We investigate the potential of structural changes and long memory properties in returns and volatility of the two major precious stock markets (SP500 and CAC40). Broadly speaking, a random variable is said to exhibit long memory behavior if its autocorrelation function is not integrable, while structural changes can induce sudden and significant shifts in the time-series behavior of that variable. The results from implementing several parametric and semi-parametric methods indicate strong evidence of long range dependence in the daily conditional return and volatility processes for the stock markets. Despite the divergence of the economic situation and the geographical positions of the countries making up our sample, the FIGARCH and FIEGARCH models mainly turn out to be the most accurate models for predicting the volatility of the stock market.
Keywords: : stock markets, fiegarch, forecasts
