Abstract
The issue of volatility clustering i.e., if periods of high volatility on stocks returns are typically followed by other periods of high volatility and vice versa, is analysed in this article at a sector level for the Chinese stock market. This analysis was performed with daily returns for the period from 2008 to 2017. When the entire dataset is analysed the statistical tests are rather consistent indicating that there is volatility clustering for all the major nine sectors (basic materials, communications, consumer cyclical, consumer non-cyclical, energy financial, industrial, technology and utilities). However, when each year is analysed independently the results are much more mixed with some sectors, such as technology companies, that could a priori look as a prime candidate for volatility clustering having less years with such feature present that other sectors such as for instance basic materials. The issue of volatility clustering at a sector level is of clear interest and can be used as another tool to optimize portfolio allocations. It is interesting to see that volatility clustering seems to be present when the statistical tests are performed over long periods of time but less so when the timeframe is shortened.
Highlights
The volatility clustering concept at its core is relatively simple
The idea is that big volatility in returns is typically followed by additional big volatility while small volatility in returns is followed by small volatility in returns in what could be described as some type of inertia in the market. (Mandelbrot, 1963) is one of the first and more frequently quoted papers in the topic of volatility clustering of assets returns
Volatility clustering was detected in the basic materials sector for six of the ten years studied using the previously mentioned Ljung-Box test
Summary
The volatility clustering concept at its core is relatively simple. (Mandelbrot, 1963) is one of the first and more frequently quoted papers in the topic of volatility clustering of assets returns. In that paper the author developed a solid mathematical foundation for understanding this issue moving away from the assumption of the returns following a normal distribution. Volatility clustering has been found in many asset returns such as foreign exchange (Andersen and Bollerslev, 1997), real estate (Pinte and Fuerst, 2014) as well as stock returns. The basic concept is rather simple and could be expressed as the idea that periods of high volatility tend to be followed by periods of high volatility and vice versa.
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