1. EF506 Corporate Treasury Management – Cian Twomey 17/11/10 Value-at-Risk (VaR) II: CFaR & ERM

2. Applications of VaR i. Operational risks & case studies ii. Limits of VaR: Taleb vs. Jorion iii. Corporate VaR iv. Cash-Flow-at-Risk (CFaR) v. Enterprise Risk Management (ERM)

3. i. Operational risks Examples: fraud; human error; communication failures; computer breakdowns’ failures of suppliers and/or customers; etc. Problems in all 4 steps to compute a daily VaR using RiskMetrics™: a) Inventory b) Valuation c) Assignment to Riskmetrics™ assets d) Computation

4. a) Inventory Take into a/c all positions on & off firm’s balance sheet System must collect the required information from all its trading operations: made difficult by IT issues Greatest difficulty: obtain data from individuals - misunderstand, forget about positions, or can even hide positions: such risks are called operational risks (OR)

5. a) Inventory & Operational Risk Barings No VaR system: Nick Leeson able to hide losses in a/c that firm viewed as used for small trading errors (Account 88888) Inventory process failed: inaccurate record of firm’s positions. Key failure: Leeson’s dual role + poor oversight Functioning VaR system may have alerted Barings to Leeson’s unauthorised trading positions VaR measurements not feasible given deficiencies in overall IT systems

6. a) Inventory & Operational Risk Allfirst: Ludwig Report (moodle): Allfirst did have VaR systems in place - only real measure used to monitor Rusnak’s FX trading In-house method: Monte Carlo simulation based Rusnak deliberately manipulated VaR calculation Bogus options appeared to hedge real positions & reduced VaR Also falsified figures for ‘holdover’ transactions which reduced Rusnak’s open currency position No independent checks on Rusnak’s direct inputs, so spreadsheet was false Result: Rusnak appeared not to exceed $1.55M VaR limit; in reality, he violated VaR limits by wide margins

7. b) Valuation Topical issue (CDOs, ABSs etc.): Only easy to assign values to positions for instruments with liquid trading Complex derivatives: estimate using some pricing model? Which model? Proprietary models? Q: Can corporate firms use these models? Not typically. Traders rewarded not for quality of pricing models, but for profits: perfectly possible for a trader to make large profits with a poor pricing model. Pricing also requires assumptions, e.g. in option pricing models, assumptions about volatility are crucial. Trader’s assumptions vs. risk manager’s assumptions

8. c) + d) Assignment problems and VaR computation problems. Very large number of different financial positions. Say firm with positions in 500 different financial assets has to choose for each asset a comparable RiskMetrics™ asset or a comparable portfolio of such assets. (LTCM had 60,000 different positions at the time of its collapse.) With derivatives, underlyings have to be matched to Riskmetrics™ asset. Suppose two firms have the same portfolios and the same Riskmetrics™ inputs. Their VaRs could differ because of different valuation models for their derivatives. Their VaRs could differ also because of different assignments to Riskmetrics™ assets and different computation methods for the VaR.

9. Case Studies Jorion: Orange County case mainly concerned with applicability of VaR http://www.gsm.uci.edu/~jorion/oc/case.html Others take issue with Jorion’s interpretation of VaR  VaR measure in isolation highlights large potential losses without conveying any info about corresponding expected upside returns  Citron was pursuing deliberate strategy; however, if VaR of $1BN had been flagged, the likely public outcry would have forced OCIP to terminate its investment programme  If mission is to take risks, gains and losses should be made public

10. LTCM: Recall from Weeks 1 & 2 LTCM’s weekly VaR based on volatility of 20% & capital of $4.7 billion was $215M, but fund lost $460M in June 1998. Aug 17: Russia restructured its debt; Aug 21 – lost $551M; lost $1.85 billion in the entire month of August. 3 weeks of Sep 1998 - lost half a billion dollars per week. Fund exceeded this one-week VaR five weeks in a row. With independent weekly returns, such an event has probability of 0.0000003 of occurring. VaR is not designed to estimate the worst possible outcome. Low-probability events happen. If low probability events happen often, though, there is something wrong with the measurement system. Why VaR failed… Jorion (moodle)…

11. 4 reasons why LTCM’s VaR estimates were biased downward in late Aug & Sep 1998 1) Fat tails: large returns occurred repeatedly 2) Correlations increased dramatically 3) Volatilities increased sharply 4) Liquidity: could not liquidate positions without affecting prices dramatically. If capital is expensive, there may be a limit on how much a fund would use. If LTCM had substantially more capital, its performance would have been worse in 1995 and 1996.  Did it make sense for LTCM to incur the small risk of things going really wrong to have a large probability of outperforming the market?

