Collateral ManagementOptimization, Efficiency & Effectiveness

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Collateral ManagementOptimization, Efficiency & Effectiveness

Kishor Chandra Das


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IntroductionWhat is Collateral Optimization? The answer to this question varies from person to person

depending on where they are in the value chain (Tabb, 2012). Seagroatt s answer to this question is

centralization of collateral management and transformation of the practice from back-office

operations function to a profit centerand Bastide says best deployment of the available collateral

to meet the margin requirements and to maximize the return on securities . In recent years there

have been numerous articles and white papers published on Collateral Optimization. The recent

white papers by Seagroatt (2012), Pery (2012) and Bastide (2012) discuss and explain the different

techniques of optimizing collateral allocation. Although these articles are good sources of knowledge

to understand the changing landscape of collateral management practice but they did not discuss

the difficulties and challenges of implementing the optimization techniques. This paper takes the

literature one step forward, from concepts to its logic and practical implementation.

Lets first look at the compelling forces behind collateral management practice and then come back

to the topic of optimization. Collateral management as a practice was started in 1980s primarily to

mitigate counterparty risk. In the early days collateral agreements were unilateral agreements,

where only the counterparty with weaker credit worthiness was required to post collateral. But now,

in the bilateral collateral agreements both counterparties have to post collateral depending on the

market movement. The bilateral collateral agreements reduce the counterparty risk but also add to

contractual obligation to post collaterals on time. Over the past 20 years it has evolved as a cross

functional operations involving complex interrelated processes and multiple parties. In todays

context, there are four broad level driving forces behind collateral management. First one is the

need for managing counterparty risk and the second force is the regulatory requirement. The

regulatory bodies also force firms to put in place adequate collateral management processes. The

Dodd-Frank Act in US and the EMIR in Europe require firms to centrally clear derivatives contracts,

which require managing the initial margin and variation margin, to mitigate systemic risk. As most of

the financial transactions now are required to be secured by collateral, the pressure on the treasury

operations is increasing to effectively manage the liquidity. Optimization of the treasury function is

the third force behind need for collateral management. The fourth force is driven by the business

opportunity. Custody banks, CCPs and prime brokers offer collateral management as a service to

their clients. Smaller firms who do not have the resources to do collateral management in-house,

they outsource this service to their custodians, PBs and CCPs. As these compelling reasons are not

independent of each other, their complex interrelationship makes collateral management evenmore complex.


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Diagram: 1

Optimization TechniquesCollateral Optimization is all about appropriate allocation of firm s collateral in the most effective

and efficient manner to achieve the business objectives. Optimization techniques are still in evolving

stage adapting the new regulatory changes and the market dynamics. The forward thinking players

in the collateral management space are implementing more advanced and sophisticated

optimization techniques in preparation for the upcoming regulatory changes whereas others are

trying to bring efficiency in their basic optimization processes. This trend is very much evident from

the ISDA margin survey, where the results show that only 21% of the firms do collateral optimization

on a daily basis but 71% of the large firms do optimization on daily basis. Many of the optimization

techniques are simple and easy to implement in daily operations. The basic levels of optimization

include

Bilateral Netting: A netting agreement allows two parties to net exposures arising from a set of

trades. A single margin call between the two parties simplifies the collateral flow, reduces the

settlement risk and improves the overall operational efficiency. An ISDA Master Agreement or

similar agreements is the approach to establish netting agreements that governs the set of

transactions covered under the netting agreement. Seagrott (2012) considers netting as an advance

level of optimization technique. But a simple netting agreement is a very common practice in OTC

derivatives market between two counterparties. The advance levels of netting includes cross

product, multi-entity and multilateral agreements, which are discussed in the later section.


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Re-hypothecation: Reuse of collateral through re-hypothecation was very widely used by all

participants prior to the credit crisis in 2008. Primarily the prime brokers were re-hypothecating the

collateral received from one counterparty with another counterparty to reduce their funding costs.

