Structured products and FRTB
The European structured products market is a 300 CHFbn market, of which Switzerland accounts for around 60%. As of Q4 2018, the total “live” volume of structured products in Switzerland was just below 200 CHFbn, representing around 3% of all securities held in custody accounts in Switzerland, with the remainder consisting of direct investments mainly in bonds, shares, and funds.
Given the low-interest rate environment, it comes as no surprise that a large share of outstanding structured products in Switzerland are yield‑enhancement products such as barrier reverse convertibles. Many such structured products often exhibit path-dependent payoffs, and typically lack closed-form pricing formulae. In these instances, banks have to revert to more sophisticated, numerical pricing methods such as Monte Carlo simulations. Given that the pricing of individual issuances already contains a high level of complexity, there are substantial challenges to the measurement of portfolio‑level risks with high accuracy.
Businesses involving non‑vanilla instruments such as structured products typically attract significantly higher regulatory capital requirements than vanilla flow businesses. In January 2016, the Basel Committee on Banking Supervision (BCBS) published a revised market risk framework; the Fundamental Review of the Trading Book (“FRTB”), which will go live in 2022. FRTB represents an overhaul of the standardised approach (SA) by making it more risk-sensitive, and is expected to increase capital charges for market risk compared to the Basel II framework.
For banks opting for an Internal Model Approach (IMA), FRTB introduces a range of model acceptance criteria, which serve as prerequisites for obtaining and retaining IMA model approval. One of the key model acceptance criteria is regular P&L attribution testing, which focuses on closely aligning the risk pricing with front office pricing. Especially for structured products businesses, the P&L attribution testing will pose a key challenge, and will require a stringent review of the valuation models currently used in the risk engines.
Risk Management of Structured Products
Accurately quantifying risks for a structured products portfolio is highly complex. Consequently, closely managing the market risk arising from such a portfolio poses a number of substantial challenges. In essence, the risk management of structured products is a combination of:
- P&L hedging
Hedges are typically “local hedges” that assess the P&L impact of small changes in the risk drivers. This involves hedging the risk-factor sensitivities (such as delta, vega, gamma), which can be calculated at the instrument level. Local hedges manage to capture P&L changes stemming from everyday risk factor fluctuations.
However, they might not be sufficient to cover risks stemming from larger “worst-case” moves in the underlying risk drivers. This is particularly the case for instruments with non-linear payoffs such as barrier reverse convertibles. Furthermore, local hedges do not take into account portfolio-level concentration risks.
- Hedging Tail-Risks
Here one looks at the “worst-case” loss events (Expected Shortfall, VaR or stress testing). Tail losses are dominated by significant movements in key risk factors that either affect the whole portfolio broadly across many positions, or the largest single exposures. Tail events often occur when various adverse movements happen jointly, which means that correlation effects come into play. Hedging for such “worst-case” losses is different from the P&L hedging in that it is not performed at the product-level, but rather it takes into account the full portfolio context.
- Reducing RWA
Some banks’ hedging activities go beyond just mitigating P&L volatility and tail risk. Indeed, banks with large structured products businesses might consider transacting hedges with a focus on reducing their RWAs. In an ideal world, the hedging of tail-risks ought to be in line with the reduction of RWA. Whereas this is broadly the case for an IMA, this is often not the case for SA users, where the sensitivity-based risk-factor shocks and the prescribed risk aggregation might not adequately reflect the institution’s own view of risk.
Given the different “fronts” of the risk-management, it is often a challenge to have a holistic overview of the different risk-measures. Different hedging strategies might be able to lower capital costs, but might not adequately hedge the real risks or vice versa. Understanding the impact of various hedges as well as the key drivers of the various risk-metrics is key for an efficient portfolio management.
An integrated solution
Deloitte has developed an integrated risk management solution for structured products portfolios, providing a user with a combined view of both internal risk metrics as well as regulatory capital measures. The solution integrates an internal model approach (IMA) alongside a standardised approach (SA) risk charge calculation.
The IMA solution is flexible in that it allows for calculating both regulatory capital measures (97.5% ES), as well as internal risk measures (VaR or ES at any quantile). Furthermore, the IMA solution enables a full revaluation of complex structured products by means of a cutting-edge Nested Monte Carlo approach, and is hence a prime position to pass P&L attribution testing. Alongside the tail risk measures, the Deloitte tool also provides an overview of key local risk metrics (Greeks).
This integrated solution allows users to compare the impact of different hedging strategies on the full range of risk and capital metrics. In particular, it enables the assessment of regulatory capital requirements for various hedging strategies under both an internal model and a standardised approach.
Tail risk measures are portfolio-level metrics, and are strongly dependent on the correlation between the underlying risk drivers. Even though the tail risks are measured at the portfolio-level, a product-level breakdown of the metrics is key for effective risk management. This is particularly relevant for trade-level allocation of costs, which can be used to incentivise risk-reducing trades. Our solution calculates product-level contribution to capital requirements (97.5% ES) by means of an Euler-allocation scheme.
The IMA development process encompasses several different modelling choices. Key examples include: the calibration of the marginal distributions of individual risk-factor and the modelling of the joint behaviour of the risk-factors. The Deloitte tool enables to produce impact assessments of key modelling assumptions and hence allows for a quantification of the inherent model risk.
Finally, the interactive user interface provides the user a comprehensive combined view of internal risk metrics alongside regulatory capital requirements for different portfolios and hedging strategies.