Brandon Renfro and Rebecca Asante
This paper presents an empirical analysis of the correlation between some demographic and financial predictor variables and the stochastic volatility of the Standard and Poor’s (S&P) 500 index between January 2000 and December 2010 inclusive. In particular, the predictor variables used for the statistical analysis are: prime rate (PR)(t), the United States population proportion between the ages of 40-64 (PP(t)), inflation rate (IR(t)), logarithm of the unemployment rate (log(UE)(t)), and consumer confidence (CC)(t)). The empirical relationship between these variables is established using multiple regression analytic techniques with EXCEL software. The relevance of each predictor variable is assessed by inspection of the P-value of the associated multiple regression coefficient. The plot of the observed and modeled S&P 500 index for the 149 data points (months) corresponding to the period spanning January 2000 and December 2010 elucidates the potential of the empirical model to forecast the volatility of the S&P 500 for the period in question. The constructed empirical multiple regression model for the observed S&P 500 has the configuration:
���¶= -160.313+7331.269*(PR)(t)+4780.536*(PP)(t)+611.035*(IR)(t)
+606.866*log(UE)(t)+1.901*(CC)(t)
The adjusted R2 for the empirical model is approximately 0.49 .This means that during the period 2000-2010, about 49% of the variability of the S&P 500 volatility could be explained by the information accrued from the joint influence of the five predictor variables.
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