6. Conclusion

We provide descriptive statistics and graphics which show that monthly excess returns data for a mutual fund reveals that their distribution is nonnormal. Since the SN distribution has been generally ignored by financial economists, we consider its potential in describing . Using mathStatica, [2], we derive the score function for the parameters , , and of a location-scale SN density and their ML estimators. We note that mathStatica and Mathematica provide tools for numerous potential uses in finance, including ML estimation of parameters of several nonnormal . We show that these densities can be further used to compute low percentiles (1%) to help compute the VaR, which is gaining considerable use by Wall Street practitioners and the financial media [4].