Jan-02 Risk Management Zvi Wiener Financial Risk Management

Zvi WienerFeb-2001 slide 2

Zvi WienerFeb-2001 slide 3 Risk Business Risk Financial Risk – market risk – credit risk – liquidity risk Operational Risk Legal Risk

Zvi WienerFeb-2001 slide 4 Risk Management Examples of good and bad risk management Good or bad risk management is NOT the same as profits and losses. There are many examples of good RM that lead to losses and bad RM that lead to gains.

Zvi WienerFeb-2001 slide 5 Barings February 26, year old bank 28 year old Nick Leeson $1,300,000,000 loss bought by ING for $1.5

Zvi WienerFeb-2001 slide 6 Metallgesellshaft 14th largest industrial group 58,000 employees offered long term oil contracts hedge by long-term forward contracts short term contracts were used (rolling hedge) 1993 price fell from $20 to $15 $1B margin call in cash

Zvi WienerFeb-2001 slide 7 Orange County Bob Citron, the county treasures $7.5B portfolio (schools, cities) borrowed $12.5B, invested in 5yr. notes interest rates increased reported at cost - big mistake! realized loss of $1.64B

Zvi WienerFeb-2001 slide 8 Public Funds ($ million) Orange County1,640 San Diego357 West Virginia279 Florida State Treasury200 Cuyahoga County137 Texas State55

Zvi WienerFeb-2001 slide 9 Derivatives ($ million) Shova Shell, Japan1,580 Kashima Oil, Japan1,450 Metallgesellschaft1,340 Barings, U.K.1,330 Codelco, Chile200 Procter & Gamble, US157

Zvi WienerFeb-2001 slide 10 Investec Clali, Jan-01 Client bought put options without sufficient funds. Loss is 8-15M NIS.

Zvi WienerFeb-2001 slide 11 Barings$1.3B Bank Negara, Malaysia 92$3B Banesto, Spain$4.7B Credit Lyonnais$10B S&L, U.S.A.$150B Japan$500B Financial Losses

Zvi WienerFeb-2001 slide 12 Value of an Option at Expiration E. Call XUnderlying

Zvi WienerFeb-2001 slide 13 Call Value before Expiration E. Call XUnderlying

Zvi WienerFeb-2001 slide 14 Call Value before Expiration E. Call XUnderlying premium

Zvi WienerFeb-2001 slide 15 Put Value at Expiration E. Put XUnderlying X

Zvi WienerFeb-2001 slide 16 Put Value before Expiration E. Put XUnderlying premium X

Zvi WienerFeb-2001 slide 17 Collar Firm B has shares of firm C of value $200M They do not want to sell the shares, but need money. Moreover they would like to decrease the exposure to financial risk. How to get it done?

Zvi WienerFeb-2001 slide 18 Collar 1. Buy a protective Put option (3y to maturity, strike = 90% of spot). 2. Sell an out-the-money Call option (3y to maturity, strike above spot). 3. Take a “cheap” loan at 90% of the current value.

Zvi WienerFeb-2001 slide 19 Collar payoff payoff Kstock 90 K

Zvi WienerFeb-2001 slide 20 Options in Hi Tech Many firms give options as a part of compensation. There is a vesting period and then there is a longer time to expiration. Most employees exercise the options at vesting with same-day-sale (because of tax). How this can be improved?

Zvi WienerFeb-2001 slide 21 Long term options payoff k K stock 50 K Sell a call Your option Result

Zvi WienerFeb-2001 slide 22 Example You have 10,000 vested options for 10 years with strike $5, while the stock is traded at $10. An immediate exercise will give you $50,000 before tax. Selling a (covered) call with strike $15 will give you $60,000 now (assuming interest rate 6% and 50% volatility) and additional profit at the end of the period!

Zvi WienerFeb-2001 slide 23 Example payoff K Your option Result 60 exercise

Zvi WienerFeb-2001 slide 24 How much can we lose? Everything correct, but useless answer. How much can we lose realistically?

Zvi WienerFeb-2001 slide 25 What is the current Risk? duration, convexity volatility delta, gamma, vega rating target zone Bonds Stocks Options Credit Forex Total?

Zvi WienerFeb-2001 slide 26 Standard Approach

Zvi WienerFeb-2001 slide 27 Modern Approach Financial Institution

Zvi WienerFeb-2001 slide 28 Risk Management Risk measurement Reporting to board Limits monitoring Diversification, reinsurance Vetting Reporting to regulators Decision making based on risk

Zvi WienerFeb-2001 slide 29 Who manages risk? Citibank Bank of England CIBC J. P. Morgan Bankers Trust AIG General Re Swiss Re Aetna Zurich Nike Sony Dell Computers Philip Morris Ford Motor

Zvi WienerFeb-2001 slide 30 Regulators BIS FSA SEC ISDA FASB Bank of Israel Galai’s committee

