# Exercise Solution 14.9

- b. Given the assumption that
is conditionally normal with^{t}L^{t}^{–1}*E*() = 0, and knowing that the the .99-quantile of a normal distribution occurs 2.326 standard deviations above the mean, we can infer from the value-at-risk values the conditional standard deviations of the^{t}Lassumed by those value-at-risk values—and hence the conditional normal distributions. With [14.9], recalculate the quantiles^{t}Lat which losses^{t}uoccur. With [14.10], we convert these to values^{t}lSee spreadsheet.^{t}n.

- d. e. We arrange the
in ascending order to form the^{t}n*n*. We calculate values as describe in Section 14.4.2. Plotting the against the_{j}*n*, we obtain (see the same spreadsheet)_{j}

The points fall more-or-less near a line passing through the origin, but the fit is poor. Based on just the graph, it cannot be determined whether the poor fit reflects a shortcoming of the VaR measure or merely the limited data.

- We calculate the correlation between the
*n*and as 0.985. Based on Exhibit 14.6, we reject the VaR measure at the .05 significance level._{j}