The Valuation Imperative: Raising the Bar in Market Data Management

Valuation is probably the most fundamental capability in financial services. Whether you are an investment manager, a bank, an insurance company or other, you need to know the value of your assets and liabilities. Valuation comes up not just in fund valuation or trading profit and loss, but in financial reporting, regulatory reporting, investor reporting, collateral management and capital allocation and risk-taking decisions.

Yet simple as it may sound, valuation practices and their resulting workflow and data needs have undergone substantial development in recent years to account for (il)liquidity and different costs associated with holding a position. Terms such as ‘fair value’, ‘prudent valuation’ and ‘additional valuation adjustments’ have come up in regulation impacting different parts of the industry, and can have different meanings depending on whether the context is buy-side or sell-side and whether the regulation is American or European. These contexts and requirements can result in different market data management aspects and potentially significant market data challenges, as Martijn Groot, VP Product Management, Asset Control explores.

The changing valuation landscape – IPV and fair value

Independent price verification (“IPV”), generally speaking, is the process of comparing front office or portfolio manager marks and valuations to a set of independently verifiable external prices to establish an accurate price to revalue positions.

Accurate in this context means assuming that a willing buyer and seller entered into the transaction freely. The term ‘fair value’ is used to denote rational and unbiased market prices.

These fair value prices are subsequently distributed to other departments (such as Finance and Risk) and used to value an organization’s portfolios, calculate P&L impact and assess required Tier 1 capital.Fair value processes have developed to provide internal stakeholders as well as investors, customers and regulators with reliable valuations. Accounting standards have kept pace with changing markets and both US GAAP and IFRS have evolved[i]to incorporate fair value measurement techniques.

IFRS13 Fair Value Measurement, for example, provides a single framework for measuring fair value and requires disclosures about fair value measurement. The standard defines fair value using an ‘exit price’ notion and uses a ‘fair value hierarchy’that results in a market-based, rather than entity-specific, measurement.

In the buy-side context the notion of fair value was introduced in the early 2000s following mutual funds mispricing scandals. The main driver was consistent valuation of securities’ closes across different time zones. Procedures and correction factors were introduced to adapt close prices from markets that had been closed for some time prior to the close and revaluation of the mutual fund that invested in those assets.

Yet, valuation complications are not limited to the trading book: IFRS9 introduced a more dynamic valuation treatment of the banking book. Its main point is to recognise credit impairments earlier via an ‘Expected Credit Loss’ measure.

IFRS9 too has been designed to value assets and liabilities in a more risk-sensitive manner. Previously, risk and accounting data was aggregated separately and obeyed different rules and norms.Under IFRS9, firms need to calculate the risk of loss on an asset up to maturity with proper recovery estimates and a forward-looking stance. The significant impact of IFRS9 on market data management lies in the increased amount of the market data required to assess different scenarios.

Regulatory processes: risk meets finance

Recently, the IPV process has been the focal point of a number of regulatory changes. Regulation such as Prudent Valuation and FRTB will significantly impact the IPV operating model and increases the function’s importance within an organization.

Prudent Valuation addresses significant flaws in traditional fair value pricing. During the 2008 credit crisis significant volatility and the drying up of liquidity led to firms overvaluing the book value of the assets they held when in practice there was no real active buyer at that price.

To account for this, Prudent Valuation rules augment the process of determining fair value by applying risk management considerations to what has traditionally been a pure pricing function by estimating exit costs and incorporating funding cost, counterparty credit risk, capital cost and so on.

In terms of a trend towards greater granularity, these efforts are similar to other methodologies employed in stress testing and risk management in the trading book.Similar to FRTB alerts on changes in liquidity horizon and risk factor modellability (“NMRF”), a data collection, integration and verification process can cater for and monitor these data needs and create triggers for both banking and trading book requirements, including alerts on rating changes and banking and trading book valuation changes generally because of a market or credit data movement.

Making sense of the complex market data landscape

The changingaccounting treatment of prices, the calculation of different valuation adjustments and the requirement to not just rely on front office, portfolio manager or trader marks have led to an increase in market data sources and the scrutiny processes to compute the required prices and adjustments.

On top of this variety in market data sources, pricing modelswill vary in interpolation and fitting models and in the inferences and conversions they make (e.g. bond price to yield, option price to volatility).

Constituent instrument prices may need to be collated and condensed into summary statistics (risk factors) such as a bond curve, a forward curve, or a credit default swap curve. Instrument market data, risk factor and calibration information on hedging instruments is also needed. Finally, there are the model parameters and model hedges, Sensitivities such as Greeks can also be considered market data.

