1. Risk Management Basics
Andras Bohak|

2. Agenda Risk Basics How Institutional Investors Use Risk Tools
Risk and Return on a Single Equity Manager
How an Asset Owner Views the Equity Manager

3. Risk Basics

4. Factor Models in One Slide

5. One Measure of Risk: Standard Deviation
Standard normal curve
Distribution of possible outcomes, with a mean of zero and standard deviation of one

6. Standard Deviation of Returns

7. Risk Relative to a Benchmark
Active risk
The goal of all active managers is to outperform the benchmark
Beating the benchmark requires taking bets by overweighting or underweighting securities
Deviating from the benchmark may not result in outperformance
Generally the larger the bets, the higher the active risk
Bets can be intended or unintended
Risk analysis helps ensure that the manager’s intended ideas are implemented without unintended bets negating alpha

8. Risk Relative to a Benchmark

9. How Institutional Investors Use Risk Tools

10. The Investment Process

11. Why Asset Owners Implement Risk Management
Monitor Total Assets
Measure the contribution to risk from asset classes, sub asset classes, and managers
Analyze Strategic and Tactical Asset Allocations
Evaluate plan risk coming from allocation and selection decisions
Evaluate Manager Risk and Performance
Measure performance against benchmarks, monitor for style drift, and select managers
Measure Tail Risk
Historical and predictive stress testing, Value at Risk, and Shortfall analysis
Perform Risk Budgeting
Optimally spend risk, based on traditional asset classes or risk-based buckets

12. Components Needed to Manage Risk
Risk Model
Structural
Forward Looking
Long Horizon
Holdings-based
Data
Holdings
Market Data
Benchmarks
Returns (user-provided)
Asset Classes
Public
Derivatives
Private / Illiquid
ETFs
Mutual Funds
Methods
Market Values
Common Factors
Simulations
Stress Testing
Optimizations
Reporting
Risk Factors
Absolute/Active Space
We provide components that can be used to support solutions across the complexity precision spectrum. From holdings-based to index; from market values to VaR simulations. From equities to alternative asset classes.
Holdings-based improves modeling precisions. Returns-based model is available for HFs, where no transparency exists.
Software Platform

13. A Typical Asset Management Setup
Front Office
Middle Office / Aggregators
Back Office
Typical Roles
Portfolio Managers
Quant Analysts
Risk Managers
Asset Allocation Teams
Performance Attribution Teams
Pension Plan Managers
Compliance
Performance Reporting
Models Types
“Analytical” Models
Holdings
Custom, home-built
Valuation
Factor (equity)
“Risk” Models
Hodge-podge
Returns
Nothing (manager-provided)
Factors (growing)
Holdings (growing)
Portfolio Accounting
Trade Order Management
Performance Measurement
Needs
Flexibility and customization
Aggregation
Structure
Automation
Predicatibility

14. Measuring Investment Risk Using Holdings Based Models
Sample Factors
Interest Rates
Credit Spreads
Prepayments
Leverage
Value/Growth
Industries
Currencies
Investment risk is a function of:
Risk
Position Size
Exposure to Factors
Factor Volatilities
Correlations Between Factors

15. Measuring Investment Risk Using Returns Based Models
Sample Returns
Asset A
Asset B
Manager A
Manager B
Investment risk is a function of:
Risk
Historical Return
Correlations Between Returns

16. Using Market and Common Factors to Model Risk
Risk analytics are function of pricing models and market or common factors
Instruments are mapped to a set of factors
Standard instrument pricing models compute exposures to factors
Sensitivities & Exposures
Stress Testing
Simulations (Historical & Monte Carlo)
Performance Attribution
Factor Risk
Market Factors
Common Factors
* This slide could probably use some arrows, but the point is to show that we have instrument-specific pricing models that are then used to compute exposures to market and/or common factors. Technically in B1, exposures are computed to both market (i.e. durations, stress tests) and common factors (BIM) – but I wouldn’t parse it so fine when discussing this slide with a client.
* Make clear when presenting that “sensitivities and exposures” also refers to counterparty risk!
Instrument Pricing Models
Equities
Fixed Income
Derivatives
Commodities
Alternatives

17. Common Factor Models Identify Sources of Risk
Based on the idea that an instrument’s volatility can be explained more precisely by its characteristics than by looking at historical returns
Returns do not capture how an asset’s fundamentals have changed over time
Current characteristics provide more insight into drivers of risk
Risk is broken down into fundamental market factors to understand sources of risk
Explain risk using a smaller set of factors that can be easily explained and used to manage the portfolio
Intuitive style, industry, interest rate, and spread factors match the investment process
To measure instrument risk, measure its exposure to these factors based on market and fundamental data
Some amount of risk is “specific”, or idiosyncratic, to that individual security, and not correlated with common factor movements

18. Equity Manager

19. Our Equity Manager ~$20M allocation Fundamental manager
Twenty names in automotive industry
Equally weighted on
No rebalance rules, no subsequent buys/sells
Benchmarked to MSCI World

20. Our Equity Manager’s Performance
Information ratio: -0.11
Beta: 1.60
Portfolio return: -15.3%
Benchmark return: -2.5%
Active return: -12.8%

21. Portfolio’s Active Return

22. Portfolio’s Active Return

23. Industry Exposures as of August 2012

24. Our Portfolio: Risk Exposure to Two Industries

25. MSCI World Benchmark: Broad Industry Exposure

26. Single Name Risk Concentration in Our Portfolio

27. US Risk Concentration Across Industries

28. Large Exposure to Style in Automobiles & Components

29. Top Ten Names Drive 90% of Risk