P2.T7. Operational & Integrated Risk Management. Michael Crouhy, Dan Galai and Robert Mark, The Essentials of Risk Management, 2nd Edition

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1 P2.T7. Operational & Integrated Risk Management Bionic Turtle FRM Practice Questions Michael Crouhy, Dan Galai and Robert Mark, The Essentials of Risk Management, 2nd Edition By David Harper, CFA FRM CIPM

2 CROUHY, CHAPTER 15: MODEL RISK... 3 P2.T MODEL ERROR AND MODEL IMPLEMENTATION RISK

3 Crouhy, Chapter 15: Model Risk P2.T Model error and model implementation risk P2.T Mitigation of model risk P2.T Model error and model implementation risk Learning Objectives: Identify and explain errors in modeling assumptions that can introduce model risk. Explain how model risk can arise in the implementation of a model According to Crouhy, Galai & Mark, JPMorgan's London Whale incident "showed that model risk has no respect for the size or standing of an institution." According to the US Senate's Subcommittee Report, despite portraying itself as an expert in risk management, the bank's Chief Investment Office (CIO) which was charted with managing excess deposits "placed a massive bet on a complex set of synthetic credit derivatives that, in 2012, lost at least $6.2 billion." Which of the following is TRUE as risk management failure that contributed to the loss at JPMorgan's CIO? a) Soon after breaching the bank' and CIO's VaR limit, a new VaR model was adopted (and approved) that reduced the SCP VaR by 50%, enabling the CIO to end its breach b) The CIO switched from its historical practice of marking credit derivative positions at or near the midpoint price in the daily range to assigning the favorable price within the daily price range c) SCP trades routinely breached the limits on all five key metrics used by CIO (ie, VaR, CS01, CSW10%, stress loss, and stop loss), and the breaches were reported to management, but the breaches were largely ignored d) All of the above are true, according to the Senate Subcommittee: the loss was caused by failures in operational risk, model risk, and corporate governance 3

4 Crouhy, Galai & Mark explain that the main cause of model risk are either (i) model error or (ii) implementation. Model error is when "the model might contain mathematical errors or, more likely, be based on simplifying assumptions that are misleading or inappropriate." Implementation is when "the model might be implemented wrongly, either by accident or as part of a deliberate fraud. " Each of the following is a classic example of how model error can be introduced EXCEPT which is the LEAST likely assumption, by itself, to create model error risk? a) To assume an asset's distribution is stationary over time in order to maintain or improve the tractability of the model b) To assume a delta-neutral hedging strategy is risk-free and can be maintained because active re-balancing is unrealistic c) To assume asset returns, follow an empirical distribution simply because the historical data happens to be easily available d) To assume the forward rates--i.e., that are used to value fixed-income instruments--are log normal although interest rates have shifted into a long-term regime of negative territory In regard to model implementation, Crouhy says "even if a model is correct and is being used to tackle an appropriate problem, there remains the danger that it will be wrongly implemented. With complicated models that require extensive programming, there is always a chance that a programming bug may affect the output of the model. Some implementations rely on numerical techniques that exhibit inherent approximation errors and limited ranges of validity. Many programs that seem error-free have been tested only under normal conditions and so may be error-prone in extreme cases and conditions. " With respect to model implementation, which of the following is the BEST pieces of advice? a) Ensure responsibility for data accuracy is clearly assigned b) Remove outliers in all cases because outliers distort skew and kurtosis of the distribution c) Volatility and correlation should be directly observed rather than forecast; if these two inputs cannot be observed, seek an alternative approach d) Seek the maximum length of the sampling period in order to improve the power of statistical tests and reduce estimation errors 4

5 Answers: D. All of the above are TRUE, according to the Senate Subcommittee: the loss was caused by failures in operational risk, model risk, and corporate governance In regard to true (A) which is model risk (emphasis ours): CIO traders, risk personnel, and quantitative analysts frequently attacked the accuracy of the risk metrics, downplaying the riskiness of credit derivatives and proposing risk measurement and model changes to lower risk results for the SCP [Synthetic Credit Portfolio]. In the case of the CIO VaR, after analysts concluded the existing model was too conservative and overstated risk, an alternative CIO model was hurriedly adopted in late January 2012, while the CIO was in breach of its own and the bankwide VaR limit. The bank did not obtain OCC approval as it should have to use the model for the SCP. The CIO s new model immediately lowered the SCP s VaR by 50%, enabling the CIO not only to end its breach, but to engage in substantially more risky derivatives trading. Months later, the bank determined that the model was improperly implemented, requiring error-prone manual data entry and incorporating formula and calculation errors. On May 10, the bank backtracked, revoking the new VaR model due to its inaccuracy in portraying risk, and reinstating the prior model. In regard to true (B) which is an operational risk failure (emphasis ours): "To minimize its reported losses, the CIO began to deviate from the valuation practices it had used in the past to price credit derivatives. In early January, the CIO had typically established the daily value of a credit derivative by marking it at or near the midpoint price in the daily range of prices (bid-ask spread) offered in the market place. Using midpoint prices had enabled the CIO to comply with the requirement that it value its derivatives using prices that were the most representative of fair value. But later in the first quarter of 2012, instead of marking near the midpoint, the CIO began to assign more favorable prices within the daily price range (bid-ask spread) to its credit derivatives. The more favorable prices enabled the CIO to report smaller losses in the daily profit/ loss (P& L) reports that the SCP filed internally within the bank. In regard to true (C), which is a case of poor corporate governance: In contrast to JPMorgan Chase s reputation for best-in-class risk management, the whale trades exposed a bank culture in which risk limit breaches were routinely disregarded, risk metrics were frequently criticized or downplayed, and risk evaluation models were targeted by bank personnel seeking to produce artificially lower capital requirements. The CIO used five key metrics and limits to gauge and control the risks associated with its trading activities, including the Value-at-Risk (VaR) limit, Credit Spread Widening 01 (CS01) limit, Credit Spread Widening 10% (CSW10%) limit, stress loss limits, and stop loss advisories. During the first three months of 2012, as the CIO traders added billions of dollars in complex credit derivatives to the SCP, the SCP trades breached the limits on all five risk metrics. In fact, from January 1 through April 30, 2012, CIO risk limits and advisories were breached more than 330 times. The SCP s many breaches were routinely reported to JPMorgan Chase and CIO management, risk personnel, and traders. The breaches did not, however, spark an in-depth review of the SCP or require immediate remedial actions to lower risk. Instead, the breaches were largely ignored or ended by raising the relevant risk limit. 5

