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Modelling Volatility Clustering

In: Econometrics in Theory and Practice

Author

Listed:
  • Panchanan Das

    (University of Calcutta, Department of Economics)

Abstract

The conventional regression model is linear and centred on the conditional first-order moment. In linear time series econometric models, the variance of the disturbance term is assumed to be constant, or the random disturbance is homoscedastic. The ARMA models are used to estimate conditional expectation of a process given the past information by assuming that the conditional variance is constant. But, in many cases, the error terms may reasonably be larger for some points or ranges of the data than for others, and the data suffer from heteroscedasticity. In the presence of heteroscedasticity, the regression coefficients for an ordinary least-squares regression are still unbiased, but the standard errors and confidence intervals estimated by conventional procedures will be large, giving a false sense of precision. This chapter examines the behaviour of volatility in terms of conditional heteroscedasticity model.

Suggested Citation

  • Panchanan Das, 2026. "Modelling Volatility Clustering," Springer Texts in Business and Economics, in: Econometrics in Theory and Practice, edition 0, chapter 13, pages 493-541, Springer.
    • Handle: RePEc:spr:sptchp:978-981-95-7226-7_13
    DOI: 10.1007/978-981-95-7226-7_13
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