volatility clustering

volatility clustering (countable and uncountable, plural volatility clusterings)

1. (finance, quantitative analysis) A phenomenon of great swings in an index being adjacent to others instead of their having random distribution.

   Meronym: volatility cluster

   • 2018 August 11, Kirkpinar Ayşegül, Evrim Mandaci Pınar, “A volatility spillover analysis between bond and commodity markets as an indicator for global liquidity risk”, in Panoeconomicus‎^[1], volume 70, number 1, published 18 February 2022, →DOI, page 82 of 71–100:

     Figure 3 shows the volatility clustering for oil, gold, and the five developing bond market return series in the period between 2008 and 2022. Regarding the magnitude of volatility clustering, China, India, Russia, and especially oil, appear more volatile than other markets and volatility clusters occurred around 2008-2010 because of worldwide economic instability. The reason for the volatility clusterings in 2008-2009 in Figure 3, was the crisis precipitated by the collapse of subprime mortgages in the U.S. in 2008.

   • 2022 July 22, Lavanya Balaji, H. B. Anita & Balaji Ashok Kumar, “Volatility Clustering in Nifty Energy Index Using GARCH Model”, in Intelligent Communication Technologies and Virtual Mobile Networks. Proceedings of ICICV 2022. Conference proceedings (Lecture Notes on Data Engineering and Communications Technologies; 131)‎^[2], Singapore: Springer, →DOI, pages 667–681:

     Both ARCH and GARCH models have proven successful in modelling real data in a variety of applications and can explain volatility clustering phenomena. […] The most commonly used models, namely the autoregressive conditional heteroscedasticity (ARCH) model and its generalisation and the generalised autoregressive conditional heteroscedasticity (GARCH) model, can only capture time-varying volatility, volatility clustering, excess kurtosis, heavy-tailed distributions, and long-memory properties (GARCH).