6 C. FALSE: To assume asset returns follow an empirical distribution (simply because the historical data happens to be easily available) is considered an advantage over "theoretical" (ie, parametric) distributions. In regard to (A), (B) and (D), each is cited as an example of model error that can create model risk. In regard to true (A), Crouhy et al say "the most frequent error in model building is to assume that the distribution of the underlying asset is stationary (i.e., unchanging) when in fact it changes over time. The case of volatility is particularly striking. " In regard to true (B), Crouhy et al say "As a practical example, most derivative pricing models are based on the assumption that a delta-neutral hedging strategy can be put in place for the instruments in question i.e., that the risk of holding a derivative can be continually offset by holding the underlying asset in an appropriate proportion (hedge ratio). In practice, a deltaneutral hedge of an option against its underlying asset is far from being completely risk-free, and keeping such a position delta-neutral over time often requires a very active rebalancing strategy. Banks rarely attempt the continuous rebalancing that pricing models assume. For one thing, the theoretical strategy implies the execution of an enormous number of transactions, and trading costs are too large for this to be feasible. Nor is continuous trading possible, even disregarding transactions costs: markets close at night, on national holidays, and on weekends." In regard to true (D), Crouhy et al say "A model can be found to be mathematically correct and generally useful and yet be misapplied to a given situation. For example, some term structure models that are widely used to value fixed-income instruments depend upon the assumption that forward rates are log normal that is, that their rates of change are normally distributed. This model seems to perform relatively well when applied to most of the world s markets with the exception of Japan for the last 10 years and the United States and Europe in the immediate post-crisis years (because central banks implemented quantitative easing monetary policies and flooded the markets with huge amount of liquidity). Post-crisis markets were characterized by very low interest rates, and Japan sometimes exhibited negative rates; in these conditions, different statistical tools (e.g., Gaussian and square root models) for interest rates work much better. " 6

7 A. TRUE: Ensure responsibility for data accuracy is clearly assigned. It's easy to underestimate the importance of data governance, but this seemingly mundane piece of advice is important: if nobody has responsibility, then some of the other rules and guidelines won't be nearly as relevant. In regard to false (B), Crouhy et al say "All statistical estimators are subject to estimation errors involving the inputs to the pricing model. A major problem in the estimation procedure is the treatment of outliers, or extreme observations. Are the outliers really outliers, in the sense that they do not reflect the true distribution? Or are they important observations that should not be dismissed? The results of the estimation procedure will be vastly different depending on how such observations are treated. Each bank, or even each trading desk within a bank, may use a different estimation procedure to estimate the model parameters. " In regard to false (C), Crouhy says "Volatilities and correlations are the hardest input parameters to judge accurately. For example, an option s strike price and maturity are fixed, and asset prices and interest rates can easily be observed directly in the market but volatilities and correlations must be forecast... Throughout the history of the derivatives markets, the fact that model parameters such as volatility and correlation cannot be observed directly has given rise to many opportunities for both genuine mistakes and deliberate tampering that can be countered only through robust control procedures and independent vetting." Finally, Crouhy also writes: "The most frequent problems in estimating values, on the one hand, and assessing the potential errors in valuation, on the other, are: Inaccurate data. Most financial institutions use internal data sources as well as external databases. The responsibility for data accuracy is often not clearly assigned. It is therefore very common to find data errors that can significantly affect the estimated parameters. Inappropriate length of sampling period. Adding more observations improves the power of statistical tests and tends to reduce the estimation errors. But the longer the sampling period, the more weight is given to potentially stale and obsolete information. Especially in dynamically changing financial markets, old data can become irrelevant and may introduce noise into the estimation process. Problems with liquidity and the bid/ ask spread. In some markets, a robust market price does not exist. The gap between the bid and ask prices may be large enough to complicate the process of finding a single value. Choices made about the price data at the time of data selection can have a major impact on the output of the model." Discuss in forum here: 7

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