Modeling volatility in sector index returns with GARCH models using an iterated algorithm

Financial market participants are interested in knowing what events can alter the volatility pattern of financial assets and how unanticipated shocks determine the persistence of volatility over time. The present paper studies these issues by detecting time periods of sudden changes in volatility by using the iterated cumulated sums of squares (ICSS) algorithm. Examining five major sectors from January 1992 to August 2003, we found that accounting for volatility shifts in the standard GARCH model considerably reduces the estimated volatility persistence. Our results have important implications regarding asset pricing, risk management, and portfolio selection.