Volatility clustering

From Wikipedia, the free encyclopedia
Jump to navigation Jump to search

In finance, volatility clustering refers to the observation, first noted as Mandelbrot (1963), that "large changes tend to be followed by large changes, of either sign, and small changes tend to be followed by small changes."[1] A quantitative manifestation of this fact is that, while returns themselves are uncorrelated, absolute returns or their squares display a positive, significant and slowly decaying autocorrelation function: corr(|rt|, |rt+τ |) > 0 for τ ranging from a few minutes to several weeks. This empirical property has been documented in the 90's by Granger and Ding (1993) [2] and Ding and Granger (1996) [3] among others; see also [4]. Some studies point further to long-range dependence in volatility time series, see Ding, Granger and Engle (1993) [5] and Barndorff-Nielsen and Shephard[6].

Observations of this type in financial time series go against simple random walk models and have led to the use of GARCH models and mean-reverting stochastic volatility models in financial forecasting and derivatives pricing. The ARCH (Engle, 1982) and GARCH (Bollerslev, 1986) models aim to more accurately describe the phenomenon of volatility clustering and related effects such as kurtosis. The main idea behind these two models is that volatility is dependent upon past realizations of the asset process and related volatility process. This is a more precise formulation of the intuition that asset volatility tends to revert to some mean rather than remaining constant or moving in monotonic fashion over time.


See also[edit]

References[edit]

  1. ^ Mandelbrot, B. B., The Variation of Certain Speculative Prices, The Journal of Business 36, No. 4, (1963), 394-419
  2. ^ Granger, C.W. J., Ding, Z. Some Properties of Absolute Return: An Alternative Measure of Risk , Annales d'Économie et de Statistique, No. 40 (Oct. - Dec., 1995), pp. 67-91
  3. ^ Ding, Z., Granger, C.W.J. Modeling volatility persistence of speculative returns: A new approach, Journal of Econometrics), 1996, vol. 73, issue 1, 185-215
  4. ^ Cont, Rama (2007). "Volatility Clustering in Financial Markets: Empirical Facts and Agent-Based Models". In Teyssière, Gilles; Kirman, Alan. Long Memory in Economics. Springer. pp. 289–309. doi:10.1007/978-3-540-34625-8_10.
  5. ^ Zhuanxin Ding, Clive W.J. Granger, Robert F. Engle (1993) A long memory property of stock market returns and a new model, Journal of Empirical Finance, Volume 1, Issue 1, 1993, Pages 83-106
  6. ^ Ole E. Barndorff-Nielsen, Neil Shephard (October 2010). "Volatility". In Cont, Rama. Encyclopedia of Quantitative Finance. Wiley. doi:10.1002/9780470061602.eqf19019.