READING 34: RISK MANAGEMENT A- Risk Management as a Process Risk management is a process involving: 1) The identification of exposures to risk, 2) The establishment of appropriate ranges for exposures 3) The continuous measurement of these exposures, and 4) The execution of appropriate adjustments whenever exposure levels fall outside of target ranges. The process is continuous and may require alterations in any of these activities to reflect new policies, preferences, and information. B- Risk Governance The process of setting overall policies and standards in risk management is called risk governance. Risk governance involves choices of governance structure, infrastructure, reporting, and methodology. The quality of risk governance can be judged by its transparency, accountability, effectiveness (achieving objectives), and efficiency (economy in the use of resources to achieve objectives). Risk governance is an element of corporate governance and begins with choices concerning governance structure. Organizations must determine whether they wish their risk management efforts to be centralized or decentralized. Under a centralized risk management system (now called Enterprise Risk Management or ERM), a company has a single risk management group that monitors and ultimately controls all of the organization’s risk-taking activities. Centralization permits economies of scale and allows a company to recognize the offsetting nature of distinct exposures that an enterprise might assume in its dayto-day operations. In addition, centralized risk management puts the responsibility on a level closer to senior management, where we have argued it belongs. It gives an overall picture of the company’s risk position, and ultimately, the overall picture is what counts. By contrast, a decentralized system places risk management responsibility on individual business unit managers. In a decentralized approach, each unit calculates and reports its exposures independently. Decentralization has the advantage of allowing the people closer to the actual risk taking to more directly manage it. An effective ERM system typically incorporates the following steps (evaluate ERM system): 1) Identify each risk factor to which the company is exposed. 2) Quantify each exposure’s size in money terms. 3) Map these inputs into a risk estimation calculation. 4) Identify overall risk exposures as well as the contribution to overall risk deriving from each risk factor.
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5) Set up a process to report on these risks periodically to senior management, who will set up a committee of division heads and executives to determine capital allocations, risk limits, and risk management policies. 6) Monitor compliance with policies and risk limits. C- Identifying Risks
Market Risk
1- Market Risk Market risk is the risk associated with interest rates, exchange rates, stock prices, and commodity prices. 2- Credit Risk Credit risk is the risk of loss caused by a counterparty or debtor’s failure to make a promised payment. 3- Liquidity Risk Liquidity risk is the risk that a financial instrument cannot be purchased or sold without a significant concession in price because of the market’s potential inability to efficiently accommodate the desired trading size. 4- Operational Risk Operational risk, sometimes called operations risk, is the risk of loss from failures in a company’s systems and procedures or from external events. These risks can arise from computer breakdowns (including bugs, viruses, and hardware problems), human error, and events completely outside of companies’ control, including “acts of God” and terrorist actions.
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5- Model Risk Model risk is the risk that a model is incorrect or misapplied; in investments, it often refers to valuation models. 6- Settlement (Herstatt) Risk Settlement risk as the risk that one party could be in the process of paying the counterparty while the counterparty is declaring bankruptcy. 7- Regulatory Risk Regulatory risk is the risk associated with the uncertainty of how a transaction will be regulated or with the potential for regulations to change. 8- Legal/Contract Risk Legal/contract risk: the possibility of loss arising from the legal system’s failure to enforce a contract in which an enterprise has a financial stake. 9- Tax Risk Tax risk arises because of the uncertainty associated with tax laws. 10- Accounting Risk Accounting risk arises from uncertainty about how a transaction should be recorded and the potential for accounting rules and regulations to change. 11- Sovereign and Political Risks
Sovereign risk is a form of credit risk in which the borrower is the government of a sovereign nation. Political risk is associated with changes in the political environment. Political risk can take many forms, both overt and subtle, and it exists in every jurisdiction where financial instruments trade. 12- Other Risks
ESG risk is the risk to a company’s market valuation resulting from environmental, social, and governance factors. Performance netting risk, which applies to entities that fund more than one strategy, is the potential for loss resulting from the failure of fees based on net performance to fully cover contractual payout obligations to individual portfolio managers that have positive performance when other portfolio managers have losses and when there are asymmetric incentive fee arrangements with the portfolio managers. Settlement netting risk (or again, simply netting risk) refers to the risk that a liquidator of a counterparty in default could challenge a netting arrangement so that profitable transactions are 3
realized for the benefit of creditors.20 Such risk is mitigated by netting agreements that can survive legal challenge. D- Measuring Risk 1- Measuring Market Risk Market risk refers to the exposure associated with actively traded financial instruments, typically those whose prices are exposed to the changes in interest rates, exchange rates, equity prices, commodity prices, or some combination thereof.
