Asymmetric volatility

Asymmetric volatility

Phenomenon that volatility is higher in down markets than in up markets.

Asymmetric Volatility

A situation in which the volatility of a security is higher when the broader market is performing poorly than when it is performing well. Experts disagree on what causes asymmetric volatility, but factors such as leverage and panic are often cited. The fact that asymmetric volatility exists is important to hedging strategies and option pricing models.
References in periodicals archive ?
Also, in the context of the Brazilian stock market, asymmetric volatility models, TARCH and RTARCH, showed to be better than symmetric volatility approaches to indicate the significance of leverage effects on volatility modeling.
We document an asymmetric volatility spillover effect from U.
3) The co-movement between oil and G7 stock markets is typically time varying and exhibits both asymmetric volatility effects and long-memory patterns.
Investor heterogeneity and asymmetric volatility under short-sale constraints: Evidence from Korean fund market*
Asymmetric volatility spillovers in deutsche mark exchange rates.
Asymmetric volatility of stock returns during the Asian crisis: Evidence from Indonesia.
An empirical investigation of asymmetric volatility in real GDP growth rates of Japan, the United Kingdom, the United States and Canada was carried out by (Ho and Tsui, 2003).
Aloui (2007) explores the nature of the mean, volatility and causality transmission mechanism between stock and foreign exchange markets for the united states and some major European markets for the pre and post euro periods with the use of extended multivariate EGARCH model in order to explain asymmetric volatility transmission mechanism.
Despite the success of GARCH model, it has been criticized for failing to capture the asymmetric volatility (Liu & Hung, 2010), since for stock prices, negative shocks to returns generally have large impacts on their volatility than positive shocks.
The relation between asymmetric volatility and return can be evaluated by Threshhold GARCH-in-mean model (TGARCH-M), Exponential GARCH-in-mean model (EGARCH-M) or Power-in-Mean GARCH model (PGARCH-M).
Engle and Ng (1993) propose the sign bias, negative size bias, positive size bias and joint tests in the standardized residuals to determine the response of the asymmetric volatility models to news.
It is interesting that empirical research using robust test statistics that are much more sophisticated than the simple Ljung-Box Q-test procedure, (see Hagerud 1997) has found that relatively few stocks show signs of asymmetric volatility clustering.
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