Course 2012-2013 a.y.

Go to class group/s: 31

CLMG (6 credits - II sem. - OP  |  SECS-S/06) - M (6 credits - II sem. - OP  |  SECS-S/06) - IM (6 credits - II sem. - OP  |  SECS-S/06) - MM (6 credits - II sem. - OP  |  SECS-S/06) - AFC (6 credits - II sem. - OP  |  SECS-S/06) - CLAPI (6 credits - II sem. - OP  |  SECS-S/06) - CLEFIN-FINANCE (6 credits - II sem. - OP  |  SECS-S/06) - CLELI (6 credits - II sem. - OP  |  SECS-S/06) - ACME (6 credits - II sem. - OP  |  SECS-S/06) - DES-ESS (6 credits - II sem. - OP  |  SECS-S/06) - EMIT (6 credits - II sem. - OP  |  SECS-S/06)

Course Director:
MASCIA BEDENDO

Course Objectives

This course is designed to illustrate how multivariate techniques are applied to finance, with particular reference to market and credit risk measurement, as well as to the pricing of multiasset derivatives. Market crashes and the resulting contagion effects have highlighted the limitations of linear correlation in capturing the dependence structure among financial asset returns for the purposes of: 1) assessing the risk of a financial institution; 2) pricing derivatives whose value depends on the interaction in the performance of different underlyings. Hence, it is essential to learn how to model dependence beyond the correlation structure implied by multivariate normal distributions. A number of practical applications to the pricing of equity / credit derivatives and to risk management will be provided. Ample time will be devoted to the practical implementation of models during MatLab sessions. Some of the applications will be illustrated by leading practitioners in the field.

Course Content Summary

• A review of the basics of multivariate modelling in finance: multivariate normal distribution; standard estimators of covariance and correlation; dimension reduction techniques. The pitfalls of the assumption of multivariate normal returns.
• Alternative measures of dependence in finance
  Rank correlations; tail dependence market crashes and contagion
  Copulas and dependence: basic properties of copulas; Gaussian and Archimedean copulas; simulation of copulas; fitting copulas to empirical financial data
• Application # 1: Modelling default correlation
  A review of default models (structural and intensity-based)
  Single-name credit derivatives (Credit Default Swaps)
  Default correlation models: one-factor Gaussian model and its extensions
  Pricing and hedging of structured credit products (basket default swaps, CDO)
• Application # 2: Risk management
  Dependence structures in financial risk management: VaR and beyond
  Contagion-risk measures
  Counterparty risk measures
  Risk aggregation
• Application # 3: Pricing equity / currency multiasset derivatives
  

Detailed Description of Assessment Methods

Student evaluation consists of both an empirical assignment and a final written closed-book exam. The empirical assignment accounts for 30% of the overall mark, while the written exam accounts for 70% of the overall mark.

The empirical assignment may be produced either by individuals or by groups of students composed by up to four people. The mark gained in the empirical assignment remains valid for one academic year.

Textbooks

• The course material includes academic papers, slides and other notes that are available on yoU@B.
• Chapters from: A.J. MCNEIL, R. FREY, P. EMBRECHTS, Quantitative Risk Management: Concepts, Techniques and Tools, Princeton University press, 2005
  

Exam textbooks & Online Articles (check availability at the Library)

Prerequisites

Students are expected to have attended a core course in statistics and to have good familiarity with undergraduate calculus and linear algebra. Prior exposure to financial derivatives (options, swaps) is beneficial, although not essential.

Last change 06/06/2012 09:43