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A liquidity-weighted GARCH model for empirical equity series

This paper develops a new GARCH-family model (named Liquidity-Weighted GARCH or LW-GARCH) for explaining the volatility behaviour of financial time series, with an application on empirical international equity series (consisting both of stock market indices and individual stock returns). The dataset consists of daily stock market indexes and individual stocks closing prices (12 series) from international stock markets, both developed (USA, Japan, Western Europe) and post-transition CEE stock markets and the sample period extends from January 2, 2001 to February 19, 2008. Further, three different time intervals are delineated to comparatively estimate and compare the accuracy of three volatility models in explaining the conditional volatility of the empirical equity series, i.e. GARCH (1, 1), EGARCH (1, 1) and the newly developed LW-GARCH. 
 
We find that the LW- GARCH model generally performs better in explaining conditional volatility than the other two models (higher values for the log-likelihood function and lower values for the BIC information criterion). In addition, we notice that the exogenous factor in the conditional variance equation has a higher explanatory power in the more recent analysis period, therefore the influence of the explanatory factor (the S&P500) has increased in recent years, so that the more recent is the time period used in the estimation, the better the LW-GARCH performs comparatively to other volatility models.