But this technique creates a complicated chain of re-hypothecation. When the owner wants the

assets to be returned it becomes difficult to get the assets back in the chain, if anyone in the chain

fails to get the asset back then the reverse process fails. The bankruptcy of Lehman brothers brought

the potential problems of re-hypothecation to the forefront. A large volume of collateral posted by

clients to Lehman Brothers was never returned back to their owners. So, the risk in the event of

bankruptcy that the re-hypothecated collateral may not be returned to the original pledger was well

understood in the market and the buy-side participants refrained from signing re-hypothecation

agreements. Although there has been a significant drop in re-hypothecation but such agreements

are still in use. In order to efficiently reuse the client collaterals for re-hypothecation, the inventory

management system must be able to differentiate client assets that are eligible for re-hypothecation

from those are not eligible. At present, most of it is managed manually and through excel

spreadsheets. Automation of this process will significantly improve the efficiency of reusing client

collateral.

Posting Lowest Grade Collateral: High grade collateral is required for managing liquidity and

maintaining the regulatory capital. These type of collateral are highly liquid and always in demand,

such as USD, GBP, SFR, Gold etc. Usually firms try to hold the highest grade collateral and post the

lowest grade eligible collateral for margin calls. In order to achieve this level of optimization, firms

must capture the eligible collaterals in their collateral agreements system and manage the grades of

their collateral in the reference data system. Grading of collateral assets can be done based on the

following fundamental characteristics (i) Credit and Market Risks (ii) Certainty of market values for

the assets (iii) Liquidity and market concentration and (iv) correlation with other risky assets. The

quality of collateral assets may change over time due the above characteristics. The reference data

systems must be able to capture these lifecycle changes of the collateral assets in order to

accurately maintain the grading of the asset. This optimization technique has its own disadvantages,

posting the lowest grade collateral does not mean that its the most cost effective solution. Low

grade collateral assets usually have higher haircuts. Posting the lowest grade collateral means

posting higher volume of the asset to meet the margin call and this may increase the funding costs

and the transaction costs. Some of the advance level optimization techniques, discussed in the

following sections, address this problem to some extent.

Cheapest to Fund: In multi-currency collateral agreement, posting one currency may be cheaper

than posting any of the other eligible currencies. For example if EUR, GBP and USD are the eligible


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currencies in the collateral agreement, then posting EUR would be cheaper in this case, as per the

information in the following table

Eligible Currency Borrowing Rate Collateral Interest Funding Rate

EUR 0.5% 1.0% -0.5%

GBP 1.0% 1.0% 0.0%

USD 1.5% 1.0% 0.5%

In multi asset class collateral agreements, posting other asset classes as collateral may be cheaper

than posting currencies. For example, a bank may find it cheaper to post bonds as collateral whereas

posting cash collateral may be cheaper for a pension fund. The basic level of cheapest to fund

optimization is limited to new margin requirements. Substitutions require advance level of

optimization, which is discussed in the below.

The global financial firms operate in a multi-jurisdiction and multi-entity structure offering complex

financial products. The basic optimization techniques, as discussed above, are not so effective in

such complex operating structure. Basic optimization techniques are applied in silo for each of the

SBUs, which do not exploit the potential of enterprise level optimization. The term Enterprise

Collateral Management refers to the advance level of collateral optimization at firm-wide level,

which includes

Firm-wide Collateral Inventory:

A firm-wide view of collateral inventory is the basic building block for efficient enterprise collateral

management. Without the firm-wide view of the inventory it wont possible to achieve collateral

optimization. In most firms today, the firm-wide inventory view is prepared on excel sheets which is

a manual process, prone to error and may not be presenting the real-time state. A centralized real-

time inventory system integrated to front, middle and back office systems for all asset classes and all

SBUs is essential for real-time optimization of collateral assets. With the new regulatory changes and

introduction of central counterparties, intra-day margin calls are going to be very common. In order

to post collaterals for the intra-day margin calls, the collateral team must have a real-time view of

the assets. The inventory view should also include client collaterals, re-hypothecation eligible

collaterals; firms collateral in possession, firms collateral posted to counterparties etc. All other

advanced optimization steps are very much reliant on the real-time and accurate view of the

collateral assets.


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Multi-entity, Cross Product & Multilateral Netting:

Most of the global counterparties have multiple legal entities in different geographies and across

different product classes. The market has seen the complexity of this in the Lehman Brother

bankruptcy case, where many clients and counterparties found themselves exposed to multiple

Lehman Brothers entities in various legal jurisdictions with different bankruptcy and insolvency laws

and contractual protections and remedies. It is essential for firms to accurately capture the

agreement information relating to the legal entities to whom they are exposed and the legal

jurisdictions in which their assets reside and monitor the real-time exposure at entity level and

portfolio level. Frequent reconciliation of exposure and collaterals at portfolio level is a solution for

making collateral operations for efficient and effective in managing counterparty risks. Multilateral

netting further enhances the efficiency of counterparty risk management. Netting the exposure with

in a group of firms will reduce the number of margin calls and number of transactions for posting

collaterals. Central counterparties are best positioned to carry out multilateral netting. The

illustration below shows the benefit of moving from bilateral netting to multilateral netting.

