Statistically, volatility clustering entails a strong autocorrelation in squared returns. A technical term given to this phenomenon is Autoregressive Conditional Heteroskedasticity (ARCH) or simply the ARCH effect. Abstract. Volatility clustering is a well-known stylized feature of financial asset returns. This paper investigates asymmetric pattern in volatility clustering by employing a univariate copula approach of Chen and Fan (2006). However, there is visual evidence in 16.1 that the series of returns exhibits conditional heteroskedasticity since we observe volatility clustering. For some applications it is useful to measure and forecast these patterns. This can be done using models which assume that the volatility can be described by an autoregressive process. sell a stock or a portfolio before it becomes too volatile. A information in today’s information set such as today’s returns. Volatility is said to be persistent if today’s return has a large Volatility clustering implies that volatility comes and goes. Volatility in a stock has a bad connotation, but many traders and investors seek out higher volatility investments in order to make higher profits. After all, if a stock or other security does not
Volatility in a stock has a bad connotation, but many traders and investors seek out higher volatility investments in order to make higher profits. After all, if a stock or other security does not
9 Apr 2019 The Behavior of Market Volatility. Time series of financial asset returns often demonstrates volatility clustering. In a time series of stock prices, for 5 Apr 2019 Estimations of GARCH(1,1) on stock and index returns. usually yield a+bvery close to 1 . For this reason the volatility clustering. phenomenon Time series of financial asset returns often exhibit the volatility clustering Asset pricing under heterogeneous expectations in an artificial stock market, in: The 16.4 Volatility Clustering and Autoregressive Conditional Heteroskedasticity Wilshire 5000 index reflect daily stock returns which are essentially unpredictable . 9 Nov 2010 This study is an attempt to model the volatility of stock returns in Indian market for the period 1997-2006 using GARCH, TARCH and E-GARCH. 25 Sep 2007 We investigate volatility clustering using a modeling approach based on the temporal aggregation results for generalized autoregressive
However, empirical evidence rejects this assumption. Financial time series such as exchange rates or stock returns exhibit so-called volatility clustering. This
This can make it hard to identify any one Volatility Cluster’s start and end point. How is volatility different from return? Is volatility just a downward move in the market? Volatility is not simply the tendency of a stock index to fall in value. When an index such as the S&P 500 falls, that is simply a negative return. Volatility clustering occurs in most stocks, as well as in other financial instruments and markets; see also [Ghoulmie et al., 2005] and . For stocks, the risk driver is the log-value X t = ln V stock t, as in , and the risk driver increment is the compounded return Δ X t = ln (V stock t ∕ V stock t − 1). This paper employs the Baidu Index as the novel proxy for unexpected information demand and shows that this novel proxy can explain the volatility clustering of Chinese stock returns. Generally speaking, these findings suggest that investors in China could take advantage of the Baidu Index to obtain information and then improve their investment decision.