An instrument’s terms and conditions lie at the heart of cash flow generation and valuation. Not surprisingly, errors in terms and conditions can drive errors in valuation. Fortunately, fixing these errors is often straightforward, provided the terms and conditions data is readily available, which is not always the case for private placement instruments.
In an article published last year, the Harvard Business Review quotes IBM research that estimates that bad data costs US business $3 Trillion per year. Although it is difficult to identify the specific cost associated with bad data in market-risk management, it is obvious that managing data has never been more important.
The success of a market-risk management implementation is largely dependent on a validated, scalable, and well-governed data management process.
Many firms now use Value-at-Risk (“VaR”) for risk reporting. Banks need VaR to report regulatory capital usage under the Market Risk Rule, as outlined in the Fed and OCC regulations  and . Additionally, hedge funds now use VaR to report a unified risk measure across multiple asset classes. There are multiple approaches to VaR, so which method should we choose? In this brief paper, we outline a case for full revaluation VaR in contrast to a simulated VaR using a “delta-gamma” approach to value assets.
There is movement in the vendor market for Risk Analytics. Barclays is divesting its POINT risk analytics system and Capital IQ has exited the risk business. These changes have prompted the market to consider vendor alternatives and the timing for a fresh look at solutions couldn’t be better.
The regulatory environment persists. There is a constant stream of demand from investors and regulators for independent risk analysis and reporting. Further, the emergence of new data management tools and the declining cost of cloud-managed hardware presents the market with an opportunity to scale data processing and reduce costs.