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risk management financial

Questions and Answers of Risk Management Financial

17. The theoretical price of a futures contract is equal to the cash or spot price plus the cost of carry.
16. The municipal bond futures contract is based on the value of the Bond Buyer Index.
15. The underlying instrument for a swap futures contract is the notional price of the fixed-rate side of a 10-year interest rate swap that has a notional principal equal to $100,000 and that
14. The underlying instrument for an 10-year Agency note futures contract is a Freddie Mac Reference Note or a Fannie Mae Benchmark Note having a par value of $100,000 and a notional coupon of 6%.
13. The 2-year, 5-year, and 10-year Treasury note futures contracts are modeled after the Treasury bond futures contract.
12. The cheapest-to-deliver issue is the issue in the deliverable basket that has the largest implied repo rate.
11. The short in a Treasury bond futures contract has several delivery options: quality option (or swap option), timing option, and wild card option.
10. Conversion factors are used to adjust the invoice price of a Treasury bond futures contract to make delivery equitable to both parties.
9. For the Treasury bond futures contract, the underlying instrument is $100,000 par value of a hypothetical 20-year, 6% coupon Treasury bond.
8. The Federal Funds futures contract is a cash settlement contract whose underlying is the average overnight federal funds for the delivery month.
7. The Eurodollar CD futures contract is a cash settlement contract whose underlying is a 3-month Eurodollar CD and is one of the most heavily traded futures contracts in the world.
6. The Treasury bill futures contract is based on a 3-month (13 week) Treasury bill with a face value of $1 million.
5. An investor who takes a long futures position realizes a gain when the futures price increases; an investor who takes a short futures position realizes a loss when the futures price decreases.
4. The parties to a futures contract are required to satisfy margin requirements.
3. A futures contract is an agreement between a buyer (seller) and an established exchange or its clearinghouse in which the buyer(seller) agrees to take (make) delivery of something at a specified
2. Parties to a forward contract are exposed to counterparty risk which is the risk that the counterparty will not satisfy its contractual obligations.
1. A forward contract is an agreement for the future delivery of something at a specified price at the end of a designated period of time but differs from a futures contract in that it is
7. Describe forward rate agreements.
6. Explain the complications in extending the standard arbitrage pricing model to the valuation of several currently traded interest rate futures contracts.
5. Demonstrate how the theoretical price of a futures/forward contract is determined.
4. Explain the pricing of forward rate agreements.
3. Eescribe the most popular interest rate futures contracts.
2. Explain the risk and return characteristics of futures/forward contracts.
1. Explain the basic features of interest rate futures and forward contracts.
13. Stress testing is the process whereby a series of simulations is carried out to investigate the impact of changing assumptions on the calculated VaR.
12. Mapping is the procedure used by Bloomberg and Riskmetrics to calculate the VaR of any security by viewing it a portfolio of one or more primitive assets.
11. The confidence interval specifies the probability of loss.
10. The time horizon is the specified time period for which VaR is calculated.
9. There are several issues in implementing VaR: time horizon, confidence intervals, mapping , and stress testing.
8. The Monte Carlo simulation method generates a large number of randomly generated simulations for possible returns in the future using volatility and correlation estimates chosen by the user.
7. The historical simulation method for calculating VaR permits nonnormal distributions of risk factor returns.
6. The variance-covariance method is less effective when return distributions have “fat tails” and assets have nonlinear payoffs.
5. The diversifed VaR takes into account the correlation between the assets in the portfolio.
4. The undiversified VaR of a portfolio is the sum of the individual asset VaRs.
3. The variance-covariance method assumes that the returns on risk factors are normally distributed, the correlations between risk factors are constant and the delta of each security is constant.
2. There are three different methods for calculating VaR: variancecovariance, historical simulation, and Monte Carlo simulation.
1. Value-at-risk (VaR) is a statistical measure of the potential risk exposure of a portfolio of assets.
6. Discuss the issues in implementing VaR.
5. Illustrate the use of VaR to measure the interest rate risk of a porfolio of fixed-income securities using the variance-covariance method.
4. Calculate the VaR of a single fixed-income security using the variancecovariance method.
3. Discuss the three different approaches to measuring VaR: variance-covariance;historical simulation; Monte Carlo simulation.
2. Define value-at-risk (VaR).
1. Introduce the concept of value-at-risk as a measurement tool for risk.
14. Generalized autoregressive conditional heteroscedasticity (GARCH)models can be used to capture the time series characteristic of yield volatility in which a period of high volatility is followed
13. A forecasted volatility can be obtained by assigning greater weight to more recent observations such that the forecasted volatility reacts faster to a recent major market movement and declines
12. The simplest method for forecasting volatility is weighting all observations equally.
11. In forecasting volatility, it is more appropriate to use an expectation of zero for the mean value.
10. Implied volatility depends on the option pricing model employed as well as features of the option itself.