Cheapest-to-Deliver:

Cheapest-to-Deliver calculation engine is the most complex and complicated part of the

optimization. Some refer the cheapest funding calculation as cheapest to deliver but there is a huge

difference in these two logics. Cheapest-to-Deliver optimization process would take into

consideration all MSAs, their respective constraints as defined in the CSAs, funding costs for sourcing


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the assets, internal transfer costs for using firms assets, transaction costs, substitution costs, and

liquidity levels all together in order to identify cheapest-to-deliver collateral from the enterprise

perspective. A simple sequential collateral selection for each agreement will not be sufficient for

delivering the best optimization; the relative cost of selected collateral must be compared among all

agreements to decide the best optimized selection. To be efficient, the optimization engine must be

integrated with other systems to initiate the relevant margin calls, collateral booking and

substitutions rather than having to rely on separate systems.

Forecasting, Auto Allocation & Substitution:

Having assets in the inventory may not be sufficient to meet the margin calls as not all assets will be

eligible as collateral in all circumstances. So, the securities inventory system needs to be integrated

with the collateral agreement system to filter inventory assets based on collateral eligibility for each

of the counterparties. Forecasting for collateral requirements requires inventory information, CSA

information, mark-to-market positions and exposure information. Projecting any shortfall of eligible

collaterals for the anticipated margin calls can improve the efficiency of managing margin calls and it

can also improve the effectiveness of sourcing the eligible collaterals at lower costs. Auto allocation

logic when applied on the anticipated margin calls, it can reserve the eligible collateral from the

inventory but the traders will not be able to trade on that asset. If the auto allocation logic is applied

at the event of receiving the margin calls then projected shortfalls needs to be managed in

compresses timeframes. More efficient auto allocation logic can reserve collaterals for anticipated

margin calls, allow the traders to trade on reserved assets and if the reserved assets are traded in

front office then it can reserve the next best collateral for the anticipated margin call. Substitution

logic adds a great amount of complexity to the optimization techniques. The complexity is added

because the collateral calculation is to be done not only for the new margin calls but for the entire

exposure for each of the counterparties, posted collaterals need also to be considered as eligible

assets in the inventory and transaction cost of substitutions has to be calculated for each of the

assets. Even if a substitution may look cheaper but more substitutions will result in higher

transaction costs and increase the overall operating cost of collateral management. Appropriate cost

efficiency metrics should be monitored to keep the operating costs on check.


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Efficiency & Effectiveness MetricsAll the above optimization techniques are employed to improve the efficiency as well as the

effectiveness of collateral operations. Measuring the metrics that are affected by these optimizationtechniques and observing the trends, pre and post optimization, can determine the degree of

success of employing these techniques. Effectiveness can be measured through the business

outcomes where as efficiency can be measured through the cost, time and effort metrics. Some of

the main metrics for monitoring the performance of collateral management operations are

Collateralization Ratio: Post the 2008 crisis, collateralization is applied to all types of derivative

transactions. Degree of Collateralization or the Collateralization Ratio shows to what extent

counterparties and the derivatives trades are covered under the collateral agreements. The first

level of metric can be to monitor the ratio of number of active collateral agreements against the

number of active trading counterparties and other related metric can be to monitor the ratio of all

collateralized trades to total number of derivatives trades. More granular level of monitoring will

also be required at each SBU level, geography level and at legal entity level.

Distribution of Eligible Collateral: Having financial assets that cannot be posted as collateral reduces

the liquidity level in the firm. For example, if a firm has Cash, Equities and Bonds in its inventory and

if Cash is the only eligible asset for collateral posting then the firms capital locked in equities and

bonds. Another example, if cash is eligible is eligible collateral for all the trades and equities are

eligible for 10% of the trades then also the liquidity become limited as equities cannot be used as

collateral for 90% of the trades and bonds cannot be used in any of the trades. The examples taken

here are simple to explain the problem but in reality eligibility monitoring will be at instrument level

rather than at asset class level. Understanding the distribution of eligible collateral can help in 2

ways 1) maintaining the appropriate level of asset inventory 2) increasing the asset coverage in the

collateralization agreements.

