Course 2021-2022 a.y.

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  |  12 credits 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 quant 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 portion of the course is devoted to tracing a link between empirical asset pricing and portfolio sorting methods and the practice of asset management. 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 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.
  • Appreciate the theoretical and practical importance of pricing the cross section of stock returns.
  • Developing and assessing the classical anomalies/priced factors: size, value, and momentum
  • Discovering and using in applied asset management the “new” anomalies: volatility, higher-order moments, and liquidity

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 structured financial instruments (equity protection structures, exotic options and barriers).
  • Price the cross section of stock returns.
  • Assess the classical anomalies/priced factors: size, value, and momentum
  • Use in applied asset management the “new” anomalies: volatility, higher-order moments, and liquidity

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 dynamic asset allocation when structured products are available 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
  • Group assignment (report, exercise, presentation, project work etc.)
     x

ATTENDING STUDENTS

  • 50%: 60-minute exam.
  • 50%: take-home group project.

NOT ATTENDING STUDENTS

  • 100%: 90-minute exam.

Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

  • T. BALI, R.,F., ENGLE, S., MURRAY, Empirical Asset pricing: The Cross Section of Stock Returns, John Wiley & Sons, 2016.
  • J.Y., CAMPBELL, Financial Decisions and Markets, Princeton University Press, 2017.
  • M. GUIDOLIN, M. PEDIO, Essentials of Time Series for Financial Applications, Academic Press, 2018, 1st edition.
  • P. WILMOTT,  Introduces Quantitative Finance,  John Wiley & Sons, 2007.
Last change 22/12/2021 23:56