When it comes to the risk management of investment assets, either individually or on a portfolio scale, there are a number of choices to be made relative to the investment appetite of the bank in question, and the risk profile of the underlying exposures.
Some of the well-known approaches adopted by EU Banks in modeling and quantifying credit, market and liquidity risks are enumerated below.
Commonly Used Risk Metrics
- Credit Risk Metrics, such as LGD, PD, EAD.
When a bank enters into a loan agreement, it creates a risk for the bank as lender. The terms and conditions of repayment may not be fulfilled as previously agreed. Credit risk factors are those that affect the borrower’s probability of default, loss to the lender given default and the lender’s exposure at default. There may be events that are specific to the individual borrower or market wide events that affect all borrowers. The modelling of a bank’s portfolio credit risk requires a specification of credit loss. If the borrower does default, then the Expected Loss (EL) is defined as:
EL = PD * LGD * EAD
where:
PD = probability of default, which is the probability that the borrower will default at the end of a pre-determined time period (e.g., one year) and expressed as a percentage. Fundamentally, this depends on the on the credit spread of the issuing entity, in conjunction with the Loss Given Default (LGD). For liquid issuers this can be derived from credit default swap curves, but for less liquid issuers it is necessary to assess the contributing components of credit risk to establish a credit spread. This is therefore dependent on the region, industry and state of the issuer’s business, interest rate and economic climate.
LGD = expected loss given default that is also defined as LGD = 1 – RR (where RR = expected recovery rate and is usually expressed as a percentage). In the event of default, the loss (or value recovered) is of key importance to the valuation. For synthetic credit products this is contractual, for anything else this depends on the market. Modelling this requires consideration of the seniority of the assets, industry, issuer, historical recoveries and trends, and an assessment of the current climate, as well as the assets and liabilities of the issuer.
EAD = expected Exposure Amount at Default, expressed in monetary amount. The EAD defines the value of the investment at risk in the event of default, specifically, the maximum exposure to loss at that time. It is quite possible that current exposure and future exposures will be different, and as such this is linked to the fair valuation of the assets (and liabilities) in question. The calculation of this requires the modelling of the investment value now, and over the duration of the investment life, which in turn depends on the evolution of the market factors upon which the value of the investment depends. Each of these metrics is quantified using a variety of stochastic, market comparative and statistical approaches, and in some instance by deriving volatility from historical or implied means.
- Market Risk Metrics, such as VaR, ETL.
A bank may invest in straight bonds, listed equities, mortgage-backed securities, covered bonds, etc., as well as conduct trading activities. The change in the value of the bank’s investment portfolio, balance sheet valuation, coupled with any risks to its earnings due to alterations in an underlying market factor, including the price of shares, interest rates, prices of commodities or foreign-currency exchange rates is defined as the Market Risk of the bank. We consider the two most commonly used metrics.
Value at Risk (VaR) = VaR is defined as the loss level that will not be exceeded with a certain confidence level during a particular period of time. This aims to capture the maximum expected loss on a portfolio. As an example, if the 10-day, 95% VaR of a portfolio is $1 million, then it is considered that there is a 5% chance that losses will exceed $1 million over the 10-day period.
VaR provides a single, easily digestible metric that reflects the riskiness and diversification benefits for a portfolio given severe perturbations to underlying prices or curves.
There are, however, shortcomings of VaR. For instance, it is typically derived based on assumed (normal market) distribution conditions, and is not a very good predictor of extreme situations and outcomes with fat tails. It may also underestimate or overestimate diversification benefits, and may not be able to capture quickly and dynamically changing fundamentals that might alter the short-term returns and correlations across asset classes.
Risks that are not quantified in a VaR methodology, such as proxy risks, basis risks, risks from calibration parameter errors and other higher order risks are usually scoped out separately using a Risks not in VaR (RNIV) framework.
Expected Tail Loss (ETL) = Where VaR aims to capture how bad things can get, the ETL provides a quantification of what the expected loss will be in a tail event. ETL is the expected loss during a determined period of time, with a percentile probability
- Liquidity Risks Metrics
There are various quantitative liquidity measures. The first stage of effective liquidity risk management is to be able to identify the ways in which a firm’s activities and outside influences can affect a firm’s liquidity risk profile. The goal is to have sufficient funds to meet obligations as they arise without selling assets, or to have excess liquidity in a normal environment and sufficient funding in a stress environment. Target liquidity ratios examples include:
- Using short-term assets/liabilities, borrowing value of unencumbered assets, short-term unsecured obligations.
- Liquidity ratio can be defined as: (cash + Borrowing Value [BV] of unencumbered securities + unsecured portion of committed facility)/unsecured debt maturing over the next 12 months, including unsecured letters of credit.
- Using the firm’s liquidity mismatch reporting, using cash inflows and outflows categorized into maturity buckets. The number and size of maturity buckets can vary to reflect the time horizon over which the firm needs to monitor liquidity profiles.