Margin Calls: Managing the margin calls is one of the core functions of collateral management

teams. Monitoring the number of incoming and outgoing margin calls is required to do the capacity

planning and to analyze the operational efficiency. Calls per member of the margin team and calls

per geography are granular level of metrics to monitor the capacity utilization, which indicates the

efficiency of the team and individuals. Daily margin amount is another related metric but the

amount itself does not give any indication of efficiency or effectiveness. The comparison of margin

amount with the dispute amount gives an indication of effectiveness of the processes.


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Dispute Ratio: There are 2 type dispute ratios, one is on the incoming margin calls and the other is

on the outgoing margin calls. These two ratios are further divided in to a count (#) ratio and an

amount ($) ratio. The number of disputed margin calls over the total number of margin calls is the

dispute ratio (#) and the total disputed margin amount over the total margin amount is the dispute

ratio ($). These 2 ratios, when calculated for incoming margin calls and the outgoing margin calls,

determine the effectiveness of the margin process. At the most granular level, these ratios

determine the effectiveness of the process with each of the counterparties and identify the

ineffective process steps.

Dispute Resolution Cycle: Disputes can arise from difference in valuation, missing trades and

ineligible collateral. For incoming margin calls the dispute resolution cycle is the time between

receiving the margin call and acknowledgement of the resolution for the dispute. For outgoingmargin calls the resolution cycle is the time between receiving the dispute call from the

counterparty and acknowledgement of the resolution for the margin call. ISDA defines T+3 (and T+4

cross continental counterparties) as the standard for resolution of disputes. The dispute resolution

process includes many process steps, tracking the time for each of the steps would be required to

appropriately address any inefficiency in the process.

Margin Call Cycle: For incoming margin calls the cycle time is the time between receiving the margin

call and acknowledgement for the settlement. Many would argue that the cycle time is between

receiving the margin call and posting the collateral, but there still some process steps after the

collateral posting that can lead to disputes. The cycle time between margin call and collateral

posting is the margin processing time and the cycle time between collateral posting and settlement

acknowledgement is the settlement time. The cycle time between margin call and settlement

acknowledgement is the aggregated view of margin processing time and settlement time and the

aggregated metric would be more effective for managing the end-to-end margin process. For

outgoing margin calls, the time between issuing the margin and settlement of the collateral is the

margin cycle time. The aggregated view the metric must also include the disputed margin calls to

assess the skew because of the disputes.

Margin Call Timeliness: The percentage of margin calls issued on time is the metric for margin call

timeliness. The timeliness can be affected by any delays from any of the dependant systems, such as

delay in M-T-M or delay in valuation or delay in receiving the price feed etc. Timeliness of margin

calls is very important for managing the counterparty exposure. If the margin calls are delayed the

firm will have higher exposure to the counterparties for a period of time that leads to higher risks. Toassess the business impact of any delays in the margin calls, another metric that would be useful is


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the Margin Value Timeliness. This is defined as the ratio of total amount of margin calls issued on

time divided by the total value of margin calls.

Margin Call Efficiency: This metric is applicable for the outgoing margin calls. The time taken for

issuing the margin calls after receiving all the dependent information from the upstream processes is

the metric for margin call efficiency. Analyzing the two metrics, margin call timeliness and margin

call efficiency, together can identify the weak processes that are within the collateral management

operations and outside, in the upstream processes.

Type-I & Type-II Errors in Margin Calls Received (MCR Type-I and Type-II): Type-I errors are those

margin calls which were not expected as per the firms calculation engine but found to be valid

margin calls after dispute investigation. These errors can cause serious problems in managing the

day-to-day liquidity of the firms. If these margin calls were not forecasted then the firms may not

have sufficient eligible collateral to post against these margin calls. These errors can lead to

operational inefficiency as the dispute resolution process is costly and time consuming. Type-II

errors are those expected margin calls, which were never received from the counterparty. Severity

of these errors are less compared to the Type-I errors but monitoring these errors can help

identifying process inaccuracies and improving it.

