysis of Asymmetric GARCH Volatility Models with Applications to Margin Measurement

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  • Elena Goldman
  • Xiangjin Shen

Abstract

We explore properties of asymmetric generalized autoregressive conditional heteroscedasticity (GARCH) models in the threshold GARCH (GTARCH) family and propose a more general Spline-GTARCH model, which captures high-frequency return volatility, low-frequency macroeconomic volatility as well as an asymmetric response to past negative news in both autoregressive conditional heteroscedasticity (ARCH) and GARCH terms. Based on maximum likelihood estimation of S&P 500 returns, S&P/TSX returns and Monte Carlo numerical example, we find that the proposed more general asymmetric volatility model has better fit, higher persistence of negative news, higher degree of risk aversion and significant effects of macroeconomic variables on the lowfrequency volatility component. We then apply a variety of volatility models in setting initial margin requirements for a central clearing counterparty (CCP). Finally, we show how to mitigate procyclicality of initial margins using a three-regime threshold autoregressive model.

Suggested Citation

  • Elena Goldman & Xiangjin Shen, 2018. "Analysis of Asymmetric GARCH Volatility Models with Applications to Margin Measurement," Staff Working Papers 18-21, Bank of Canada.
    • Handle: RePEc:bca:bocawp:18-21
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    References listed on IDEAS

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    Cited by:

    1. Dávid Zoltán Szabó & Kata Váradi, 2022. "Margin requirements based on a stochastic correlation model," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(10), pages 1797-1820, October.
    2. Lorraine Muguto & Paul-Francois Muzindutsi, 2022. "A Comparative Analysis of the Nature of Stock Return Volatility in BRICS and G7 Markets," JRFM, MDPI, vol. 15(2), pages 1-27, February.

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    More about this item

    Keywords

    Econometric and statistical models; Payment clearing and settlement systems;

    JEL classification:

    • E41 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Demand for Money
    • 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
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation

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