12. Overall Lessons from Cases? VaR in isolation will do little to keep a firm’s risk exposures in line with the firm’s chosen risk tolerances. Without a well-developed risk management infrastructure – policies and procedures, systems, and well-defined senior management responsibilities – VaR will deliver little, if any, benefits. As all the great derivatives disasters illustrate, no form of risk management – including VaR – is a substitute for good management. Risk management as a process encompasses much more than just risk measurement.

13. Economist: ‘The gods strike back’ & ‘Number crunchers crunched’ Link on moodle – key issues What was the ‘new paradigm’ of risk management? What went wrong with financial models in 2007-9? How was VaR implicated in the crisis? How has it affected perception of financial risk? Were other factors besides poor modelling at fault? What now for VaR? For financial markets?

14. VaR & 2007-9 Credit Crunch NYT article by Nocera (4/1/09) – link on moodle  Outlines background & appeal of VaR  Problems with VaR: “relatively useless as a risk- management tool and potentially catastrophic when its use creates a false sense of security among senior managers and watchdogs” (Einhorn); Taleb – “a fraud”  Taleb’s point: ‘greatest risks are never the ones you can see and measure, but the ones you can’t see and therefore can never measure’  Describes risk managers’ justifications for using VaR (similar to Jorion’s)

15. VaR & 2007-9 Credit Crunch VaR more useful with liquid assets and a long data history Did regulators, investors, senior executives understand real meaning of VaR numbers? Need to combine risk modelling and judgment – latter is forgotten during bubbles… Also risk managers (CFOs, corporate treasurers etc) not viewed as profit centres VaR is a signal – must use it correctly…

16. Haldene: ‘Why banks failed the stress test’, Feb 2009 - worth reading! “Risk management models proved themselves wrong…” “Failed Keynes’ test – that it is better to be roughly right than precisely wrong. With hindsight, these models were both very precise and very wrong.” Outlines history of VaR + stress testing “2008 might well be remembered as year stress-testing failed” 3 categories of market failures: disaster myopia network externalities (contagion, spillovers)  should calculate conditional VaRs (CoVaRs) – VaRs conditional on other institutions in the network simultaneously facing stress misaligned incentives (principal-agent problems)

17. Taleb’s ‘Black Swan’

18. Pros & Cons of VaR: Taleb vs. Jorion Taleb’s ‘Black Swan’ idea  has criticised ‘pseudo-science’ of financial engineering NYT article makes some similar points… Jorion: VaR still an “essential component of sound risk mgt. systems” Acknowledges potential problems in FT ‘Following the Herd’ article Compare 2 views... http://www.fooledbyrandomness.com/jorion.html www.edge.org/3rd_culture/taleb08/taleb08_index.html

19. iii. Taleb Interview Financial engineering not scientific enough ‘VaR is charlatanism’: not scientifically possible to estimate risks of rare events – ‘misleading precision’ Prefer to rely on non-parametric methods, not relying on statistical probabilities, i.e. ‘old-school’ trading ‘Traders will find the smallest crack in the VaR models…largest position…while showing smallest amount of risk’ ‘VaR is a school for sitting ducks’– makes uncorrelated markets become very correlated if everyone uses VaR Alternative:  leverage,  dynamic hedging, less naïve diversification

20. iv. Jorion’s Defence of VaR & FT article of ‘following the herd’ Recognises VAR ‘vicious circle hypothesis’ idea (figure) VaR useful for monitoring; detecting inordinate risks; errors etc.  cannot get absolute measure of worst case  complement VaR with stress-testing VaR can deal with asymmetric payoffs Better able to model daily volatilities (e.g. with GARCH) than other variables (e.g. expected returns) Improve risk forecasts, not rely on ‘market lore’ VaR = engineering = ‘art of the approximation’ VaR  ‘wobbly speedometer’ ; better than nothing