The major upgrade in capabilities needed to comply with these regulations requires the introduction of best practices in operations as well as the infrastructure to support that; allowing this in a cost-effective and scalable way will separate competitive banks from laggards.

Best practice in IPV

The IPV function is one important stakeholder in market data management. But market data management is a foundational capability: it is a prerequisite to support many if not most financial services applications.

Yet, the current state of a valuation process often comes down to significant data integration, cleansing and calculation in Excel. Spreadsheets which are meant for reporting and to be the end state in a data processing chain are typically misused as an intermediate step in a data management process. Many prices are manually sourced from trading terminals. This introduces operational risks, prevents an operation to scale and lacks a record of decisions required for regulators.

What is needed is a cost-efficient process that keeps the costs of change (new input data, new reports, new rules, higher volumes) as low as possible and that satisfies regulatory scrutiny.

A common market data source for finance, risk, scenario management, stress testing, product control, quant group is a major step forward to lower the cost of change and makes for faster cycle time in developing and deploying valuation and risk models.

Finally, an IPV solution should include dashboards that present summary information to help the different stakeholders. It requires a solid exceptions handling process to help analysts with speedy and accurate resolution of issues, support timely intervention, set rules to minimise the number of false positives, catch all suspects, allow them to (re)set and tweak business rules, onboard new rules, sources and asset classes to lower cycle time for the business and reduce cost of change. It demands that all changes and interventions are documented and that firms can explain data lineage, i.e. how they arrived at their valuation marks by showing all inputs, rules and decisions used in the process.

Conclusion

The changing valuation landscape has arguably evolved silently, yet significantly, as a result of the raft of regulation the financial services industry has endured over the last decade. However, IPV is now set to firmly establish itself as a fundamental component of an organisation. Firms need to use a broader range of market data sources and metrics, scrutinize their processesand be able to explain how they arrive at their valuation prices. Sound market data management is a fundamental requirement for this. And as valuation is such a central capability, it will underpin improvements across a financial services firm.

Martijn Groot, VP Product Strategy, Asset Control

[i] In the case of US GAAP there is ASC820, IFRS has IFRS 13 on fair value measurements.

Valuation is probably the most fundamental capability in financial services. Whether you are an investment manager, a bank, an insurance company or other, you need to know the value of your assets and liabilities. Valuation comes up not just in fund valuation or trading profit and loss, but in financial reporting, regulatory reporting, investor reporting, collateral management and capital allocation and risk-taking decisions.

Yet simple as it may sound, valuation practices and their resulting workflow and data needs have undergone substantial development in recent years to account for (il)liquidity and different costs associated with holding a position. Terms such as ‘fair value’, ‘prudent valuation’ and ‘additional valuation adjustments’ have come up in regulation impacting different parts of the industry, and can have different meanings depending on whether the context is buy-side or sell-side and whether the regulation is American or European. These contexts and requirements can result in different market data management aspects and potentially significant market data challenges, as Martijn Groot, VP Product Management, Asset Control explores.

The changing valuation landscape – IPV and fair value

Independent price verification (“IPV”), generally speaking, is the process of comparing front office or portfolio manager marks and valuations to a set of independently verifiable external prices to establish an accurate price to revalue positions.

Accurate in this context means assuming that a willing buyer and seller entered into the transaction freely. The term ‘fair value’ is used to denote rational and unbiased market prices.

These fair value prices are subsequently distributed to other departments (such as Finance and Risk) and used to value an organization’s portfolios, calculate P&L impact and assess required Tier 1 capital.Fair value processes have developed to provide internal stakeholders as well as investors, customers and regulators with reliable valuations. Accounting standards have kept pace with changing markets and both US GAAP and IFRS have evolved[i]to incorporate fair value measurement techniques.

IFRS13 Fair Value Measurement, for example, provides a single framework for measuring fair value and requires disclosures about fair value measurement. The standard defines fair value using an ‘exit price’ notion and uses a ‘fair value hierarchy’that results in a market-based, rather than entity-specific, measurement.

In the buy-side context the notion of fair value was introduced in the early 2000s following mutual funds mispricing scandals. The main driver was consistent valuation of securities’ closes across different time zones. Procedures and correction factors were introduced to adapt close prices from markets that had been closed for some time prior to the close and revaluation of the mutual fund that invested in those assets.