The most widely used and arguably the most important of these is the standard deviation of price outcomes associated with an underlying asset. We usually refer to this measure as the asset’s volatility. In some applications, such as indexing, volatility relative to a benchmark is paramount. In those cases, our focus should be on the volatility of the deviation of a portfolio’s returns in excess of a stated benchmark portfolio’s returns, known as active risk, tracking risk, tracking error volatility, or by some simply as tracking error.
A portfolio’s exposure to losses because of market risk typically takes one of two forms: 1) sensitivity to adverse movements in the value of a key variable in valuation (primary or first-order measures of risk) 2) risk measures associated with changes in sensitivities (secondary or second-order measures of risk). Primary measures of risk often reflect linear elements in valuation relationships; secondary measures often take account of curvature in valuation relationships. Each asset class (e.g., bonds, foreign exchange, equities) has specific first- and second-order measures. Let us consider measures of primary sources of risk first. For a stock or stock portfolio, beta measures sensitivity to market movements and is a linear risk measure. For bonds, duration measures the sensitivity of a bond or bond portfolio to a small parallel shift in the yield curve and is a linear measure, as is delta for options, which measures an option’s sensitivity to a small change in the value of its underlying. These measures all reflect the expected change in price of a financial instrument for a unit change in the value of another instrument. Second-order measures of risk deal with the change in the price sensitivity of a financial instrument and include convexity for fixed-income portfolios and gamma for options. Convexity measures how interest rate sensitivity changes with changes in interest rates. Gamma measures the delta’s sensitivity to a change in the underlying’s value. Delta and gamma together capture first- and second-order effects of a change in the underlying. For options, two other major factors determine price: volatility and time to expiration, both first-order or primary effects. Sensitivity to volatility is reflected in vega, the change in the price of an option for a 4
change in the underlying’s volatility. Option prices are also sensitive to changes in time to expiration, as measured by theta, the change in price of an option associated with a one-day reduction in its time to expiration. 2- Value at Risk Value at risk (VAR) is an estimate of the loss (in money terms) that we expect to be exceeded with a given level of probability over a specified time period. First, we see that VAR is an estimate of the loss that we expect to be exceeded. Hence, it measures a minimum loss. The actual loss may be much worse without necessarily impugning the VAR model’s accuracy. Second, we see that VAR is associated with a given probability. Third, we see that VAR has a time element and that as such, VARs cannot be compared directly unless they share the same time interval. a. Elements of Measuring Value at Risk An appropriate VAR measure requires the user to make a number of decisions about the calculation’s structure. Three important ones are picking a probability level, selecting the time period over which to measure VAR, and choosing the specific approach to modeling the loss distribution. The industry has developed a set of three standardized methods for estimating VAR: the analytical or variance–covariance method, the historical method, and the Monte Carlo simulation method. We will describe and illustrate each of these in turn. b. The Analytical or Variance–Covariance Method The analytical or variance–covariance method begins with the assumption that portfolio returns are normally distributed. With the standard normal distribution, 5 percent of possible outcomes are likely to be smaller than 1.65. Therefore, to calculate a 5 percent VAR for a portfolio (i.e., VAR at a probability of 0.05), we would estimate its expected return and subtract 1.65 times its estimated standard deviation of returns. Some approaches to estimating VAR using the analytical method assume an expected return of zero. This assumption is generally thought to be acceptable for daily VAR calculations because expected daily return will indeed tend to be close to zero. Because expected returns are typically positive for longer time horizons, shifting the distribution by assuming a zero expected return will result in a larger projected loss, so the VAR estimate will be greater. Therefore, this small adjustment offers a slightly more conservative result and avoids the problem of having to estimate the expected return, a task typically much harder than that of estimating associated volatility. Another advantage of this adjustment is that it makes it easier to adjust the VAR for a different time period. This simple conversion of a shorter- term VAR to a longer-term VAR (or vice versa) does not work, however, if the average return is
