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principles of risk management

Questions and Answers of Principles Of Risk Management

Outline the major types of financial futures contracts.
What is the underlying idea behind the creation of financial futures?
Please access the Internet (world wide web) and try to investigate the following:(a) the number of futures and options exchanges around the world;(b) the number of financial futures and options
Explain the rationale behind current accounting rules applied to financial derivatives.
Explain the major difference between exchange-traded contracts and overthe-counter derivatives.
Outline different motivations for financial innovation in the form of derivative instruments.
Explain the rationale behind the introduction of risk-linked securities and contingent capital instruments. Who benefits from these techniques?
Explain how risk exposures are managed through conventional risk-transfer arrangements in the insurance and reinsurance markets.
Explain the rationales behind the introduction of the loan sales, asset trading, and asset securitization techniques. Who benefits from these techniques?
Outline three major types of derivative instruments that are commonly observed in financial markets.
Explain how uncertainties around the creation of financial assets and investment in commercial assets may affect economic growth.
Explain the relationship between the creation of financial assets and investment in commercial assets in the credit intermediation process.
Explain how the risk management process should be structured.
What are some essential limitations of value-at-risk (VaR)?
How can value-at-risk (VaR) be determined?
Explain the concept value-at-risk (VaR) and its potential usefulness.
How can portfolio theory be applied in risk management?
Explain the concept of volatility and how it may be used.
How is the concept of risk conceived in:(a) financial theory?(b) financial institutions?(c) multinational corporations?
Outline major types of risk and discuss the relationships between them.
Explain the relationship between interest rate and currency risk.
What are some essential limitations of the duration measure?
What is the difference between duration and modified duration and what is its significance?
Explain the concept of duration and what it can be used for.
Explain why currency exposures can arise and how they can be monitored and managed.
Explain how interest rate exposures can arise and how they can be monitored and managed.
Explain how the forward foreign exchange rates are determined.
Explain how the foreign exchange market works.
Briefly discuss how we may determine the appropriate credit-risk spread(compared to the risk-free T-Note rate) on a corporate financial asset.
Explain how to calculate yield, zero-coupon, and forward rates.
Explain the difference between yield-to-maturity, zero-coupon rates, and forward rates.
What is the role of credit markets and financial institutions in the global economy.
Briefly outline the macro economic relationship in an open economy.
Briefly outline and explain the relationships between monetary flows in the domestic economy and the international financial markets.
Explain the relationship between a country’s current accounts, the capital accounts, and the reserve position.
Explain the relationship between a country’s trade balance, current accounts, and capital accounts.
Explain how a country can finance a trade balance deficit.
5. Using the RiskMetrics variances calculated in exercise 1, compute the uniform transform variable. Plot the histogram of the uniform variable. Does it look flat?
3. For the 1% and 5% value at risk, calculate the indicator “hit” sequence for both RiskMetrics and Historical Simulation models. The hit sequence takes on the value 1 if the return is below the
2. Compute the 1% and 5% 1-day Value-at-Risk for each day using RiskMetrics and Historical Simulation with 500 observations.
1. Compute the daily variance of the returns on the S&P 500 using the RiskMetrics approach.
5. Replicate Figure 12.6. How does the loss rate distribution change when the correlation changes?
4. Assume a portfolio with 100 small loans. The average default rate is 3% per year and the asset correlation of the firms underlying the loans is 5%. The recovery rate is 40%. Compute the $VaR and
3. Assume that a company has 100,000 shares outstanding and that the stock is trading at $8 with a stock price volatility of 50% per year. The company has $500,000 in debt (face value)that matures in
2. Consider a company with assets worth $100 and a face value of debt of $80. The log return on government debt is 3%. If the company debt expires in two years and the asset volatility is 80% per
1. Use the AR(1) model from Chapter 3 to model the default rate in Figure 12.2. What does the AR(1) model reveal regarding the persistence of the default rate? Can you fit the default rate closely?
