in this paper, we have examined abrupt changes in volatility of tepix index in tehran stock exchange during august 23, 2010 to june 12, 2014. applying the iterated cumulative sum of squares (icss) algorithm proposed by inclan and tiao (1994) and the modified version of this algorithm consisting kappa 1 and kappa 2 test statistics developed by sansó et al. (2004), we have specified that the dete...

In this study, we have investigated sudden changes in volatility and re-examined the persistence of volatility in Japanese and Korean stock markets during 1986–2008. Using the iterated cumulative sums of squares (ICSS) algorithm, we have determined that the identification of sudden changes is generally associated with global financial and political events. We have also demonstrated that control...

Nowadays many researchers use GARCH models to generate volatility forecasts. However, it is well known that volatility persistence, as indicated by the sum of the two parameters G1 and A1[1], in GARCH models is usually too high. Since volatility forecasts in GARCH models are based on these two parameters, this may lead to poor volatility forecasts. It has long been argued that this high persist...

This article studies U.S. monthly inflation, inflation growth, and price level dynamics from January 1967 to September 2005. Two rolling samples are constructed to recover evidence about instability in inflation, inflation growth, and price level persistence and volatility. Evidence is presented that changes in inflation, inflation growth, and price level persistence and volatility coincide wit...

This paper explores the mechanism on how the persistence of the stock return volatility is created using a model of an agent-based stock market. First, artificial stock markets with different learning mechanisms, i.e., individual and social learning are examined. The simulation result shows that a social learning economy produces persistence of return volatility while an individual learning eco...

Decreases in stock market returns often lead to higher increases in volatility than increases in returns of the same magnitude, and it is common to incorporate these so-called leverage effects in GARCH and stochastic volatility models. Recent research has also found it useful to account for leverage in models of realized volatility, as well as in models of the continuous and jump components of ...