1. If a Normal distribution is assumed for the possible future outcomes, the Value at Risk (VaR) is fairly straightforward and simple to calculate if the mean and standard deviation are known.
   1. True
   2. False

2. A fairly accurate estimate of the Value at Risk (VaR) can be determined by using the generally accepted assumption that daily returns of the stock market as measured by the S&P 500 Index are consistent with a Normal distribution.
   1. True
   2. False

3. The Value at Risk (VaR) tells us nothing about the possible losses worse than the VaR amount.
   1. True
   2. False

4. Three approaches commonly used to calculate Value at Risk (VaR) and Expected Shortfall (ES) are (1) A parametric approach in which a specific distribution is assumed, (2) A non-parametric approach which uses historical data with an assumption that the future will be similar to what has happened in the past, and (3) A Monte Carlo simulation which develops multiple trials to produce a distribution of potential future outcomes.
   1. True
   2. False
5. A non-parametric historical simulation approach for calculating Value at Risk (VaR) can be implemented by sorting historical data for a given period and using the Percentile function in Excel to determine the VaR.
   1. True
   2. False
7. The Exponentially Weighted Moving Average (EWMA) removes autocorrelation by assigning equal weights to historical variances.
   1. True
   2. False
9. A successful GARCH method of modelling variance over time will remove autocorrelation in the squared returns of a data series.
   1. True
   2. False