21. iv. Jorion’s Defence of VaR & FT article of ‘following the herd’ Agrees that monitoring using VaR may give traders an incentive to ‘game’ their VaR… “why risk management is not a black box, but a dynamic process…must be aware of the human trait for adaptation” Basel rules vs. in-house volatility measures: regulatory VaR more slow moving & stable FT: “Arguments that bank trading and VaR systems contribute to volatility have no empirical support” VaR still an “essential component of sound risk mgt. systems”

22. Another Alternative: Expected Shortfall Expected shortfall  Hull: “A measure that produces better incentives for traders than VAR is expected shortfall. This is also sometimes referred to as conditional VAR, or tail loss. Where VAR asks the question 'how bad can things get?', expected shortfall asks 'if things do get bad, what is our expected loss?‘ http://www.risk.net/public/showPage.html?page=438284 http://www.risk.net/public/showPage.html?page=438284  Now favoured by ECB, Bank of England and others Lot of current research – e.g. Yamai & Yoshiba, “Value-at-risk versus expected shortfall: A practical perspective”, JBF, 2005, 29(4), 997-1015 [on moodle] 2 cases where expected shortfall may work better: concentrated credit portfolio and foreign exchange rates under market stress

23. VaR: Some Issues to Note Risk management as ‘much a craft as a science’ – VaR only a tool VaR only one tool of many (ref. Goldman Sachs) VaR still to be used for regulatory and accounting disclosure purposes (e.g. Basel II) Dedicated website: www.gloriamundi.orgwww.gloriamundi.org Also note Emanuel Derman’s website: http://www.ederman.com/new/index.html http://www.ederman.com/new/index.html And Paul Wilmott: www.wilmott.comwww.wilmott.com

24. iii. Corporate VaR VaR originated in financial institutions where most instruments are traded on fairly liquid markets and marked-to-market. 2 obvious limitations for corporates: a) Positions for corporates are more difficult to value – why? b) Corporates usually attempt to manage risks over longer horizons than a day or a month – usually varying from months to years.

25. Potential Corporate Uses of VaR 3 potential uses: a) Manage the market risks of particular positions – e.g. FX positions b) Enable a firm to manage exposures resulting from other decisions c) VaR principles can be applied to non- market-price risks

26. Implementing VaR for Corporates – ref. Stocks article Examines the critical decisions needed when implementing a corporate financial risk management framework with VaR. Focuses only on market risks. Applications to debt, FX, commodities, and cash portfolios.

27. Creating a VaR framework for Treasury Implementation process critical to the success + effectiveness of VaR. Considerable undertaking; may require risk management education across the firm. 3 phases to creating a VaR framework: a) Strategic Assessment b) Methodology Selection & Design c) Implementation of VaR Framework

28. a) Strategic Assessment Similar to risk inventory discussed previously (week 2)  Stocks suggests using a risk map (p. 80) Key issue: clarifying strategic objectives to manage senior management’s expectations of the value and applications for the VaR framework (possible outcomes on p.84) Also include ‘an evaluation of the competitive environment’ (Stocks, p.85)

29. b) Methodology Selection & Design Determine most appropriate methodology for measuring VaR and creates performance benchmarks against which to manage risks. 3 primary methods – summary on p.83 & detail pp. 87-9 Smartco: Variance-covariance vs. historical simulation  compare VaR with benchmark portfolio Factors affecting which method to choose outlined

30. c) Implementation of VaR Framework Q: Will risk management be done internally or outsourced?  trade-off involved Corporate treasurer must get final approval on all issues from senior managers. Organisational issues: monitoring + reporting; internal control structure to mitigate market, legal, operational etc. risks; training of treasury; software system. Conclusion: “VaR can be a valuable tool for measuring the market risk of specific types of exposures…meant to assist in the risk management decision-making process; it should not be the decision-making process” (p.92).

31. VaR and Corporate Risk Management Objectives Value risk vs. cash flow risk managers: Value risk managers – desire to avoid bankruptcy; mitigate problems from asymmetric information; reduce expected tax liabilities (ref. Weeks 1+2) Cash flow risk managers – reduce cash flow volatility and thus increase debt capacity.