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not zero. In these cases, one would have to convert the average return and standard deviation to the different time period and compute the VAR from the adjusted average and standard deviation. The analytical or variance–covariance method’s primary advantage is its simplicity. Its primary disadvantage is its reliance on several simplifying assumptions, including the normality of return distributions. c. The Historical Method Using historical VAR, we calculate returns for a given portfolio using actual daily prices from a userspecified period in the recent past, graphing these returns into a histogram. From there, it becomes easy to identify the loss that is exceeded with a probability of 0.05 (or 0.01 percent, if preferred). The historical method has the advantage of being nonparametric (i.e., involving minimal probabilitydistribution assumptions), enabling the user to avoid any assumptions about the type of probability distribution that generates returns. The disadvantage, however, is that this method relies completely on events of the past, and whatever distribution prevailed in the past might not hold in the future. d. The Monte Carlo Simulation Method The third approach to estimating VAR is Monte Carlo simulation. In general, Monte Carlo simulation produces random outcomes so we can examine what might happen given a particular set of risks. It is used widely in the sciences as well as in business to study a variety of problems. In the financial world in recent years, it has become an extremely important technique for measuring risk. Monte Carlo simulation generates random outcomes according to an assumed probability distribution and a set of input parameters. We can then analyze these outcomes to gauge the risk associated with the events in question. When estimating VAR, we use Monte Carlo simulation to produce random portfolio returns. We then assemble these returns into a summary distribution from which we can determine at which level the lower 5 percent (or 1 percent, if preferred) of return outcomes occur. We then apply this figure to the portfolio value to obtain VAR. Monte Carlo simulation is often the only practical means of generating the information necessary to manage the risk. With tens of thousands of transactions on the books of most dealers, however, Monte Carlo simulation can require extensive commitments of computer resources. e. “Surplus at Risk”: VAR as It Applies to Pension Fund Portfolios The difference between the value of the pension fund’s assets and liabilities is referred to as the surplus, and it is this value that pension fund managers seek to enhance and protect. If this surplus falls into negative territory, the plan sponsor must contribute funds to make up the deficit over a period of time that is specified as part of the fund’s plan. In order to reflect this set of realities in their risk estimations, pension fund managers typically apply VAR methodologies not to their portfolio of assets but to the surplus. To do so, they simply express their liability portfolio as a set of short securities and calculate VAR on the net position. VAR handles this 6
process quite elegantly, and once this adjustment is made, all three VAR methodologies can be applied to the task. 3- The Advantages and Limitations of VAR Although value at risk has become the industry standard for risk assessment, it also has widely documented imperfections: 1) VAR can be difficult to estimate, and different estimation methods can give quite different values. 2) VAR can also lull one into a false sense of security by giving the impression that the risk is properly measured and under control. 3) VAR often underestimates the magnitude and frequency of the worst returns, although this problem often derives from erroneous assumptions and models. 4) VAR for individual positions does not generally aggregate in a simple way to portfolio VAR. 5) Also, VAR fails to incorporate positive results into its risk profile, and as such, it arguably provides an incomplete picture of overall exposures. Advantages of VAR: 1) VAR has the attraction of quantifying the potential loss in simple terms and can be easily understood by senior management. 2) Another advantage of VAR is its versatility. 4- Extensions and Supplements to VAR
Incremental VAR measures the incremental effect of an asset on the VAR of a portfolio by measuring the difference between the portfolio’s VAR while including a specified asset and the portfolio’s VAR with that asset eliminated. Some variations of VAR are cash flow at risk (CFAR) and earnings at risk (EAR). CFAR and EAR measure the risk to a company’s cash flow or earning, respectively, instead of its market value as in the case of VAR. CFAR is the minimum cash flow loss that we expect to be exceeded with a given probability over a specified time period. EAR is defined analogously to CFAR but measures risk to accounting earnings. CFAR and EAR can be used when a company (or portfolio of assets) generates cash flows or profits but cannot be readily valued in a publicly traded market, or when the analyst’s focus is on the risk to cash flow and earnings, for example, in a valuation. CFAR and EAR can complement VAR’s perspective on risk. Another useful tool to supplement VAR is the tail value at risk (TVAR), also known as the conditional tail expectation. TVAR is defined as the VAR plus the expected loss in excess of VAR, when such excess loss occurs.
