Duration:

3 days

3 days

Location:

Prague, NH Hotel Prague

Prague, NH Hotel Prague

- An Introduction to Quantitative Risk Analysis
- Basic Risk Measures and their Limitations
- Value-at-Risk and other Measures of Downside Risk
- Measuring VaR for Linear and Non-Linear Positions
- Back-testing VaR Models
- Stress Testing for Market, Credit, Liquidity and Operational Risks
- Building and Implementing Risk Management System

The purpose of this seminar is to give you a good understanding of quantitative methods for calculating Value-at-Risk and for back-testing and stress-testing of risk measurement models.

We start with an overall introduction to modern risk analysis and explain why risk measurement has become more important and challenging. We briefly review basic risk measures such as beta, duration, modified duration, convexity and standard deviation and discuss their limitations in a world with increasingly complex financial instruments.

We then give a thorough explanation of how "Value-at-Risk" and other measures of shortfall risk can be calculated for linear as well as non-linear exposures. We explain the use of delta-normal and delta-gamma-normal methods for the calculation of VaR for forwards, swaps and options, and we explain and demonstrate the use numerical techniques (including historical simulation and Monte Carlo simulation) for calculating VaR of more complex instruments and portfolios.

We explain how to back-test these "Value-at-Risk" models. As a particular case study, we look at the back-testing requirements of the Basel II framework. We also take you a step further to show how the impact of estimation risks can be considered by using dynamic parametric VaR models and by correcting standard back-testing procedures.

Further, we explain how to perform stress testing of risk management models for Basel II compliance and to improve internal risk management. We cover a range of methodologies, from simple sensitivity tests to complex stress tests, which aim to assess the impact of a severe macroeconomic stress event on measures like earnings and economic capital. We give examples of stress test for different risk types including market, credit, operational and liquidity risk.

Finally, we discuss how risk management system can be built, tested and implemented.

We start with an overall introduction to modern risk analysis and explain why risk measurement has become more important and challenging. We briefly review basic risk measures such as beta, duration, modified duration, convexity and standard deviation and discuss their limitations in a world with increasingly complex financial instruments.

We then give a thorough explanation of how "Value-at-Risk" and other measures of shortfall risk can be calculated for linear as well as non-linear exposures. We explain the use of delta-normal and delta-gamma-normal methods for the calculation of VaR for forwards, swaps and options, and we explain and demonstrate the use numerical techniques (including historical simulation and Monte Carlo simulation) for calculating VaR of more complex instruments and portfolios.

We explain how to back-test these "Value-at-Risk" models. As a particular case study, we look at the back-testing requirements of the Basel II framework. We also take you a step further to show how the impact of estimation risks can be considered by using dynamic parametric VaR models and by correcting standard back-testing procedures.

Further, we explain how to perform stress testing of risk management models for Basel II compliance and to improve internal risk management. We cover a range of methodologies, from simple sensitivity tests to complex stress tests, which aim to assess the impact of a severe macroeconomic stress event on measures like earnings and economic capital. We give examples of stress test for different risk types including market, credit, operational and liquidity risk.

Finally, we discuss how risk management system can be built, tested and implemented.

- Overview of Coherent Measures of Risk
- General Introduction to Value-at-Risk
- The risk management revolution
- Caveats in using VaR in risk management

- Measuring Multiperiod VaR and Scaling
- Forecasting Volatilities and Correlations
- Bounds for Aggregate Risk
- Harlow’s Lower Partial Moments
- Probability of Shortfall
- Expected shortfall
- Variance of expected shortfall

- Exercises

- Measuring VaR for Portfolios of Linear Instruments
- Position mapping
- Correlation and portfolio volatility
- Undiversified VaR
- Diversified VaR
- VaR for asset portfolios
- VaR for assets/liabilities

- VaR for Linear Derivatives Positions
- FRAs and Deposit Futures
- Bond Forwards and Futures
- FX Forwards
- Interest Rate and FX Swaps
- Exercises

- Local versus Full Valuation
- Delta-Normal Method
- Full Valuation
- Delta-Gamma Approximation
- Historical Simulation Methods
- Monte Carlo Simulation Methods
- Building blocks in Monte Carlo simulation
- Constructing and simulating the SDE
- Sampling from multivariate distributions
- Simulating pay-off profiles
- Calculating percentiles/VaR
- Using Monte Carlo Simulation and Principal Components Analysis

- Exercises

- Setup for Backtesting
- Model Backtesting with Exceptions
- Decision Rule to Accept or Reject Model
- Model Verification: Other Approaches
- Case: Backtesting in Basel
- Conditional Coverage Models
- Examples and Exercises

- Why Stress Testing?
- Implementing Scenario Analysis
- Generating Unidimensional Scenarios
- Multidimensional Scenario Analysis
- Stress-Testing Model Parameters
- Managing Stress Tests

- Using VaR to Measure and Control Risk
- Using VaR for Active Risk Management
- VaR in Investment Management
- The Technology of Risk
- VaR and Liquidity Risk
- Operational and Integrated Risk Management
- VaR, Economic Capital and RAROC
- Exercises

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