32. VaR and Corporate Risk Management Objectives VaR is more suited to value risk management – typically this means certain financial institutions. Also VaR is not a panacea – in isolation, it will not compensate for the absence of a well-developed risk management infrastructure.

33. iv. Cash-Flow at-Risk (CfaR) Analogous to VaR: quantify cash flow risks and aggregate them to take account of correlations. Key feature – risk defined in terms of profits rather than asset value. CFaR: “lowest likely cash flow over some period at some chosen confidence level” or the “risks of missing targets in business plans” Aligned to budget planning – horizon; planning cycles; definitions; conventions; and operating units. Involves a system of simulating cash flows based on assumptions about underlying risk factors and the sensitivities of cash flows to those factors

34. CFaR CFaR: 2 approaches (handout) - Sungard: “cash that would be received or paid in a portfolio of 'transactions' with x% of certainty in a given (e.g. 365 days) time horizon” - Stein et al: “ probability distribution of a company’s operating cash flows over some horizon in the future, based on information available today.” - Latter is more standard interpretation

35. Uses of CFaR Standard use: link to cash budgeting / forecasting Stein et al: summary statistics such as 5% or 1% “worst-case” outcomes, thereby providing corporate CFOs with answers to questions like:  “How much can my company’s operating cash flow be expected to decline over the next year if we experience a downturn that turns out to be a five- percent tail event?”  Purpose?

36. Sungard: Airline Hedging Example “Suppose that on December 24, 2004 an airline was considering hedging the exposure that would result from jet fuel purchases in the first quarter of 2005. Currently, the airline is budgeting fuel cost margin of 18%; that is, it expects the fuel cost to be 18% of total revenue. The airline purchases all of its fuel based on the daily Platt's Jet/Kero oil index at US Gulf.”

37. Sungard: Airline Hedging Example Several Questions raised… Assumed the following objective: “The objective of the hedge program is to keep the fuel margin (the fuel cost as a percentage of the revenue) between 18.50%-19.00%.”

38. Sungard: Airline Hedging Example CFaR report  Monte Carlo simulation shows with 95% certainty that the jet fuel prices will not exceed $1.635 per gallon at Q1 2005. Note: Price inputs for calculation came from markets through the forward curves and the implied volatilities.  Construed as the "consensus“ forecast of jet fuel prices using statistical analysis, eliminating subjectivity.

39. Sungard: Airline Hedging Example Based on worst case jet fuel prices of $1.635/gallon, airline must hedge to ensure its hedge objective as not hedging could have worst case impact of fuel cost margin of 22.90% Table shows that hedging 80% to 90% of the total fuel purchase would ensure the executive mandate Scope for some discretion

40. Sungard: Fuel Margin Calculation

41. Sungard: Results of Hedge Programme Hedge put in at $1.28 per gallon for 85% of total fuel purchase or 850,000 gallons through a swap Table (p.4): somewhat successful: achieved 18.34% fuel margin vs. goal of 18.75% CFaR can also be used to apportion total hedge volume to several instruments

42. Using CFaR vs. VaR Estimating CFaR is challenging CFaR can be seen as analogous to VaR that can be useful for non-financial firms. Use same basic methodological approach? VaR: Stein et al outline “bottom up” method – requires: a) Identification of main sources of risk b) Matching risks to traded assets for which data are available Q: Can this be replicated for non-financial firms?

43. Stein et al: Example - Dell Identifying & quantifying exposures is key Example: FX risk  Quantifiable FX risk (e.g. on transactions) likely to be less important than economic FX exposure & less important than other risks – e.g. marketing strategy or new product development by Dell or rivals  Would “bottom up” approach be able to capture all the risks & aggregate them appropriately?  “Top-down” historical cash flow volatility approach hampered by lack of data (quarterly)

44. Stein et al’s Alternative Method Identify a group of companies that are good comparables for Dell – e.g. 25 companies & 5 years’ quarterly data on each  500 observations  Possible to estimate 5% tail probabilities with some confidence. Article describes how this was implemented Benchmarked on 4 dimensions: (1) market capitalization, (2) profitability, (3) industry riskiness & (4) stock-price volatility. Plausible?