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5- Stress Testing Managers often use stress testing to supplement VAR as a risk measure. The main purpose of VAR analysis is to quantify potential losses under normal market conditions. Stress testing, by comparison, seeks to identify unusual circumstances that could lead to losses in excess of those typically expected. Clearly, different scenarios will have attached probabilities of occurring that vary from the highly likely to the almost totally improbable. It is, therefore, the natural complement to VAR analysis. Two broad approaches exist in stress testing: scenario analysis and stressing models. a. Scenario Analysis Scenario analysis is the process of evaluating a portfolio under different states of the world. Quite often it involves designing scenarios with deliberately large movements in the key variables that affect the values of a portfolio’s assets and derivatives. One type of scenario analysis, that of stylized scenarios, involves simulating a movement in at least one interest rate, exchange rate, stock price, or commodity price relevant to the portfolio. These movements might range from fairly modest changes to quite extreme shifts. One problem with the stylized scenario approach is that the shocks tend to be applied to variables in a sequential fashion. In reality, these shocks often happen at the same time, have much different correlations than normal, or have some causal relationship connecting them. Another approach to scenario analysis involves using actual extreme events that have occurred in the past. We might also create scenarios based on hypothetical events—events that have never happened in the markets or market outcomes to which we attach a small probability. These types of scenarios are very difficult to analyze and may generate confusing outcomes, so it is important to carefully craft hypothetical analyses if they are to generate information that adds value to the risk management processes. b. Stressing Models Given the difficulty in estimating the sensitivities of a portfolio’s instruments to the scenarios we might design, another approach might be to use an existing model and apply shocks and perturbations to the model inputs in some mechanical way. This approach might be considered more scientific because it emphasizes a range of possibilities rather than a single set of scenarios, but it will be more computationally demanding. It is also possible to glean some idea of the likelihood of different scenarios occurring. The simplest form of stressing model is referred to as factor push, the basic idea of which to is to push the prices and risk factors of an underlying model in the most disadvantageous way and to work out the combined effect on the portfolio’s value. But factor push also has its limitations and difficulties— principally the enormous model risk that occurs in assuming the underlying model will function in an extreme risk climate. 8
Other approaches include maximum loss optimization—in which we would try to optimize mathematically the risk variable that will produce the maximum loss— and worst-case scenario analysis—in which we can examine the worst case that we actually expect to occur. 6- Measuring Credit Risk Credit risk is present when there is a positive probability that one party owing money to another will renege on the obligation. If the defaulting party has insufficient resources to cover the loss or the creditor cannot impose a claim on any assets the debtor has that are unrelated to the line of business in which the credit was extended, the creditor can suffer a loss. A creditor might be able to recover some of the loss, perhaps by having the debtor sell assets and pay the creditors a portion of their claim. Credit losses have two dimensions: the likelihood of loss and the associated amount of loss In the risk management business, exposure must often be viewed from two different time perspectives. We must assess first the risk associated with immediate credit events and second the risk associated with events that may happen later. With respect to credit, the risk of events happening in the immediate future is called current credit risk (or, alternatively, jump-to-default risk); it relates to the risk that amounts due at the present time will not be paid. Assuming, however, that the counterparty is solvent and that it will make the current payment with certainty, the risk remains that the entity will default at a later date. This risk is called potential credit risk, and it can differ quite significantly from current credit risk. Another element of credit risk, which blends current and potential credit risk, is the possibility that a counterparty will default on a current payment to a different creditor. Most direct lending or derivative-based credit contracts stipulate that if a borrower defaults on any outstanding credit obligations, the borrower is in default on them all (this is known as a cross-default provision). VAR is also used, albeit with greater difficulty, to measure credit risk. This measure is sometimes called credit VAR, default VAR, or credit at risk. Like ordinary VAR, it reflects the minimum loss with a given probability during a period of time. To accurately measure credit VAR, a risk manager must focus on the upper tail of the distribution of market returns, where the return to the position is positive, in contrast to market risk VAR, which focuses on the lower tail. a. Option-Pricing Theory and Credit Risk Option theory enables us to better understand the nature of credit risk. In this section, we will see that the stock of a company with leverage can be viewed as a call option on its assets. This approach will lead to the result that a bond with credit risk can be viewed as a default-free bond plus an implicit short put option written by the bondholders for the stockholders. Consider a company with assets with a market value of 𝐴0 and debt with a face value of 𝐹. The debt is in the form of a single zero-coupon bond due at time 𝑇. The bond’s market value is 𝐵0 . Thus the stock’s market value is 9
𝑆0 = 𝐴0 − 𝐵0 At time 𝑇, the assets will be worth 𝐴 𝑇 and the company will owe 𝐹.