4. Replicate Figure 11.9.
3. Assume a short position of one option contract with 43 days to maturity and a strike price of 1135. Using the preceding 5;000 random normal numbers, calculate the changes in the 10-day portfolio
2. Assume a portfolio that consists of a short position of one in each of the option contracts.Calculate the 10-day, 1% dollar VaRs using the delta-based and the gamma-based models.Assume a normal
1. Assume a volatility of 0:015 per calendar day for option pricing and a volatility of 0:015 p 365=252 D 0:0181 per trading day for return volatility. Calculate the delta and gamma of a short
7. Calculate the square root of the mean squared pricing error from the IVF and MIVF models and compare them to the square root of the MSE from the standard BSM model and the Gram-Charlier model.
4. Fit the Gram-Charlier option price to the data. Estimate a model with skewness only. Use nonlinear test squares (NLS).
1. Calculate the BSM price for each option using a standard deviation of 0.01 per day. Using Solver, find the volatility that minimizes the mean squared pricing error using 0.01 as a starting value.
4. Simulate 10,000 sets of returns from the model in exercise 3. Compute the 1% VaR and ES from the model.
3. Estimate a normal copula model on the S&P 500 and 10-year bond return data. Assume that the marginal distributions have RiskMetrics volatility with symmetric t shocks. Estimate the d parameter for
2. Simulate 10,000 data points from a bivariate normal distribution to replicate the thresholds in Figure 9.3.
1. Replicate the threshold correlations in Figures 9.1 and 9.2. Use a grid of thresholds from 0.15 to 0.85 in increments of 0.01.
4. Using the DCC model estimated in Chapter 7 try to replicate the correlation forecasts in Figure 8.5, using 10,000 Monte Carlo simulations. Compared with Figure 8.5 do you find evidence of Monte
3. Repeat exercise 1 computing ES rather than VaR.
2. Consider counterfactual scenarios where the volatility on the last day of the sample was three times its actual value and also one-half its actual value. Recompute the 10-day VaR in exercise 1.
1. Construct the 10-day, 1% VaR on the last day of the sample using FHS (with 10,000 simulations), RiskMetrics scaling the daily VaRs by p10 (although it is incorrect), and Monte Carlo simulations of
6. Estimate the GARCH DCC model for the two assets. Set the starting values to D 0:05 and D 0:9: Plot the dynamic correlations. Calculate and plot the 1-day, 1% VaR.
5. Use QMLE to estimate in the exponential smoother version of the dynamic conditional correlation (DCC) model for two assets. Set the starting value of to 0.94. Calculate the 1-day, 1% VaR.
4. Estimate an NGARCH(1,1) model for the two assets. Standardize each return using its GARCH standard deviation.
3. Calculate the unconditional 1-day, 1% Value-at-Risk for a portfolio consisting of 50% in each asset. Calculate also the 1-day, 1% Value-at-Risk for each asset individually. Use the normal
2. Compute the unconditional covariance and the correlation for the two assets.
1. Calculate daily log returns and plot them on the same scale. How different is the magnitude of variations across the two assets?
9. Use the asymmetric t distribution to construct Figure 6.5.
8. Use the asymmetric t distribution to construct Figure 6.4.
7. For each day in 2010, calculate the 1-day, 1% VaRs using the following methods: (a) Risk-Metrics, that is, normal distribution with an exponential smoother on variance using the weight D 0:94;
6. Construct the QQ plot using the EVT distribution for the 50 largest losses. Compare your result with Figure 6.7.
5. Estimate the EVT model on the standardized portfolio returns using the Hill estimator. Use the 50 largest losses to estimate EVT. Calculate the 0.01% standardized return quantile implied by each
4. Using QMLE, estimate the NGARCH(1,1)-Qt.d/ model. Fix the variance parameters at their values from exercise 3. Set the starting value of d equal to 10. (Excel hint: Use the GAMMALN function for
3. Standardize the returns using the volatilities from exercise 2. Construct a QQ plot for the standardized returns using the normal distribution. Compare your result with the bottom panel of Figure
2. Copy and paste the estimated NGARCH(1,1) volatilities from Chapter 4.
1. Construct a QQ plot of the S&P 500 returns divided by the unconditional standard deviation.Use the normal distribution. Compare your result with the top panel of Figure 6.2. (Excel hint: Use the
5. Estimate a HAR model in logarithms on the RV data. Use the next day’s RV on the lefthand side and use daily, weekly, and monthly regressors on the right-hand side. Compare the regression fit
4. Estimate a HAR model in logarithms on the RP data you constructed in exercise 2. Use the next day’s RP on the left-hand side and use daily, weekly, and monthly regressors on the right-hand side.