45. YEAR AHEAD C-FAR DISTRIBUTIONS FOR COCA-COLA, DELL, AND CYGNUS (Fig 1)

46. Comparables Approach to CFaR Pros Looking directly at cash flow variability Repeat to get averages No systematic biases Non-parametric: i.e. not assuming normality Cons  Cannot capture firm- level factors causing differences in CFaR  No link to corporate strategy Exact model specifications explained pp. 12 – 16

47. Stein’s Results

48. -$28.50: in a 5% worst-case year, Dell’s EBITDA would < expectations by $28.50 for every $100 of book assets it has

49. Dell: comparables listed in Table 3, p. 16 Each firm in 1 of 81 bins  c. 1,000 forecast errors for CFaR estimation

50. 3 Standard Uses of CFaR 1) Capital Structure (debt/equity) Policy Key determinant of probability of distress is variability of cash flows  use C-FaR Example: US electricity industry (next slide) 2) Risk Management Policy Recall Froot et al (topic 1): hedging adding value linked to “probability that operating cash flows will fall to the point that important strategic investments are compromised.” Example: Stein et al, p.11 3) Disclosure: Managing Investors’ Expectations “Put earnings shocks into a credible, objective, peer- benchmarked perspective.”

51. Example: US electricity industry

52. v. Enterprise Risk Management (ERM) ERM explained: Dowd, Trema (handout) + Dickinson, Mercer articles (moodle) Dowd: Essence of ERM: management of overall institutional risk across all risk categories & business units. ERM aka ‘strategic’, ‘integrated’ & ‘holistic’ risk management Dickinson: “Enterprise risk is the extent to which the outcomes from the corporate strategy of a company may differ from those specified in its corporate objectives, or the extent to which they fail to meet these objectives (using a ‘downside risk’ measure)”

53. What is ERM? NB: S&P to use ERM evaluation in ratings for non-financial firms from 2009 – evaluating the risk and risk- management information given by firms (link on moodle) ERM system deals with broad risk categories (market risk, credit risk, etc), different risks attached to differing instruments & portfolios (equity risks, commodity risks, swap risks, etc), risks associated with different business units up to level of institution as a whole & risks associated with having offices in different locations operating under differing legal and regulatory systems. Myriad of risks – e.g. Trema (Figure 1) & Casualty (110-1)

54. Risk Map - Trema

55. Motives for Developing ERM Evolution toward ERM can be characterised by a number of driving forces: 1. More – and more – complicated risks 2. External pressures (e.g. governance, shareholder value) 3. Portfolio point of view 4. Growing tendency to quantify risks (incl. VaR) 5. Benchmarking 6. Risk as opportunity

56. Mercer: Treasurer taking on more responsibilities “Treasurers have become more involved in managing a range of financial risks (e.g. pensions, insurance and commodities) besides their conventional roles in foreign exchange, interest rate and cash management” (p.13) Why?  Have requisite skills  CFOs have delegated due to time constraints

57. Mercer – Current Practice Many companies had some ERM system Typically ‘compliance-led’ & static Mostly “only qualitative assessments of probability and impact, address risks individually, fail to incorporate risk into decision-making and deal in one-off ‘point’ estimates rather than dynamic ranges of outcomes.” Leading players “incorporating a risk-return perspective” “Practices, however, are strongly polarised; a few firms’ efforts far outstrip those of the pack.”

58. Evolution of enterprise risk management

59. Mercer – Key ERM Characteristics i) Risk measurement ii) Risk appetite iii) Balance sheet management iv) Performance measurement v) Culture vi) Transparency  Need senior management input

60. Dowd: 4 Main Features of an ERM System i ) Centralised data warehouse in which to store all position, credit and transaction data.. Vast task in any large firm - by product, office, country and client. Account for netting agreements, guarantees, collateral terms, legal issues and other factors ii) Analytics: Where market risks are concerned, analytics might be a Value-at-Risk (VaR) system combined with some system for stress testing. Want systems to analyse credit, liquidity, operational and legal risks.

61. 4 Main Features of an ERM System iii) Monitoring and evaluation: Centralised systems of monitoring and evaluation. Verify data and flag up data problems; Monitor & enforce compliance with position limits and other constraints Validate the pricing, VaR and other models used for analytical purposes; collect and process data for purposes of risk adjustment and performance evaluation.

62. 4 Main Features of an ERM System iv) Decision-making: Feed output into risk measurement and risk management processes. Reports  verification problems and compliance issues; performance evaluation to determine remuneration; validation output sent decision-makers.