Type-I & Type-II Errors in Margin Calls Issued (MCI Type-I and Type-II): These errors are equivalent

to the errors that we discussed above but in the margin issue process. Type-I errors are those issued

margin calls where the problem was at the firms end and Type-II errors are at the counterparties

end. Both these errors are equally critical as both situations will require the dispute resolution

process to be invoked. Fixing the Type-I errors will help improving the firms calculation accuracy and

analysis of the Type-II errors will not only identify the most erroneous counterparties but also help

improving the calculation accuracy at the counterparty end.

Margin Calc Inaccuracy: This inaccuracy metric is derived from the calculated margin amount. This issum of the disputed amount over the total margin amount as calculated by the calc engine. Again

these can be two sub metrics, one for the internal inaccuracy and the other for counterparties

inaccuracy.

Lets take a simple example to understand this metric. The firm issues 2 margin calls of $ 500,000

and $ 300,000 and the second margin call ($ 300,000) was disputed by the counterparty and after

the dispute resolution the agreement with the counterparty was to pay $ 250,000. Also the firm


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received 1 unexpected margin call from a counterparty for amount $ 400,000, which was disputed

by the firm. After dispute resolution the firm agreed to pay $300,000. In this case the calculation will

be

1

The metrics discussed above are some of the main metrics required for monitoring the performance

of the collateral management operations. The cost efficiency metrics are not covered in this paper,

which are primarily dependent on the structure of the collateral management team, their

geographic spread and the infrastructure and transaction costs. In a practical world, firms must have

to establish their own set of metrics at a very granular level and also aggregated at the firm level,

which can be derived from the metrics discussed above, to measure effectiveness and efficiency

(cost, timeliness and processing time) for their internal benchmarking and also for benchmarking

against the industry. Optimization techniques are implemented to improve both, effectiveness and

efficiency. Relating the impacted metrics with each of the implemented optimization techniques and

measuring the metrics can help in determining the improvements brought by the optimizations.

ConclusionCollateral Management has come a long way since its inception in 1980s, from a simple back office

operation to a complex cross functional business operation. As the new market environment is

shaping up, both buy side and sell side firms need to define new processes for operating their

businesses, managing their counterparty risk, managing adequate level of liquidity, and adopting

changes for the central counterparty clearance. With the changing market environment collateral

optimization techniques are also evolving. The basic optimization techniques are applied in silo at

SBU levels but the advanced optimization techniques require integration of systems and processes

at the enterprise level. As the processes involved in enterprise collateral management are well

beyond the margin calls and span across different business functions, from the legal aspects of

1Although the received margin call is for $400,000 but it was an unexpected margin call (MCR Type-I Error)


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collateralization to risk management and liquidity management, it requires appropriate metrics and

measurement techniques to track and monitor the performance of each process. Industry

benchmarking of the process metrics, with participation from buy side firms as well as the sell side

firms, would be a good step forward in establishing maturity level of the optimizations.


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ReferencesBastide, C. d. (2012). Collateral Management in the Front Office. Mysis.

(2010). Best Practices for the OTC Derivatives Collateral Process. ISDA.

Egmond, J. v. (2011). The Impact of Collateralization on Swap Curves and their Users. Netspar.

(2009). Guidelines for Implementation of the ISDA 2009 Collateral Dispute Resolution Procedure.

ISDA.

(2012). ISDA Margin Survey 2012. ISDA.

Levels, A., & Capel, J. (2012). Is Collateral Becoming Scarce?De Nederlandsche Bank.

Pery, F. (2012). Collateral Optimization : A Fund Manager's Perspective. Citibank.

Seagroatt, M. (2012). Collateral Optimisation in a Centrally Cleared World. 4sight Financial Software.

Tabb, A. (2012). Optimizing Collateral : In Search of a Margin Oasis. Tabb Group.


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About the Author

Kishor Chandra Das is Managing Consultant in Headstrong, a global consulting firm

in the capital markets, with a focus on technology and process transformation. Mr.

Das is an experienced consultant in the industry, with 16 years of experience in

business transformation, process reengineering, and IT outsourcing projects. Kishors

research interests are in Collateral Management, Counterparty Risk Management,

Project Value Management, and in Strategy and Project Execution. He is a member

of the Project Management Institute UK Chapter and can be reached at

[email protected]

Kishor Chandra Das, 2012
mailto:[email protected]:[email protected]:[email protected]

Collateral ManagementOptimization, Efficiency & Effectiveness

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