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Time Dependency in Returns, Time Variability in Returns, Skewness-Kurtosis Boundary, Various Distributions, Financial Assets Returns, Conditional Models, Riskmetrics Method. Above points are representatives for questions of Quantitative Methods in Finance given in this past exam paper.
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Exam Code(s) 1MIF Exam(s) M.Econ.Sc. (International Finance) Module Code(s) EC Module(s) Quantitative Methods in Finance Paper No. 1 Repeat Paper External Examiner(s) Professor Cillian Ryan Internal Examiner(s) Professor Eamon O’Shea Dr. Srinivas Raghavendra Instructions: Answer any four questions. Each question carries 25 marks. Duration 3 hrs. No. of Pages 2 Department(s) ECONOMICS Course Co-ordinator(s) Srinivas Raghavendra Requirements : MCQ Handout Statistical Tables Graph Paper Log Graph Paper Other Material
1. Discuss the methods developed to test the time-dependency in returns and volatility. (25 marks) 2. Describe, compare and contrast the Jarque-Bera test statistic and the Quantile (q-q) plot as tests of normality in financial asset returns (25 marks) 3. (i) Discuss the characteristics of time variability in returns, volatility, skewness and kurtosis. (ii) Describe the tests to be used for estimating time variability in returns and volatility. (iii) Comment on the concept of the general Skewness-Kurtosis boundary by which various distributions with higher moments are classified. (25 marks) 4. (i) Describe and distinguish between an ARCH (p) process and a GARCH (p,q) process (ii) Explain and assess the appropriateness of using GARCH for forecasting risk in financial assets’ returns. (25 marks) 5. (i) Justify the need to compute the tail index (ζ) when estimating Value at Risk. (ii) Discuss the parametric and semi-parametric estimation methods of the tail index. (25 marks) 6. (i) Describe and distinguish between the conditional and unconditional models in extreme value theory. (ii) Evaluate Morgan Stanley’s RiskMetrics Method of risk estimation. (25 marks)