Historically, corporate credit risk managers focused principally on current exposure measurement. This meant that financial products were measured mainly on a nominal or notional basis. With the rise in derivatives trading, however, this approach did not provide an acceptable indication of credit risk, since losses took a relatively long time to evolve for swaps, options and other forward-looking instruments.
A more useful measure was the ‘mark-to-market plus add-on’ approach – the add-on represented a potential increase of the exposure over time. It worked well for derivative standalone deals, however it had clear shortcomings for complex products, portfolio netting and advanced collateral management.
Along Came Monte Carlo
Over the last decade, Monte Carlo has become the de facto standard for calculating exposures, specifically for over-the-counter (OTC) derivatives. The calculation of potential future exposure (PFE) requires a multi-step Monte Carlo simulation, where different scenarios describe the joint evolution paths of all of the underlying market factors affecting exposures and collateral. This is the most accurate, generally applicable and reliable way of capturing the complex, stochastic nature of PFE, as well as modelling collateral and netting to reduce exposure.
Firms and corporates have made significant investments in hardware, software and operations, building sophisticated PFE measurement systems. Throwing increasing amounts of technology at this area, however, it’s expensive and naive as it will not in itself provide the whole solution.
The Human Touch
One point frequently forgotten is that Monte Carlo-based portfolio simulations require a substantial investment in human skills, if they are to be used effectively. Without this expertise, the shiny technology could become a black box, producing a number that has little to do with the true risk of the firm. This is particularly a problem for risk management departments in smaller or regional firms with limited resources.
Simulation methods are only accurate if they are configured correctly and monitored. Both IT resources and functional experts are required to maintain and develop the parameters of the engine in such a way that the simulations converge numerically and the results can be explained. Firms must understand that the final figures are based on models – models for evolving market rates into the future and models for pricing the contracts. The assumptions and simplifications on which these are based must be checked and justified on an ongoing basis.
Need for Best Practice
Firms need a best practice approach and they also need to focus on what will work best for their own organisation. They need to take into account factors like the size of counterparty and type of deal from which the exposure is derived. They also need to look at the size and skills of the team available to configure and monitor the credit risk solution.
From a day-to-day operational point of view, the best approach is a combined approach. Firms need to invest in technology for advanced Monte Carlo simulations for the largest and most complex off-balance-sheet exposures, where they bring real value. They must also ensure they have access to the skillsets required to support these techniques effectively. Simpler analytical methods can be retained for all other exposures.
When taking on this approach, firms need to ask themselves two questions:
- Can we justify the figures obtained?
- Are these figures sufficiently risk sensitive?
Exposure figures are used to monitor a firm’s operations and as a basis for decisions. The more complex an exposure, the more important it is for risk managers to justify their figures and explain how they were obtained. It is equally important that the exposure measure adjusts through time and differentiates between specific instrument and counterparty criteria. In this case, PFE via Monte Carlo makes sense, but with smaller overall exposures and less complex deals, PFE may be estimated via add-ons. This ideal solution depends on the firm being able to apply different approaches to different parts of its exposure: for example to different netting sets or different counterparties.
Some structured products present firms with a particular challenge. Most trading systems have flexible structuring tools embedded within them. However the front office flexibility isn’t carried through to the PFE simulator. From a technical point of view, it is rarely possible to plug these tools directly into a full, Monte Carlo-based portfolio simulation. In the case of some structured products, the compute time required to simulate exposure cannot realistically be achieved.
Firms need to use a two-step approach:
- Apply an add-on method in order to approve the trade.
- Include it in the daily process.
Then firms can move to a more complex credit risk approach via monitoring it through its lifecycle.
This add-on can be obtained via a combined approach, with the add-on simulated via Monte Carlo and updated say monthly, while the mark-to market value is computed daily.
Use of the add-on approach brings its own questions:
- How do I set prudent but accurate add-ons?
- What level of ‘granularity’ should they have – in other words, do I define add-ons by type of instrument, by currency, by maturity and/or by other criteria?
- How often should these add-ons be reviewed?
Simple formulas can be based on current market value, delta adjusted effective notional and credit conversion factors that depend on, for example, the instrument type properties and counterparty ratings. The Basel II guidelines for banks on calculating the exposure at default (EAD) for OTC derivatives are a good starting point.
The add-on can also be calculated from the PFE profile of a standalone transaction. For some instrument types, closed form or semi-analytic formulas are available, or a full Monte Carlo simulation can be performed for the deal. The add-on is then estimated from the peak of the PFE profile, calculated at the desired confidence level.
Additionally, specific ‘shocks’ may be applied to the underlying market factors. These may be fully deterministic or may take scenarios from a historical period, possibly a historical period of significant stress like the add-on through stress testing and via historical simulation.
Horses for Courses
The optimal way to manage credit risk is with a combined approach. Firms should only calculate PFE for OTC derivatives and apply Monte Carlo simulations for the largest and most complex exposures where they bring real value. They can weave in analytical methods for other exposures and take advantage, where appropriate, of a combination of add-on plus simulation methods, such as the Basel II Internal Model Method (IMM).
Success depends ultimately on risk managers knowing their business and being able to ‘slice and dice’ when quantifying PFE to best reflect the different elements of a firm’s exposure.
Many banks around the world, large and small, continue to experience major security failures. Biometric systems such as pay-by-selfie, iris scanners and vein pattern authentication can help.
The implementation date of Europe's revised Markets in Financial Instruments Directive, aka MiFID II, is fast approaching. Yet evidence suggests that awareness about the impact of Brexit on MiFID II is, at best, only patchy and there are some alarming misconceptions.
Banks might feel justified in victim blaming when fraud occurs, but it does little for customer confidence.
Politicians have united in urging the Reserve Bank of Australia to lend its backing to the digital currency by officially recognising it.