Zvi WienerFeb-2001 slide 31 Basic Steps in RM process Identify risks Data base (market + position) Risk measurement Regulators Risk Management Reporting Strategic decisions

Zvi WienerFeb-2001 slide 32 Building a RM system Initial study of risks Decision, Risk Manager Risk measurement system Responsibilities and structure Testing Active Risk Management Staff training and maintenance

Zvi WienerFeb-2001 slide 33 Risk Management and Risk Measurement

Zvi WienerFeb-2001 slide 34 Risk Management System Predict future Identify business opportunities Be always right! Risk Management System Can Predict loss, given event Identify most dangerous scenarios Recommend how to change risk profile Can NOT

Zvi WienerFeb-2001 slide 35 Tool, not rule! Limits, Duration, ALM, DFA, VaR

Zvi WienerFeb-2001 slide 36 Definition VaR is defined as the predicted worst-case loss at a specific confidence level (e.g. 99%) over a certain period of time.

Zvi WienerFeb-2001 slide 37 Profit/Loss VaR 1% VaR 1%

Zvi WienerFeb-2001 slide 38 Meaning of VaR A portfolio manager has a daily VaR equal $1M at 99% confidence level. This means that there is only one chance in 100 that a daily loss bigger than $1M occurs, 1% VaR under normal market conditions.

Zvi WienerFeb-2001 slide 39 History of VaR 80’s - major US banks - proprietary 93 G-30 recommendations 94 - RiskMetrics by J.P.Morgan 98 - Basel SEC, FSA, ISDA, pension funds, dealers Widely used and misused!

Zvi WienerFeb-2001 slide 40 Risk Management Structure Market data Current position Risk Mapping Valuation Value-at-Risk Reporting and Risk Management

Zvi WienerFeb-2001 slide 41 interest rates and dollar are NOT independent Value Interest Rate dollar

Zvi WienerFeb-2001 slide 42 Risk Measuring Software CATS, CARMA Algorithmics, Risk Watch Infinity J.P. Morgan, FourFifteen FEA, Outlook Reuters, Sailfish Kamacura Bankers Trust, RAROC INSSINC, Orchestra

Zvi WienerFeb-2001 slide 43 Qualitative Requirements An independent risk management unit Board of directors involvement Internal model as an integral part Internal controller and risk model Backtesting Stress test

Zvi WienerFeb-2001 slide 44 Quantitative Requirements 99% confidence interval 10 business days horizon At least one year of historic data Data base revised at least every quarter All types of risk exposure Derivatives

Zvi WienerFeb-2001 slide 45 Types of Assets and Risks Real projects - cashflow versus financing Fixed Income Optionality Credit exposure Legal, operational, authorities

Zvi WienerFeb-2001 slide 46 Risk Factors There are many bonds, stocks and currencies. The idea is to choose a small set of relevant economic factors and to map everything on these factors. Exchange rates Interest rates (for each maturity and indexation) Spreads Stock indices

Zvi WienerFeb-2001 slide 47 How to measure VaR Historical Simulations Variance-Covariance Monte Carlo Analytical Methods

Zvi WienerFeb-2001 slide 48 Historical Simulations Fix current portfolio. Pretend that market changes are similar to those observed in the past. Calculate P&L (profit-loss). Find the lowest quantile.

Zvi WienerFeb-2001 slide 49 Returns year 1% of worst cases

Zvi WienerFeb-2001 slide 50 Profit/Loss VaR 1% VaR 1%

Zvi WienerFeb-2001 slide 51 Weights Since old observations can be less relevant, there is a technique that assigns decreasing weights to older observations. Typically the decrease is exponential. See RiskMetrics Technical Document for details.

Zvi WienerFeb-2001 slide 52 Variance Covariance Means and covariances of market factors Mean and standard deviation of the portfolio Delta or Delta-Gamma approximation VaR 1% =  P – 2.33  P Based on the normality assumption!

Zvi WienerFeb-2001 slide 53  Variance-Covariance 2.33    1%

Zvi WienerFeb-2001 slide 54 Monte Carlo

Zvi WienerFeb-2001 slide 55 Monte Carlo Distribution of market factors Simulation of a large number of events P&L for each scenario Order the results VaR = lowest quantile

Zvi WienerFeb-2001 slide 56 Monte Carlo Simulation

Zvi WienerFeb-2001 slide 57 Real Projects Most daily returns are invisible. Proper financing should be based on risk exposure of each specific project. Note that accounting standards not always reflect financial risk properly.

Zvi WienerFeb-2001 slide 58 Example You are going to invest in Japan. Take a loan in Yen. Financial statements will reflect your investment according to the exchange rate at the day of investment and your liability will be linked to yen. Actually there is no currency risk.

Zvi WienerFeb-2001 slide 59 Airline company fuel - oil prices and $ purchasing airplanes - $ and Euro salaries - NIS, some $ tickets $ marketing - different currencies payments to airports for services

Zvi WienerFeb-2001 slide 60 Airline company loans equity callable bonds

Zvi WienerFeb-2001 slide 61 Airline company Base currency - by major stockholder. Time horizon - by time of possible price change. Earnings at risk, not value at risk, since there is too much optionality in setting prices. One can create a one year cashflow forecast and measure its sensitivity to different market events.