Model Risk
Modeling valuations and risks by their very definition are incomplete or approximation exercises. A false sense of comfort can be achieved from a misplaced belief that historical trends or risk events alone can accurately measure developing risks. To some extent, sophisticated models and technology provided risk managers with a false sense of security. However the valuations and risks being modeled for the future keep getting morphed by changing market and regulatory conditions, not to mention non-linear and complex behavioral, macro-economic and social factors. Various factors need to be calibrated, and various aspects assumed to arrive at an estimation of risk.
Calibration to observed inputs, and market comparisons whenever possible, are essential to keep modeling outputs honest. Challenging the results from a front office, mid- and back-office standpoint, coupled with a shared and strong risk management culture are required to keep modeling efforts prudent. This culture should be established top-down and bottom-up, with enough people along the chain being empowered to identify, call out and discuss risks with relevant stakeholders and senior management teams.
When it comes to the model risk control process overall, there is a model lifecycle that organizations should institute with clear responsibility and ownership at each point of the process. Aspects of an effective model risk framework include:
- Dissemination of model risk scores and user education along with model results.
- Recognition of models as a “work-in-progress” that need to be continually re-examined and improved.
- Independence of various functions, in particular, the model development, risk control and audit functions.
- Clear definitions of ownership with accountability.
- Effective change management processes with checkpoints and defined criteria at each stage.
- Emphasis on documentation at each stage in the model lifecycle.
Capital Risk Management is an assessment of a firm’s ability to withstand the impact of credit, market and other risks it is exposed to.
In light of the continually increasing regulatory requirements on both financial and peripheral industries, risk management practices and their evolution are firmly on the radar of senior management, board members, auditors and other stakeholders.
Nowadays, risk management is not simply viewed as a diversion of resources from profitable activities, but as a fundamental tool for sound business and financial decisions. The increased scope and complexity of regulatory requirements poses greater challenges for financial institutions. Regulators increasingly want banks to estimate their economic capital or Internal Capital Adequacy Assessment Process (ICAAP) requirements by quantifying all of the inherent risks the bank is exposed to.
Economic capital is defined as the amount of capital needed to absorb losses over a certain time horizon, with a certain confidence level. It is meant to ensure survival in a worst-case scenario and covers in a granular manner market, credit, liquidity and operational risks. The estimation of economic capital is done by using a combination of Portfolio VaR calculations and/or stress tests, on portfolio valuations and earnings models. The development of economic capital models enables better quantification of tail risks.
The quantification of risk is a key step towards the management and mitigation of risk, and there are many approaches to consider. Many of these approaches are becoming more standardized across the United States, within the U.K. and across the EU and within each country’s own regulatory regime.
Implications to capital and risk management continue to evolve and at times converge under MiFID II, Solvency II, CRR/CRD-IV, PruVal and Basel III, and there is a greater focus on more granular risk management reporting, and the quality of the underlying analytics and models used to create the metrics.
Future Outlook and Trends affecting Risk Management within Banks
- More complex governmental regulation
Lingering sovereign intervention and financial stimulus will continue to mean a higher level of authoritative scrutiny of banks and their usage of public funds. More standardized approaches to estimating internal bank risks and capital risk management models will undoubtedly evolve. Compliance with prudential regulations, internal fraud, external and international regulatory regimes, including the possibility of adherence to many more localized regulations in some jurisdictions is a real eventuality. For instance within the U.K., post Brexit, and within the United States after a potential re-tooling of Dodd Frank. This will require banks to build better and more automated risk tools to maintain efficiency and to keep compliance costs down. - Impact of FinTech and start-ups on valuation and risk tools, customer banking experience
Retail and commercial banks will be in a constant state of competition with upcoming start-ups that will be disruptive to their operating model, and will develop creative ways to service clients and maintain due diligence checks, and develop real time and shorter cycle customer and counterparty risk management and settlement tools. Banks will continue to imbue these advances by, and in combination of, acquisitions and self-build to keep pace with heightened customer expectations for lower cycle times and effective risk controls. - The specter of big data and predictive analytics
Banks have always been consumers and analyzers of big data. The advent and continued evolution of many more sophisticated tools and predictive analytics to slice and dice data and perform value added analytics will go hand in glove to ongoing banking enhancements. This will also provide a boost to the development of newer ways to assess credit, market, operational, reputational and other risks, including probably the development of new and dynamically estimated metrics. - The advent of a digital world
The digitizing of all sources of information from the customer to counterpart, from the front office to the back office processes, will mean that banks will need to deploy sufficient technology budgets to digitize and make efficient all internal processes. This will in turn have the impact of several processes that run in silos to coordinate and share information in a more digitized format. Such a large amount of data input from various interfaces will make the risk manager's task more challenging and interesting. For instance, the migration profile of certain customers as highlighted by marketers and originators may provide risk managers a signal that they need to calibrate risks surrounding their own estimation around the valuation or credit risk of a certain portfolio accordingly.