Yet, valuation complications are not limited to the trading book: IFRS9 introduced a more dynamic valuation treatment of the banking book. Its main point is to recognise credit impairments earlier via an ‘Expected Credit Loss’ measure.

IFRS9 too has been designed to value assets and liabilities in a more risk-sensitive manner. Previously, risk and accounting data was aggregated separately and obeyed different rules and norms.Under IFRS9, firms need to calculate the risk of loss on an asset up to maturity with proper recovery estimates and a forward-looking stance. The significant impact of IFRS9 on market data management lies in the increased amount of the market data required to assess different scenarios.

Regulatory processes: risk meets finance

Recently, the IPV process has been the focal point of a number of regulatory changes. Regulation such as Prudent Valuation and FRTB will significantly impact the IPV operating model and increases the function’s importance within an organization.

Prudent Valuation addresses significant flaws in traditional fair value pricing. During the 2008 credit crisis significant volatility and the drying up of liquidity led to firms overvaluing the book value of the assets they held when in practice there was no real active buyer at that price.

To account for this, Prudent Valuation rules augment the process of determining fair value by applying risk management considerations to what has traditionally been a pure pricing function by estimating exit costs and incorporating funding cost, counterparty credit risk, capital cost and so on.

In terms of a trend towards greater granularity, these efforts are similar to other methodologies employed in stress testing and risk management in the trading book.Similar to FRTB alerts on changes in liquidity horizon and risk factor modellability (“NMRF”), a data collection, integration and verification process can cater for and monitor these data needs and create triggers for both banking and trading book requirements, including alerts on rating changes and banking and trading book valuation changes generally because of a market or credit data movement.

Making sense of the complex market data landscape

The changingaccounting treatment of prices, the calculation of different valuation adjustments and the requirement to not just rely on front office, portfolio manager or trader marks have led to an increase in market data sources and the scrutiny processes to compute the required prices and adjustments.

On top of this variety in market data sources, pricing modelswill vary in interpolation and fitting models and in the inferences and conversions they make (e.g. bond price to yield, option price to volatility).

Constituent instrument prices may need to be collated and condensed into summary statistics (risk factors) such as a bond curve, a forward curve, or a credit default swap curve. Instrument market data, risk factor and calibration information on hedging instruments is also needed. Finally, there are the model parameters and model hedges, Sensitivities such as Greeks can also be considered market data.

The major upgrade in capabilities needed to comply with these regulations requires the introduction of best practices in operations as well as the infrastructure to support that; allowing this in a cost-effective and scalable way will separate competitive banks from laggards.

Best practice in IPV

The IPV function is one important stakeholder in market data management. But market data management is a foundational capability: it is a prerequisite to support many if not most financial services applications.

Yet, the current state of a valuation process often comes down to significant data integration, cleansing and calculation in Excel. Spreadsheets which are meant for reporting and to be the end state in a data processing chain are typically misused as an intermediate step in a data management process. Many prices are manually sourced from trading terminals. This introduces operational risks, prevents an operation to scale and lacks a record of decisions required for regulators.

What is needed is a cost-efficient process that keeps the costs of change (new input data, new reports, new rules, higher volumes) as low as possible and that satisfies regulatory scrutiny.

A common market data source for finance, risk, scenario management, stress testing, product control, quant group is a major step forward to lower the cost of change and makes for faster cycle time in developing and deploying valuation and risk models.

Finally, an IPV solution should include dashboards that present summary information to help the different stakeholders. It requires a solid exceptions handling process to help analysts with speedy and accurate resolution of issues, support timely intervention, set rules to minimise the number of false positives, catch all suspects, allow them to (re)set and tweak business rules, onboard new rules, sources and asset classes to lower cycle time for the business and reduce cost of change. It demands that all changes and interventions are documented and that firms can explain data lineage, i.e. how they arrived at their valuation marks by showing all inputs, rules and decisions used in the process.

Conclusion

The changing valuation landscape has arguably evolved silently, yet significantly, as a result of the raft of regulation the financial services industry has endured over the last decade. However, IPV is now set to firmly establish itself as a fundamental component of an organisation. Firms need to use a broader range of market data sources and metrics, scrutinize their processesand be able to explain how they arrive at their valuation prices. Sound market data management is a fundamental requirement for this. And as valuation is such a central capability, it will underpin improvements across a financial services firm.

Martijn Groot, VP Product Strategy, Asset Control

[i] In the case of US GAAP there is ASC820, IFRS has IFRS 13 on fair value measurements.