Docsity
Docsity

Prepare for your exams
Prepare for your exams

Study with the several resources on Docsity


Earn points to download
Earn points to download

Earn points by helping other students or get them with a premium plan


Guidelines and tips
Guidelines and tips

Repeat Examination for EC566: Quantitative Methods in Finance, 2011/2012, Exams of Quantitative Techniques

Information about a repeat examination for the ec566: quantitative methods in finance module, which is part of the m.econ.sc. International finance course. Details about the exam code, module code, paper number, duration, instructions, requirements, and question topics. The examination covers various concepts such as stability conditions for difference equations, non-normality of asset returns, arch and garch models, multivariate garch models, and value at risk (var).

Typology: Exams

2011/2012

Uploaded on 11/29/2012

juni
juni 🇮🇳

4

(17)

122 documents

1 / 2

Toggle sidebar

This page cannot be seen from the preview

Don't miss anything!

bg1
Repeat Examinations 2011/ 2012
Exam Code(s)
1MIF1
Exam(s)
M.Econ.Sc. International Finance
Module Code(s)
EC566
Module(s)
Quantitative Methods in Finance
Paper No.
Second sitting
Repeat Paper
External Examiner(s)
Professor Liam Delaney
Internal Examiner(s)
Professor John McHale
Professor Ciaran O’Neill
Mr. Naoise Metadjer
Instructions:
Answer any 5 questions. Each question carries equal
marks
2
Dr. A. Ahearne/Mr. N. Metadjer
Requirements:
MCQ
Release to Library: Yes No
Handout
Statistical/ Log Tables
Cambridge Tables
Graph Paper
Log Graph Paper
Other Materials
PTO
pf2

Partial preview of the text

Download Repeat Examination for EC566: Quantitative Methods in Finance, 2011/2012 and more Exams Quantitative Techniques in PDF only on Docsity!

Repeat Examinations 201 1 / 201 2

Exam Code(s) 1MIF Exam(s) M.Econ.Sc. International Finance Module Code(s) EC Module(s) Quantitative Methods in Finance Paper No. Second sitting Repeat Paper External Examiner(s) Professor Liam Delaney Internal Examiner(s) Professor John McHale Professor Ciaran O’Neill Mr. Naoise Metadjer

Instructions: Answer any 5 questions. Each question carries equal

marks Duration: 3 hrs. No. of Pages 2 Discipline(s) Course Co-ordinator(s) Dr. A. Ahearne/Mr. N. Metadjer Requirements : MCQ Release to Library: Yes No Handout Statistical/ Log Tables Cambridge Tables Graph Paper Log Graph Paper Other Materials PTO

EC566: Quantitative Methods in Finance Please answer any FIVE questions. Each question carries equal marks

  1. Solve and derive the stability condition for the following difference equation Yt = a 0 + a 1 Yt- 1 + εt (Assume the initial condition is given by y 0 ) [20]
  2. It has been noted for a long time that most financial asset returns are non-normal and this property of non-normality is strongly featured in two statistical phenomena: (i) Extreme events occur more often than predicted by a normal distribution (ii) Crashes occur more often than Booms. Explain what these phenomena correspond to in terms of the distribution of asset returns and discuss the analytical methods that have been developed to test the normality of the asset return distribution. [20]
  3. i) Describe and distinguish between an ARCH(p) and a GARCH(p,q) model. [10] ii) Describe and discuss how GARCH methods can be used to model asymmetry in the asset return distribution. [10]
  4. i) Discuss the general structure of multivariate GARCH models. [5] ii) Explain the limitations of the multivariate GARCH-VEC model and how the GARCH-BEKK model overcomes some of these limitations. In your answer describe one of these models in matrix form. [15]
  5. Assume you have a portfolio of stocks comprising of $10m in IBM stocks with a 2% daily volatility and $5m in AT&T stocks with a 1% daily volatility. i) Calculate the 99% ten day Value at risk for IBM and AT&T separately. [8] ii) Calculate the 99% ten day Value at Risk for the portfolio assuming a correlation coefficient of .7 between IBM and AT&T. What is the diversification benefit? [12]
  6. i) Outline the limitations of the covariance method for estimating Value at Risk (VaR) under the assumption of normally distributed asset returns. [5] ii) Describe how GARCH and GARCH-t models can be used to improve VaR estimates. [15]