Notice that the payoffs to the stockholders resemble those of a call option in which the underlying is the assets, the exercise price is F, and the option expires at time T, the bond maturity date. Indeed, the stock of a company with a single zero-coupon bond issue is a call option on the assets. To better understand the nature of stock as a call option, let us recall the concept of put–call parity, where 𝑝0 + 𝑆0 = 𝑐0 + 𝑋/(1 + 𝑟)𝑇 or in the current framework 𝑝0 + 𝐴0 = 𝑆0 + 𝐹/(1 + 𝑟)𝑇
Replacing 𝐴0 = 𝑆0 + 𝐵0 ; the bond’s market value must then be: 𝐵0 = (1
𝐹 + 𝑟)𝑇
− 𝑝0
The first term on the right-hand side is equivalent to a default-free zero-coupon bond paying F at maturity. The second term is a short put. The bondholders’ claim, which is subject to default, can thus be viewed as a default-free bond and a short put on the assets. In other words, the bondholders have implicitly written the stockholders a put on the assets. From the stockholders’ perspective, this put is their right to fully discharge their liability by turning over the assets to the bondholders, even though those assets could be worth less than the bondholders’ claim. In legal terminology, this put option is called the stockholders’ right of limited liability. 10
b. The Credit Risk of Forward Contracts Recall that forward contracts involve commitments on the part of each party. No cash is due at the start, and no cash is paid until expiration, at which time one party owes the greater amount to the other. The party that owes the larger amount could default, leaving the other with a claim of the defaulted amount. Each party assumes the other’s credit risk. Prior to expiration, no current credit risk exists, because no current payments are owed, but there is potential credit risk in connection with the payments to be made at expiration. Current credit risk arises when the contract is at its expiration. Below we will examine how potential credit risk changes during the life of the contract as the value of the underlying changes. From the perspective of a given party, a forward contract’s market value can be easily calculated as the present value of the amount owed to the party minus the present value of the amount it owes. So, the market value at a given time reflects the potential credit risk. Recall that the value of a contract at time 𝑡 is 𝑉𝑡 (0, 𝑇) = 𝑆𝑡 −
𝐹(0,𝑡) (1+𝑟)(𝑇−𝑡)
And the forward price is 𝐹(0, 𝑇) = 𝑆0 (1 + 𝑟)𝑇
c. The Credit Risk of Swaps A swap is similar to a series of forward contracts. The periodic payments associated with a swap imply, however, that credit risk will be present at a series of points during the contract’s life. As with forward contracts, the swap’s market value can be calculated at any time and reflects the present value of the amount at risk for a credit loss (i.e., the potential credit risk). The credit risk of swaps can vary greatly across product types within this asset class and over a given swap’s lifetime. For interest rate and equity swaps, the potential credit risk is largest during the middle period of the swap’s life. One exception to this pattern involves currency swaps, which often provide for the payment of the notional principal at the beginning and at the end of the life of the transaction. Because the notional principal tends to be a large amount relative to the payments, the potential for loss caused by the counterparty defaulting on the final notional principal payment is great. Thus, whereas interest rate swaps have their greatest credit risk midway during the life of the swap, currency swaps have their greatest credit risk between the midpoint and the end of the life of the swap. The value of a swap is 𝑝𝑟𝑒𝑠𝑒𝑛𝑡 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝑡ℎ𝑒 𝑐𝑎𝑠ℎ 𝑟𝑒𝑐𝑒𝑖𝑣𝑒𝑑 – 𝑡ℎ𝑒 𝑝𝑟𝑒𝑠𝑒𝑛𝑡 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝑡ℎ𝑒 𝑐𝑎𝑠ℎ 𝑝𝑎𝑖𝑑 d. The Credit Risk of Options Options, on the other hand, have unilateral credit risk. The buyer of an option pays a cash premium at the start and owes nothing more unless, under the buyer’s sole discretion, he decides to exercise the 11
option. Once the premium is paid, the seller assumes no credit risk from the buyer. Instead, credit risk accrues entirely to the buyer and can be quite significant. If the buyer exercises the option, the seller must meet certain terms embedded in the contract. If the option is a call, the seller must deliver the underlying or pay an equivalent cash settlement. If the option is a put, the seller must accept delivery of the underlying and pay for it or meet these obligations in the form of cash payments. Derivatives’ credit risk can be quite substantial, but this risk is considerably less than that faced by most lenders. When a lender makes a loan, the interest and principal are at risk. The loan principal corresponds closely to the notional principal of most derivative contracts. With the exception of currency swaps, the notional principal is never exchanged in a swap. Even with currency swaps, however, the risk is much smaller than on a loan. If a counterparty defaults on a currency swap, the amount owed to the defaulting counterparty serves as a type of collateral because the creditor is not required to pay it to the defaulting party. Therefore, the credit risk on derivative transactions tends to be quite small relative to that on loans. 