63. Putative Benefits of ERM - Dowd a) Take the broader risk picture into account b) Helps to resolve coordination issues between decentralised agents in different business units c) Greater economic efficiency in other ways d) Less risk of major problems vs. older systems of management control and risk management

64. Putative Benefits of ERM - Dowd e) Imposes a discipline on the firm: - greater consistency in data collection, measurement and processing, - highlights inadequacies in the data, - facilitates auditing and monitoring, - more difficult for human error or fraud to slip by unnoticed - improves overall management control; f) Provide stakeholders with better risk information

65. Casualty Case Studies – website linked on moodle Outline a number of success stories related to: a) Risk assessment b) Distribution strategy c) Performance measurement d) Asset allocation e) Strategic planning f) Product design g) Dividend strategy h) Risk financing

66. Simple ERM Example: Linking Market & Credit Risks Market Risk: “Exposure to the uncertain market value of a portfolio” “the potential loss arising from changes in market prices, namely, foreign exchange, interest rates and equity prices.” Credit Risk: “the risk that is associated with the possibility that a borrower will default on any monies that are owed.”

67. Bringing Credit & Market Risks Together a) Market based pricing Incorporating counterparty (credit) risk into pricing of trading assets Reconciling market pricing for “tradable” loans on banking book & related derivative products (e.g. CDS) b) Enterprise (Wide) Risk Management (ERM) Internal – Senior Management wants common, consistent language when talking about risk External – Analysts don’t differentiate between what causes profit volatility – Credit or Market Risk

68. Integration of Credit & Market Risks  Broad adoption of ERM even in financial services industry not yet reality  Even more challenging for corporates Difficult to implement because of far- reaching implications on entrenched systems & processes Most institutions have distinct Market Risk and Credit Risk groups

69. Integration of Credit & Market Risks Several challenges to integration exist : a) People / Pedigree b) Measurement Purposes c) Organisational d) Systems e) Methodology / Theoretical

70. Different Pedigrees Market Risk Traders used to thinking about Risk (volatility) Positions are always considered in a portfolio context – either trader’s or market Traders are transaction/ product oriented Statistical measurement of risk in place since 80’s Credit Risk LOSS considered a bad four-letter word Portfolio context is new Corporate risk managers / bankers are “relationship” oriented Statistical measurement of risk only since mid-90’s

71. Aligning Purpose Market: Risk Measures (VaR & related) are used to measure and manage risk on dynamic basis Back-test actual performance over a year against 95% daily VaR and ensure models/policies are working VaR limits widely used to set risk tolerance and control risk

72. Aligning Purpose Credit: Risk Measures (Capital & related) are used more for performance measurement since risk not traditionally viewed as dynamic Usage for risk management catching on Hard to convince bankers that Capital for Credit Risk “works” How to test against 1 in 2,500 year events ? However, uses in pricing (e.g. RAROC) also becoming norm

73. Systems Systems don’t talk well to each other difficult to collect necessary data in one place If they could talk, what would they say? currently no one standard acceptable measure of risk across the enterprise What language would they use? Greeks and VaR in Market Risk Notional Exposures and Ratings in Credit

74. Casualty: Practical issues for succesful implementation Several factors may determine success: 1) Designating an ERM ‘Champion’ 2) Making ERM part of the enterprise culture 3) Determining all possible risks of the organisation 4) Quantifying operational and strategic risks 5) Integrating risks 6) Lack of appropriate risk transfer mechanisms 7) Monitoring the process 8) Start slowly – build up successes

75. Mercer: Limits to ERM “Research found a great disparity between treasurers’ aspirations with respect to ERM and their achievements to date. Potential reasons: a) Organisational Resistance b) Stakeholder disinterest

77. Conclusion: Is the effort worth it? Mercer: “For the most part, treasurers remain disengaged from ERM and have made little progress toward it.” Is topic still foreign to senior management? Benefits appear marginal vs. obvious costs Yet “evidence of a growing trend toward the integrated, quantitative approach that supports decision-making”. “Requires treasurers to educate themselves in new technical skills, best practices, non-financial risks and business strategy”. Computing power and advanced techniques may make this reality sooner rather than later?