9. Implied volatility can also be used to estimate yield volatility.
8. Assuming that the yield volatility is approximately normally distributed, the annual standard deviation can be used to construct a confidence interval for the yield one year from now.
7. Yield volatility varies considerably over time.
6. Typically, 250 days, 260 days, or 365 days are used to annualize the daily standard deviation.
5. A daily standard deviation is annualized by multiplying it by the square root of the number of days in a year.
4. The selection of the number of observations and the time period can have a significant effect on the calculated daily standard deviation.
3. The observation used in the calculation of the standard deviation is the natural logarithm of the percentage change in yield between two dates.
2. Yield volatility can be estimated from daily yield observations.
1. The standard deviation is commonly used as a measure of volatility.
5. Describe the different approaches for forecasting volatility.
4. Explain what implied volatility is.
3. Explain the different ways the daily standard deviation can be annualized.
2. Demonstrate how the daily standard deviation is affected by the number of observations used in the calculation and the time period selected.
1. Explain how the standard deviation is estimated from historical yield data.
20. The coefficient of determination between two random variables is equal to the square of the correlation coefficient.
19. The coefficient of determination indicates the percentage of the variation in the dependent variable explained by the explanatory variable or variables.
18. The coefficient of determination can take on a value between 0 and 1.
17. The coefficient of determination, or R-squared, is a measure of how good the relationship is between the dependent variable and the explanatory variables.
16. The procedure for estimating the parameters of a regression is the method of least squares.
15. In a multiple linear regression, there is more than one explanatory variable.
14. In a simple linear regression, there is one dependent variable and one explanatory variable.
13. In regression analysis, one random variable is assumed to be affected by one or more other random variables.
12. Regression analysis is a statistical technique that can be used to estimate relationships between variables.
11. Hedging involves identifying one or more instruments that have a correlation close to −1 with the position that the manager seeks to protect, and selecting the appropriate amount of the hedging
10. The correlation is important in selecting hedging instruments.
9. For a manager to measure the risk of a portfolio, it is critical to have a good estimate of the correlation of returns between each pair of assets in the portfolio.
8. The variance of a portfolio’s return is reduced the lower the correlation, with the maximum reduction when the correlation is −1.
7. The variance of a portfolio’s return depends not only on the variance of the assets, but also upon the correlation between the assets.
6. The variance of a portfolio’s return is not simply the weighted average of the variance of the return of the component assets.
5. The covariance is related to the correlation, being the product of the standard deviation of the random variables and their correlation.
4. A negative value for the correlation coefficient means that the two random variables tend to move in the opposite direction and are said to be negatively correlated.
3. A positive value for the correlation coefficient means that the two random variables tend to move together and are said to be positively correlated.
2. The correlation coefficient can have a value between −1 and 1.
1. The correlation coefficient measures the association between two random variables with no cause and effect assumed.
6. Explain what the coefficient of determination of a regression measures.
5. Explain what regression analysis is and how to estimate a regression.
4. Explain the role of correlation in selecting hedging instruments.
3. Describe how the variance of the return of a portfolio of assets is calculated and the important role that the correlation plays.
2. Describe what the covariance is and its relationship to the correlation coefficient.
1. Describe what is meant by the correlation coefficient between two random variables and how it is calculated.
19. For complex bonds and bond positions, Monte Carlo simulation can be used to obtain a probability distribution.
18. A confidence interval gives a range for possible values of a random variable and a probability associated with that range.
17. There is only a small positive serial correlation for government bond returns.
16. Serial correlation or autocorrelation is the correlation between returns over time.
15. A positively skewed distribution is one in which there is a long tail to the right; a negatively skewed distribution is one in which there is a long tail to the left.
14. A skewed distribution is a probability distribution that is not symmetric around the expected value.
13. There are statistical tests that can be used to determine whether a historical distribution can be characterized as a normal distribution.
12. In order to apply the normal distribution to make statements about probabilities, it is necessary to assess whether a historical distribution is properly characterized as normally distributed.
11. If a random variable follows a normal distribution then the expected value and the standard deviation are the only two parameters that are needed to make statements about the probability of
10. The area under the normal distribution or normal curve between any two points on the horizontal axis is the probability of obtaining a value between those two values.
9. A normal distribution is a symmetric probability distribution that is used in many business applications.
8. In jump process, a random variable can realize large movements without taking on interim values.

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