Insegnamento a.a. 2020-2021

20541 - ADVANCED QUANTITATIVE METHODS FOR ASSET PRICING AND STRUCTURING

Department of Finance

Course taught in English
Go to class group/s: 31
CLMG (6 credits - II sem. - OP  |  SECS-P/05) - M (6 credits - II sem. - OP  |  SECS-P/05) - IM (6 credits - II sem. - OP  |  SECS-P/05) - MM (6 credits - II sem. - OP  |  SECS-P/05) - AFC (6 credits - II sem. - OP  |  SECS-P/05) - CLELI (6 credits - II sem. - OP  |  SECS-P/05) - ACME (6 credits - II sem. - OP  |  SECS-P/05) - DES-ESS (6 credits - II sem. - OP  |  SECS-P/05) - EMIT (6 credits - II sem. - OP  |  SECS-P/05) - GIO (6 credits - II sem. - OP  |  SECS-P/05) - DSBA (6 credits - II sem. - OP  |  SECS-P/05) - PPA (6 credits - II sem. - OP  |  SECS-P/05) - FIN (6 credits - II sem. - OP  |  SECS-P/05)
Course Director:
MASSIMO GUIDOLIN

Classes: 31 (II sem.)
Instructors:
Class 31: MASSIMO GUIDOLIN


Suggested background knowledge

Although these are not formal pre-requisites, a working knowledge of the key contents of quantitative finance and derivatives as well as of financial econometrics are useful.

Mission & Content Summary

MISSION

This course is designed to illustrate how multivariate techniques are applied to finance, with particular reference to the practice of asset management under nonlinear multivariate dependencies and the measurement and the pricing of multi-asset derivatives. Market crashes and the resulting contagion effects have emphasized the limitations of linear correlations in capturing the dependence structure among 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 underlying assets. 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 issues of structuring of complex derivative securities are provided. A few sessions are devoted to the practical implementation of models in MatLab.

CONTENT SUMMARY

  • The instability of correlations: multivariate GARCH and DCC Models and Markov Switching.
  • The use of realized variance and covariance in risk management.
  • Copulas in risk management.
  • Non linear time series models and models with regimes.
  • The econometrics of network connectedness and its applications to risk management.
  • Single name credit derivatives.
  • Reduced form intensity models.
  • Structural models.
  • Multi name credit derivatives (CDOs).
  • Introduction to structured financial instruments: equity protection structures; exotic options and barriers and their applications in structuring.
  • Correlations and structured products: basket derivatives and certificates.
  • An Introduction to simulations and Monte Carlo pricing.
  • The role of structured products in dynamic asset management.

Intended Learning Outcomes (ILO)

KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Understand risk when correlations are unstable and there are network effects.
  • Understand risk and price assets using realized variance and covariance estimates.
  • Appreciate the effects on risks and pricing of non linear time series dynamics.
  • Grasp the importance of network effects in contagion dynamics.
  • Know the basic structure and economic nature of single name credit derivatives.
  • Understand the structure of multiname credit derivatives (CDOs).
  • Know the structure and price features of structured financial instruments (equity protection structures, exotic options and barriers).
  • Understand the role of structured products in dynamic asset management.

APPLYING KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Assess risk when correlations are unstable and there are network effects.
  • Assess risk and price assets using realized variance and covariance estimates.
  • Apply copulas in risk management.
  • Assess risks and produce forecasts from non linear time series models.
  • Appreciate the importance of network structure and effects in measuring and pricing risks.
  • Price single name credit derivatives.
  • Price CDS using reduced form intensity models.
  • Price CDS using structural models.
  • Price Multi name credit derivatives (CDOs).
  • Price structured financial instruments (equity protection structures, exotic options and barriers).

Teaching methods

  • Face-to-face lectures
  • Exercises (exercises, database, software etc.)
  • Group assignments

DETAILS

Exercises are performed by illustrating MatLab script codes in a variety of applications. There is a take-home group assignment to be executed in Matlab on Pricing CDS and CDOs and on the effects of multivariate predictability on optimal asset allocation.


Assessment methods

  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
     x
  • Oral individual exam
     x
  • Group assignment (report, exercise, presentation, project work etc.)
     x

ATTENDING STUDENTS

  • 66.67%: 1 hour exam.
  • 20%: take-home group project.
  • 13.33%: Individual presentation/discussion of project and brief oral examination.

NOT ATTENDING STUDENTS

  • 100%: 2 hour exam.

Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

  • J. HULL, Options, Futures, and Other Derivatives,  Perason, 9th edition, 2018. 
  • M. GUIDOLIN, M. PEDIO, Essentials of Time Series for Financial Applications, Academic Press, 2018, 1st edition.
  • M. CAMELIA (ed), I certificati di investimento. Mercati, strutture finanziarie, strategie gestionali,  Edizioni Il Sole 24 ORE, 2009. 
  • P. WILMOTT,  Introduces Quantitative Finance,  John Wiley & Sons, 2007.
Last change 15/12/2020 15:33