Long Memory Volatility Models in R: Application to a Regional Blue Chips Index

Abstract

An increasing number of researchers and analysts have formulated in the last decade innovative statistical equations for modelling and forecasting the volatility of financial returns. The evolution of technology and new software development have contributed positively to emerging economies, as well as in the improvement of market competition. We investigate in this study some experimental extensions of the multiplicative error model, which has been introduced by Engle (2002) for positive valued processes and it is specified as the product of a conditionally autoregressive scale factor and an innovation process with positive support. We use R 3.1.3  for the optimization, a modern software for statistical computing and graphics. Empirical analysis is carried out starting from May 2009 to January 2015. The equations fit fairly well the volatility of ‘STOXX Balkan 50 Equal Weight’ Index, which represents leading blue chips from eight Balkan countries in terms of free-float market cap. The models seem to absorb completely the slow decay of the global autocorrelation function.