Institutions invested in a broad range of corporate debt instruments can draw various business benefits from adopting an integrated market and credit risk view. The low-yield environment in major government bond markets (see Fig.1), combined with recent spread volatility increases, reinforce these aspects. For risk analysts, asset allocation experts, and fund managers, it means capturing in a single framework obligors’ rating migration and default situations, on top of yield curve shifts and spread-widening impacts. This integration should allow business stakeholders to blend risk factor and financial instrument info, build timely hedging strategies at the issuer level, or refine their portfolio overlays. Below, we illustrate some of these integration benefits at the position level and advocate related needs for increased simulation volumes and exploration of tail risk drivers.
When sweet can turn sour
Fund managers and asset allocation specialists may diverge on their assessment of domestic bonds’ investment risk and returns. These teams often consider different time horizons and risk indicators as well. For example, obligor credit risks may not be a prime concern for the former group, compared to current market-implied spread values and volatility. For the latter group, credit events will eventually materialize, perhaps over longer time horizons. For risk managers, an integrated view of market and credit risks provides a convenient link between shorter front-office and longer asset/liability management (ALM) time horizons. It also consistently supports the various teams’ opinions across the entire investment process.
The following example assumes a 1M nominal position held in a BBB-rated corporate bond maturing in 2049. It features embedded options, meaning its issuer can exercise calls every year from 2024 on. A 40% recovery rate assumption and a sector mapping tag are attached to the bond’s underlying legal entity. The graph below (see Fig. 2) shows the simulated value of this position across 10,000 stochastic, multi-step scenarios.
Market risk factors and credit risk drivers are attached to diffusion processes. Leveraging a Merton approach, a credit score is computed for each scenario and time step, capturing rating migration effects and default states for this issuance.
A closer look at projected cash-flows can help stakeholders visualize situations where call options are exercised from 2024 on. The simulation process also uncovers scenarios where obligor default occurs. In these infrequent cases, the position value falls to 400k (see Fig. 3), in line with user-specified recovery inputs. Under the stated assumptions, obligor default cases have effectively materialized at different time horizons.
Exploring beyond single figures
For ALM teams, portfolio construction specialists and risk managers, the consequences of these integrated market and credit risk simulation outcomes are twofold. First, it confirms that obligor credit risk does matter. In the investment-grade example above, default occurs in 2 scenarios out of 10,000 at medium-term horizon. In these rare occurrences though, the bond’s principal value shrinks by 60%. Second, the expected default frequency of this issuance shows that a Value-at-Risk analysis will likely miss these costly events. Even at a 99.5% quantile, these two default events would most likely be missed by this risk indicator. Simulation-based expected tail loss measures (ETL), spanning across all scenarios beyond the specified quantile, would pick up these exceptional losses.
As presented in this example, a more integrated risk management approach allows decision-makers to uncover situations they may have ignored in traditional market and credit silos. This case also identifies marginal changes required to current processes. Obligor and credit correlation data must be sourced. Simulation volumes need to be increased. ETL measures and contributing scenarios shall be explored. Integrated market and credit risk analysis also generates many new data points. As obligors, scenarios, time steps and risk factor numbers rise to cover real portfolios, risk managers shall consider analytics suited to the task at hand. Besides the required scalability, these solutions need to be open to different input sources, allow for transformed data exploration and be adapted to various teams’ usage. The emergence of new platforms shall certainly facilitate these tasks, help stakeholders assess credit-linked opportunities, and flag risky sweet ones that will soon turn sour again.
To learn more about SS&C Algorithmics’ financial risk management solutions, please visit our product page.
Asset Management, Risk Management