Zvi WienerFeb-2001 slide 62 Reporting Division of VaR by business units, areas of activity, counterparty, currency. Performance measurement - RAROC (Risk Adjusted Return On Capital).

Zvi WienerFeb-2001 slide 63 How VaR is used Internal Risk Management Reporting Regulators

Zvi WienerFeb-2001 slide 64 Backtesting Verification of Risk Management models. Comparison if the model’s forecast VaR with the actual outcome - P&L. Exception occurs when actual loss exceeds VaR. After exception - explanation and action.

Zvi WienerFeb-2001 slide 65 Backtesting Green zone - up to 4 exceptions Yellow zone exceptions Red zone - 10 exceptions or more OK increasing k intervention

Zvi WienerFeb-2001 slide 66 Stress Designed to estimate potential losses in abnormal markets. Extreme events Fat tails Central questions: How much we can lose in a certain scenario? What event could cause a big loss?

Zvi WienerFeb-2001 slide 67 Unifying Approach One number Based on Statistics Portfolio Theory Verification Widely Accepted Easy Comparison

Zvi WienerFeb-2001 slide 68 Board of Directors (Basle, September 1998) periodic discussions with management concerning the effectiveness of the internal control system a timely review of evaluations of internal controls made by management, internal and external auditors periodic efforts to ensure that management has promptly followed up on recommendations and concerns expressed by auditors and supervisory authorities on internal control weaknesses a periodic review of the appropriateness of the bank’s strategy and risk limits.

Zvi WienerFeb-2001 slide 69 Open Questions Risks related to cashflow Non-traded assets Credit information Global Database Liquidity problem

Zvi WienerFeb-2001 slide 70 Issues Specific to Israel Indexation Exchange Band Shallow Markets

Zvi WienerFeb-2001 slide 71 pluto.mscc.huji.ac.il/~mswiener/ Useful Internet sites Regulators Insurance Companies Risk Management in SEC reports Risk Management resources

Zvi WienerFeb-2001 slide 72

Zvi WienerFeb-2001 slide 73 How to hedge financial risk? Static hedge Forwardsagreements that fix the price Futures Optionsstatic hedge Dynamic delta or vega hedge, with a variable amount of options held. It is applicable if there is a very liquid market and low transaction costs.

Zvi WienerFeb-2001 slide 74 RMG rmgaccess

Zvi WienerFeb-2001 slide 75 Consulting Oliver, Wyman and Co. Willis Corroon Richard Scora Ernst and Young Enterprise Advisors Kamakura

Zvi WienerFeb-2001 slide 76 Examples of Risk Reports

Zvi WienerFeb-2001 slide 77 Regulators BIS G-30 FSA SEC market risk disclosure rules market risk reporting FED, FRB our GARP report Swiss Central Bank Financial Accounting Standards Board

Zvi WienerFeb-2001 slide 78 SEC reports Edgar Yahoo – find symbol – profile – raw SEC reports  market risk in 10K 7A

Zvi WienerFeb-2001 slide 79 3 methods Sensitivity – requires a deep understanding of positions Tabular – when there are 1-2 major risk factors Value-at-Risk – for active risk management

Zvi WienerFeb-2001 slide 80 KPMG report Survey of disclosures: SEC Market Risk, 1999 SEC: GARP

Zvi WienerFeb-2001 slide 81 World Experience Bankers Trust, J.P. Morgan, investment banks Bank regulators, commercial banks Insurance, dealers Investment funds (LTCM) Real companies Investors learn to read risk information!

Zvi WienerFeb-2001 slide 82 Agriculture revenues $1.25B consulting Willis Corroon

Zvi WienerFeb-2001 slide 83 Nike Salaries are paid in Asia Shoes are sold worldwide Financing comes from USA Marketing, storing, shipping worldwide use VaR since 1998.

Zvi WienerFeb-2001 slide 84 Merck index.htmlsee “sensitivity”

Zvi WienerFeb-2001 slide 85 Articles Value at Risk as a Diagnostic Tool for Corporates: The Airline Industry n html Agricultural Applications of Value-at-Risk Analysis: A Perspective wpfi html

Zvi WienerFeb-2001 slide 86 Publications “The New Risk Management: the Good, the Bad, and the Ugly”, P. Dybvig, W. Marshall n_fed.pdf Association for Investment Management and Research

Zvi WienerFeb-2001 slide 87 Web tour ZW, students, VaR and risk management Gloriamundy GARP SEC reports Google

Zvi WienerFeb-2001 slide 88 What is more risky and why? A. 1 year bond B. 10 year bond

Zvi WienerFeb-2001 slide 89 What is more risky and why? A. An in-the-money option? B. An out-of-the-money option?