Master
2019/2020

# Risk Management I

Type:
Elective course (Financial Economics)

Area of studies:
Economics

Delivered by:
International College of Economics and Finance

When:
2 year, 1 semester

Mode of studies:
offline

Instructors:
Vincent Fardeau

Master’s programme:
Financial Economics

Language:
English

ECTS credits:
3

Contact hours:
30

### Course Syllabus

#### Abstract

Prerequisites: first year courses of the MSc in Financial Economics, in particular Financial Economics I (asset pricing). This course deals with the ways in which risks are quantified and managed by financial institutions. It consists of two parts, one on market risk and one on credit risk. The first part of the course studies how to model the risk of portfolios emanating from fluctuations in market prices, or market risk. A parametric structure on the distribution of returns may be imposed, or the realised distribution of returns can be used to generate a nonparametric distribution of returns. With the parametric or non-parametric distribution of returns in hand, the risk of particular portfolios can be studied and optimised with reference to the likelihood of losses (Value-at-Risk or Expected Short-fall). Finally applications and shortcomings of market risk management tools in banking and financial stability regulation will be studied, and in particular the evolution of the Basel regulation. The second part of the course gives an introduction to commonly used models of credit risk. Credit risk is the risk of loss due to a debtor's non-payment of a bond or a loan. Models of default risk of a single counterparty are studied, and then extended to the case of portfolios of bond or loans. The major complication with portfolios is the correlation of defaults. Regulation of credit risk in the Basel II Accord and its transition to Basel III is presented briefly. Finally, financial instruments used to mitigate credit risk, in particular credit derivatives, are discussed. This part of the course is designed to strike a balance between a practical approach to the most popular credit risk models and their theoretical underpinning

#### Learning Objectives

- The course provides students with the tools of risk management and an introduction to the regulatory framework. The course presents the technical aspects of risk management but also insists on the economics of risk management (traders' incentives, general equilibrium effects of regulation, etc.).
- At the end of the course, students should be familiar with, and be able to assess critically, the main techniques and metrics of market risk management, the rationale for and development over time of the regulatory framework, and the main instruments and models used to manage credit risk.

#### Expected Learning Outcomes

- To explain how deviations from Modigliani Miller theorem generate a rationale for hedging
- To distinguish different types of risk
- Tooutline stylized facts about asset returns
- To be able to hedge bonds, futures, plain-vanilla options
- To explain the notion of coherent risk measure
- To calculate Value at Risk and Expected Shortfall using different methods
- To outline the determinants of Value at Risk and Expected Shortfall
- To outline stylized facts about volatility
- To compare different methods and models to forecast volatility (GARCH-type models, realized volatility, implied volatility)
- To evaluate the strengths and limitations of these models
- To backtest a risk management model
- To use standard tests about he violation ratio and the independence of violations
- To assess the challenges and benefits of stresstesting methodologies
- To analyze the regulatory environment for market risk and its recent developments
- To assess the aggregate effects of the regulation on prices and portfolio choice
- To explain how regulation can foster endogenous risk
- To explain ratings-based models of credit risk
- To implement structural models of credit risk using KMV methodology
- To explain intensity models of credit risk
- To explain the Vasicek model and how it underpins the regulatory framework for credit risk
- To be able to use credit derivatives for hedging

#### Course Contents

- Risk measuresValue-at-Risk (VaR) and Expected Shortfall (ES) Coherent risk measures Some analytical expressions for VaR The choice of parameters Historical simulation for VaR and ES
- Credit Risk on PortfoliosVasicek model of correlated defaults Economic capital and reserves using Vasicek model Introduction to copulas
- The case for Risk ManagementWhy hedge? Typology of risks Some well-known risk-management failures Stylized facts about asset returns Hedging assets vs hedging portfolios
- Credit Risk on a Single CounterpartyScoring models Default rates implied from bond prices Ratings Transition matrices Rating-based models (CreditMetrics) Default rates implied from equity prices: Asset-based (structural) models (Merton model) KMV implementation ntensity-based (reduced form) models Constant intensity special case Jarrow-Turnbull and Duffie-Singleton models
- Backtesting and stress testingViolation ratios, Bernoulli tests Testing independence of violations and window length Stress-tests
- Value-at-Risk and regulationThe Basel framework: Basel I, II, III, Liquidity Coverage Ratio, Net Stable Funding Ratio, Fundamental Review of the Trading Book The economics of VaR: Endogenous risk: VaR and procyclical leverage Equilibrium effects of VaR constraints
- Volatility modelingMoving average and weighted moving average GARCH type models Implied and realized volatility
- Credit DerivativesBrief overview of single-name CDS, Basket CDS and CDOs The economics of structured finance

#### Assessment Elements

- Mid-term testStudents who missed the test due to a valid reason are assigned an additonal date to sit it.
- Final exam

#### Bibliography

#### Recommended Core Bibliography

- Christoffersen, P. F. (2003). Elements of Financial Risk Management. Amsterdam: Academic Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=104701
- Hull, J. (2015). Risk Management and Financial Institutions (Vol. Fourth Edition). Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=963813

#### Recommended Additional Bibliography

- Duffie, D., & Singleton, K. J. (2003). Credit Risk : Pricing, Measurement, and Management. Princeton, N.J.: Princeton University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=329732
- Lando, D. (2004). Credit Risk Modeling : Theory and Applications. Princeton, NJ: Princeton University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=329697
- Saunders, A., & Allen, L. (2002). Credit Risk Measurement : New Approaches to Value at Risk and Other Paradigms (Vol. 2nd ed). New York: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=74090