X-Valuation Adjustment is a catch-all term for various adjustments made to derivative instruments. These kinds of calculations are computationally intensive: they require a modeller to calculate a larger number of default scenarios and the potential loss (Potential Future Exposure, or PFE) in each case. Agent-based simulation provides a framework to compute more accurate valuation adjustments for better risk management; let’s see why.
"Computational simulation adds a layer of wisdom to our understanding of complex systems"
Although wisdom sits at the apex of the knowledge hierarchy, it is often the aspect that is least discussed. Wisdom should be the ultimate goal of any analytical endeavour. Scrutinising data lets us explore structures, spot patterns, and understand relationships. However, insight from data alone is insufficient to make wise decisions when we are confronted by new and unseen scenarios. Why is this the case? And how do we gear our analysis towards obtaining wisdom?
"If we are walking along a cliff, it is not simply enough to feel solid ground beneath our feet, it is also nice to see how far from the edge we are."
Changes in accounting standards tend to be pretty dull, but this one is seismic. IFRS 9 introduces a new era for expected loss provisioning: banks now need to model expected losses throughout the lifetime of a financial instrument. In practice, this means simulating millions of realistic future scenarios in order to accurately compute probability-weighted losses. Advances in complexity science and computational economics have made this type of risk management possible, allowing financial institutions more than just simple compliance, but also true business value and competitive advantage.
Contagion risk is the risk that a shock to one financial institution spills over to others. In this way, small shocks can have significant effects. Contagion is one of the key dynamics that gives rise to systemic risk in a complex adaptive system. The complex web of interconnections between the constituent parts of the financial system can act as pathways along which instability spreads and amplifies.
Traditional modelling approaches are ill-suited to the type of scenario-based risk management that stress testing is concerned with. Agent based modelling (or ABM), in contrast, provides a natural framework for exploring challenging scenarios faced by complex adaptive systems — as I shall illustrate later in this post with a simple mortgage model.