7- Liquidity Risk One of the implicit assumptions in risk management with VAR is that positions can be liquidated when they approach or move outside pre-agreed risk limits. In practice, some assets are far more liquid than others and practitioners will often liquidity-adjust VAR estimates accordingly. Wide bid–ask spreads in proportion to price are an obvious measure of the cost of trading an illiquid instrument or underlying security. But some instruments simply trade very infrequently at any price—a far more complex problem, because infrequently quoted prices often give the statistical illusion of low or lower volatility. This dynamic is counterintuitive, because we would expect instruments that are illiquid to have a higher bid–ask spread and higher volatilities. E- Managing Risk The different components of managing risk include: An effective risk governance model, which places overall responsibility at the senior management level, allocates resources effectively and features the appropriate separation of tasks between revenue generators and those on the control side of the business. Appropriate systems and technology to combine information analysis in such a way as to provide timely and accurate risk information to decision makers. Sufficient and suitably trained personnel to evaluate risk information and articulate it to those who need this information for the purposes of decision making. 1- Managing Market Risk a. Risk Budgeting Risk budgeting focuses on questions such as, “Where do we want to take risk?” and “What is the efficient allocation of risk across various units of an organization or investment opportunities?” Risk budgeting is relevant in both an organizational and a portfolio management context. 12
To take an organizational perspective first, risk budgeting involves establishing objectives for individuals, groups, or divisions of an organization that take into account the allocation of an acceptable level of risk. A well-run risk-taking enterprise manages these limits carefully and constantly monitors their implementation. Any excesses are reported to management immediately for corrective action. Under this type of regime, management can compare the profits generated by each unit with the amount of capital and risk employed. The point about risk budgeting is that it is a comprehensive methodology that empowers management to allocate capital and risk in an optimal way to the most profitable areas of a business, taking account of the correlation of returns in those business areas. 2- Managing Credit Risk a. Reducing Credit Risk by Limiting Exposure Limiting the amount of exposure to a given party is the primary means of managing credit risk. Banks have regulatory constraints on the amount of credit risk they can assume, which are specified in terms of formulas. b. Reducing Credit Risk by Marking to Market One device that the futures market uses to control credit risk is marking tradable positions to market. The OTC derivatives market also uses marking to market to deal with credit risk: Some OTC contracts are marked to market periodically during their lives. Marking to market is usually done only with contracts with two-way credit risk. Option credit risk is normally handled by collateral. c. Reducing Credit Risk with Collateral The posting of collateral is a widely accepted credit exposure mitigant in both lending and derivatives transactions. One very prominent example of its use comes from futures markets, which require that all market participants post margin collateral. d. Reducing Credit Risk with Netting One of the most common features used in two-way contracts with a credit risk component, such as forwards and swaps, is netting. e. Reducing Credit Risk with Minimum Credit Standards and Enhanced Derivative Product Companies As noted above, the first line of defense against credit risk is limiting the amount of business one party engages in with another. An important and related concept is to ensure that all credit-based business is undertaken with entities that have adequate levels of credit quality. The historical standard measures 13
for such credit quality come from rating agencies such as Moody’s Investors Service and Standard & Poor’s. Many derivatives dealers have taken action to control their exposure to rating downgrades. One such action is the formation of a type of subsidiary that is separate from the dealer’s other activities. These subsidiaries are referred to as enhanced derivatives products companies (EDPCs), sometimes known as special purpose vehicles (SPVs). These companies are usually completely separate from the parent organization and are not liable for the parent’s debts. They tend to be very heavily capitalized and are committed to hedging all of their derivatives positions. As a result of these features, these subsidiaries almost always receive the highest credit quality rating by the rating agencies. In the event that the parent goes bankrupt, the EDPC is not liable for the parent company’s debts; if the EDPC goes under, however, the parent is liable for an amount up to its equity investment and may find it necessary to provide even more protection. Hence, an EDPC would typically have a higher credit rating than its parent. In fact, it is precisely for the purpose of obtaining the highest credit rating, and thus the most favorable financing terms with counterparties, that banks and broker dealers go through the expense of putting together EDPCs. f.
