MACROECONOMIC CAUSES OF VOLATILITY IN THE EURO AREA’S AGGREGATE STOCK RETURN
Ahmed, Shaker (2014)
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The purpose of this paper is to determine whether macroeconomic and financial variables Granger cause time varying volatility in aggregate stock return of the Euro Area. Using the daily data from 2005-2013 monthly realized volatility is calculated as the sum of squared daily returns over the respective month for the Euro Stoxx, the Euro Stoxx 50 and the Euro Stoxx Optimized Banks indices. These three indices area used as proxies for the aggregate market, blue chip companies and banking industry, respectively, in the Euro area. The entire sample period is further divided into three sub-sample periods: pre-crash period is from January 2005 to October, 2007, market crash period is from November, 2007 to February, 2009 and post-crash period is from March, 2009 to December, 2013. The sample periods’ selection is motivated to capture the effects of business cycle and the recent financial crisis of 2007-2009. Nine macroeconomic and financial variables used in this paper are: bank leverage, consumption growth, credit growth, commercial paper to treasury spread (CP), expected GDP growth, GDP growth, term spread, volatility of inflation and industrial production. The In-sample analysis shows that the forecastability of macro variables varies through time and business cycle. Their predictability is higher during the crisis of 2007-2009 and when the bull or the bear market condition is considered in isolation. The pattern of Granger casualty during the bull market differs from that of the bear market. The blue chip index is found to be more sensitive to the changes in macro variables than the broad market index. However, the set of macro variables affecting the banking sector and their predictability pattern are different from the other two indices those represent the overall market. The most successful out-of-sample forecasting approaches involve simple combinations of macro variables, namely median and trimmed mean of individual forecasting variables