In memory views of data can in principle also include

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systems to consume and deliver data is to leverage in-memory. In-memory views of data can in principle also include transformations. The benefit is the potential speed of data delivery and that a physical duplicate of data is prevented. Another factor that affects the choice of distributed calculation and storage on a grid of multiple computer nodes is the network bandwidth. Network bandwidth is a scarce resource even when a dedicated network is set up for the grid. Nowadays multigigabytes of data can be transmitted among computers. However, oftentimes multiple nodes share one network cable. If a lot of data are transmitted through the network, it can easily saturate the net- work bandwidth capacity. Therefore, one should consider the network factor in risk system architecture. MODEL RISK MANAGEMENT Risk analytics is the core of a risk system. It is also this book’s focus. In a book that focuses heavily on risk analysis models it is appropriate to discuss model risk management even before going into detailed discussion of any models. A risk system contains many different models used in the risk calculations. Models have their lifecycles and need to be frequently validated and tested. The need for more risk calcula- tions and their growing complexity also means that banks need to manage and validate more and more models that are deployed in the risk system(s). Sometimes models are managed on a firmwide level even if certain risk calculations are executed on a silo risk system level. A firmwide model risk management approach is motivated by the firmwide stress test require- ments from CCAR and EBA. Given the many different models that are involved in banks’ regulatory calculations, banks’ practice in model risk management is also a serious concern for regulators. For example, in 2011, the US Federal Reserve and the Office of the Comptroller of the Currency (OCC) issued joint guidance on model risk management practices.
18 INTRODUCTION Model risk can arise from many points in an analytical risk process. It cannot be com- pletely avoided but it can be managed. Model risk can arise and thus ought to be managed in the following aspects: Model data: The first source of model risk is data. We are familiar with the garbage-in-garbage-out rule. When the input data have major flaws, even the best model can generate mis- leading results. Data flaws can occur not only due to the data quality but also from a partial selection of the data history. For example, in the years preceding the 2007 finan- cial crisis, housing price statistics displayed upward trending for many years with only sporadic default losses. When banks used this part of the historical data in the hous- ing price models and the credit default risk models it was impossible to obtain tail risk levels anywhere close to the losses in the 2007 financial crisis. Even if data are avail- able that cover a full business cycle with both up- and downturns it is still just one observation of a business cycle. The future business cycle swings will most likely not be similar to the last one. This observation is relevant not only in the context of using data for model calibration such as how probability of default depends on the business cycle but also in selection and design of (macroeconomic) stress scenarios for the portfolios.

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