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EDHEC-Risk Executive Education

Understanding, Designing and Testing Quantitative Risk Models: Overview

20 August, 2014 - Singapore


A seminar aiming to provide practitioners with an understanding of the objectives and limitations of quantitative risk models and with the background to design, calibrate and stress-test risk models that are fit for purpose.

Since the global financial crisis of 2008, there has been a substantial increase in emphasis on risk measurement and management at the instigation of both industry players and regulatory bodies. Risk measurement and management techniques now receive attention in excess of what is required for mere compliance with risk-based capital and risk limit regulations as significant resources are expended to refine and integrate them into a coherent process supporting better investment management. Despite this increased focus, a lot of confusion remains about what a quantitative risk management process can and cannot achieve. In this context, this course aims to provide practitioners with an understanding of the objectives and limitations of quantitative risk models and with the background to design, calibrate and stress-test risk models that are fit for purpose.

After defining risk and attempting a typology of risks, the course introduces and discusses the fundamental building blocks of risk models (i.e. the risk horizon, the risk drivers, the portfolio profit-and-loss (P&L) distribution and the risk measures). It discusses the characteristics of the portfolio P&L distribution at the risk measurement horizon and the application of a particular risk measure to collapse the risk present in this distribution into numbers that can be analysed. It explains how risk management requires knowledge of the risk drivers of the various financial instruments in the portfolio and how they combine to impact the P&L distribution. The course underlines that to understand the limitations of risk management, one has first to recognise that any risk model is based on assumptions concerning the dynamics of risk drivers, the contingent pricing of the instruments in the portfolio, and the way the interrelations of these instruments combine to impact the P&L. The course also stresses that another dimension of model-risk is the informational content of any data used for parameter estimation. Back-testing and stress-testing are necessary steps to fully grasp the limitations of risk models and adequately manage them. The course discusses these issues both conceptually and in the context of specific risk models.

The first class of risk models analysed by the course are the traditional models that draw from modern portfolio theory, assume normal distributions for instruments and focus on volatility at the risk horizon. The course explains how portfolio positions are approached by their marginal risks and percentage contribution to volatility, how the variance-covariance matrix is used to produce a portfolio-wide risk measure, and how factor models are used not only to make the estimation of the variance-covariance matrix more computationally efficient, but also to gain insight into the underlying systematic sources of risk for investment decision making and risk hedging. The most successful commercial implementation of factor models is probably the Barra multi-factor equity model family.

A drawback of volatility as a proxy for risk is that it gives equal importance to both profits and losses, whereas investors and regulators are more concerned about the latter. The course thus introduces Valueat- Risk (VaR) as a portfolio downside risk measure which has gained central importance for regulatory capital computation and financial reporting, but also for risk budgeting and management, and it discusses different methods for calculating VaR. The course also relaxes the normality assumption of the traditional model, which suits equity portfolios that do not exhibit high levels of skewness or kurtosis, but is particularly inappropriate for portfolios that include instruments exhibiting bond-like convexity or derivative-like payoffs. In this context, instruments need to be viewed as functions of the underlying risk factors, which adds a layer of complexity. Against this backdrop, the course discusses the Delta-Normal VaR approach which relies on a linear decomposition of the portfolio P&L distribution by the sensitivities of the instruments to the underlying risk drivers. It explains how this method, which underpins the RiskMetrics model, gains in computational efficiency at the cost of some heavy assumptions, notably that of normally distributed risk drivers. The course then discusses the failures of the Delta-Normal approach and the pros and cons of the different ways of correcting them, such as introducing second-order terms into the linear-sensitivity model (i.e. the Delta-Gamma extension); using a model agnostic to any distributional assumptions (historical VaR); and simulating the dynamics of the risk drivers to price all instruments and build the portfolio P&L at the risk horizon (full-valuation Monte Carlo). The course uses a case study to compare the different VaR models and also introduces VaR tools to manage risk from the individual positions up. Finally, the review of VaR models is used to introduce a discussion on how to assess, mitigate and monitor model-risk as part of a sound management process through back-testing, stress-testing, control of estimation risk and ongoing review and robustification.

The latest crisis has underlined the importance of understanding, quantifying and managing the risk of extreme events. VaR being but a loss threshold which is expected to be violated according to a pre-defined frequency, it cannot inform on the severity of the losses suffered when these violations occur: tail risk measurement requires going beyond VaR. The course thus concludes by equipping participants with tools to measure and manage extreme risk. This final section begins with an attempt to upgrade the classical VaR with information about skewness and kurtosis (Cornish-Fisher VaR), and then introduces a measure of average tail loss – the conditional value-at-risk (CVaR). The course concludes on a comparison of the assumptions, model flexibility, and data requirements of two approaches to computing CVaR: fullparametric modelling of the portfolio P&L vs. a semi-parametric approach integrating insights from Extreme Value Theory first developed for engineering and earth sciences.


Seminar Instructor:

  • Stoyan Stoyanov, Professor of Finance at EDHEC Business School and Head of Research at EDHEC Risk Institute–Asia.


Key Learning Benefits:

The course will enable participants to:

  • Understand the main components of a generic risk model
  • Understand the contribution of factor models to position- and portfolio-level risk management
  • Measure and hedge underlying risk sources and derive a portfolio-wide risk measure
  • Review the traditional VaR framework and VaR computation approaches
  • Find out about the advantages and limitations of advanced VaR models
  • Learn how to assess model risk and back-test and stress-test risk models
  • Understand how to extend the VaR framework to measure extreme risk


Who Should Attend:

The programme is intended for banking, insurance and investment management professionals who participate in the design, implementation and oversight of risk models used for risk budgeting and risk management, risk-based capital assessment and financial reporting, investment analysis and portfolio construction, and risk analysis and performance measurement.

It should be of particular interest to practitioners with the following functions and from the following types of institutions:

Functions

  • Risk committee members
  • Chief risk officers
  • Chief finance officers / Chief actuaries
  • Heads of model development
  • Heads of model validation / risk / risk management
  • Heads of risk and performance analysis
  • Chief investment officers / Directors of investments
  • Heads of investment solutions / structuring / financial services
  • Traders
  • Portfolio managers
  • Risk managers / Middle-office managers
  • Senior analysts and risk / Investment officers, Senior actuaries
  • Senior auditors
  • Senior consultants
  • Senior research officers
Institutions
  • Corporate and investment banks
  • Insurance and reinsurance companies
  • Asset management companies and private banks
  • End-institutional investors
  • Research firms and consultancies
  • Financial software vendors


Continuing Education Credits:

As a participant in the CFA Institute Approved-Provider Program, EDHEC-Risk Institute has determined that this programme qualifies for 7 credit hours. If you are a CFA Institute member, CE credit for your participation in this programme will be automatically recorded in your CE tracking tool.

Please see www.cfainstitute.org/ceprogram for more information.


Understanding, Designing and Testing Quantitative Risk Models:


Attachments

Brochure