3. Run a regression using RV instead of the squared returns as proxies for observed variance;that is, regress RVtC1 D b0 Cb12t C1 CetC1 Is the constant term significantly different from zero? Is the
2. Run a regression using RP instead of the squared returns as proxies for observed variance;that is, regress RPtC1 D b0 Cb12t C1 CetC1, where RPtC1 D 14ln.2/D2t C1 Is the constant term
1. Run a regression of daily squared returns on the variance forecast from the GARCH model with a leverage term from Chapter 4. Include a constant term in the regression R2t C1 D b0 Cb12t C1
4. Estimate the component GARCH model defined by2t C1 D vtC1 CR2t????vtC2t????vtvtC1 D 2 CvR2t????2tCvvt ????2
3. Include the option implied volatility VIX series from the Chicago Board Options Exchange(CBOE) as an explanatory variable in the GARCH equation. Use MLE to estimate2t C1 D !C .Rt ????t/2 C2t
2. Include a leverage effect in the variance equation. Estimate2t C1 D !C .Rt ????t/2 C2t; with Rt D tzt; and zt N.0;1/Set starting values to D 0:07; D 0:85; ! D 0:000005; and D 0:5. What
1. Estimate the simple GARCH(1,1) model on the S&P 500 daily log returns using the maximum likelihood estimation (MLE) technique. First estimate2t C1 D !CR2t C2t; with Rt D tzt; and zt
5. Using the data set in the worksheet named Question 3.4, estimate an MA(1) model using maximum likelihood. Use the starting values suggested in the text. Use Solver in Excel to maximize the
4. Using the data sets in the worksheet named Question 3.4, estimate an AR(1) model on each of the 100 columns of data. (Excel hint: Use the LINEST function.) Plot the histogram of the 100 1
3. Reproduce Figure 3.2.
2. Reproduce Figure 3.1.
1. Using the data in the worksheet named Question 3.1 reproduce the moments and regression coefficients at the bottom of Table 3.1.
4. Reconstruct the P/Ls in Figure 2.6.
3. For each day from July 1, 2008 through December 31, 2009, calculate the 10-day, 1% VaRs using the following methods: (a) RiskMetrics, that is, normal distribution with an exponential smoother on
2. Assume you are long $1 of the S&P 500 index on each day. Calculate the 1-day, 1% VaRs on each day in October 1987 using Weighted Historical Simulation. You can ignore the linear interpolation part
1. Assume you are long $1 of the S&P 500 index on each day. Calculate the 1-day, 1% VaRs on each day in October 1987 using Historical Simulation. Use a 250-day moving window.Plot the VaR and the
8. Calculate the 1-day, 1% VaR on each day in the sample using the sequence of variances 2t C1 and the standard normal distribution assumption for the shock ztC1.
6. Compute standardized returns as zt D Rt= t and calculate the mean, standard deviation, skewness, and kurtosis of the standardized returns. Compare them with those found in exercise 2.
5. Set 20(i.e., the variance of the first observation) equal to the variance of the entire sequence of returns (you can square the standard deviation found earlier). Then calculate2t C1 D 0:942t
4. Calculate the first through 100th lag autocorrelation of squared returns. Again, plot the autocorrelations against the lag order. Compare your result with Figure 1.3.
3. Calculate the first through 100th lag autocorrelation. Plot the autocorrelations against the lag order. (Excel hint: Use the function CORREL.) Compare your result with Figure 1.1.
2. Calculate the mean, standard deviation, skewness, and kurtosis of returns. Plot a histogram of the returns with the normal distribution imposed as well. (Excel hints: You can either use the

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