Transferring Credit Risk with Credit Derivatives
In a credit default swap, the protection buyer pays the protection seller in return for the right to receive a payment from the seller in the event of a specified credit event. In a total return swap, the protection buyer pays the total return on a reference obligation (or basket of reference obligations) in return for floating-rate payments. If the reference obligation has a credit event, the total return on the reference obligation should fall; the total return should also fall in the event of an increase in interest rates, so the protection seller (total return receiver) in this contract is actually exposed to both credit risk and interest rate risk. A credit spread option is an option on the yield spread of a reference obligation and over a referenced benchmark (such as the yield on a specific default-free security of the same maturity); by contrast, a credit spread forward is a forward contract on a yield spread.
Credit derivatives may be used not only to eliminate credit risk but also to assume credit risk. 3- Performance Evaluation Following is a list of standard methodologies for expressing return in units of exposure assumption: Sharpe Ratio 𝑆ℎ𝑎𝑟𝑝𝑒 𝑟𝑎𝑡𝑖𝑜 =
𝑀𝑒𝑎𝑛 𝑝𝑜𝑟𝑡𝑓𝑜𝑙𝑖𝑜 𝑟𝑒𝑡𝑢𝑟𝑛 – 𝑅𝑖𝑠𝑘 − 𝑓𝑟𝑒𝑒 𝑟𝑎𝑡𝑒 𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑝𝑜𝑟𝑡𝑓𝑜𝑙𝑖𝑜 𝑟𝑒𝑡𝑢𝑟𝑛
Risk-Adjusted Return on Capital (RAROC) This concept divides the expected return on an investment by a measure of capital at risk, a measure of the investment’s risk that can take a number of different forms
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and can be calculated in a variety of ways that may have proprietary features. The company may require that an investment’s expected RAROC exceed a RAROC benchmark level for capital to be allocated to it. Return over Maximum Drawdown (RoMAD): Drawdown, in the field of hedge fund management, is defined as the difference between a portfolio’s maximum point of return (known in industry parlance as its “high-water” mark), and any subsequent low point of performance. Maximum drawdown is the largest difference between a high-water and a subsequent low. Return over maximum drawdown is simply the average return in a given year that a portfolio generates, expressed as a percentage of this drawdown figure. Sortino Ratio 𝑆𝑜𝑟𝑡𝑖𝑛𝑜 𝑟𝑎𝑡𝑖𝑜 =
(𝑀𝑒𝑎𝑛 𝑝𝑜𝑟𝑡𝑓𝑜𝑙𝑖𝑜 𝑟𝑒𝑡𝑢𝑟𝑛 − 𝑀𝐴𝑅 ) 𝐷𝑜𝑤𝑛𝑠𝑖𝑑𝑒 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛
4- Capital Allocation 1) Nominal, Notional, or Monetary Position Limits: Under this approach, the enterprise simply defines the amount of capital that the individual portfolio or business unit can use in a specified activity, based on the actual amount of money exposed in the markets. 2) VAR-Based Position Limits As an alternative or supplement to notional limits, enterprises often assign a VAR limit as a proxy for allocated capital. 3) Maximum Loss Limits Irrespective of other types of limit regimes that it might have in place, it is crucial for any risk-taking enterprise to establish a maximum loss limit for each of its risk-taking units. 4) Internal Capital Requirements Internal capital requirements specify the level of capital that management believes to be appropriate for the firm. 5) Regulatory Capital Requirements In addition, many institutions (e.g., securities firms and banks) must calculate and meet regulatory capital requirements. 5- Psychological and Behavioral Considerations The main factor to consider from a risk management perspective is the importance of establishing a risk governance framework that anticipates the points in a cycle when the incentives of risk takers diverge from those of risk capital allocators.
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