ASSET BACKED TRADING
Figure 2: Application of Decision-Support Analytics For Asset-Backed Trading Strategies
Valuation Hedging
Market Risk
Budget Risk
Operational Risk
Source:
www.nquantx.com Dynamic risk simulation involves modelling the
variability of one or more metrics (e.g., cash flow, earnings, mark-to-market, liquidity, etc.) based on realistic potential changes in a set of key state variables, as well as the firm’s response to those changes (e.g., operating, hedging and trading strategies). The analysis consists on ‘leaping forward’ at various points in time into the future. New technologies exist which streamline the
storage and consistent definition of data across the enterprise, and risk management systems should take full advantage of them to achieve consistency of risk information across different business units. For example, contract, market and counterparty information that often resides in different systems used by market and credit risk groups needs to be integrated in order to perform Earnings at Risk (EaR) and Cash Flow at Risk (CFaR) analysis (Figure 2). Another problem is that the risk management groups in many firms are still organized in silos, and often focused on measuring individual risks, or risks for a component of the portfolio. For example the financial portfolio to hedge or monetize the value of the asset is just a component of the profitability of the asset, and analysing it in isolation is not sufficient.
Spreadsheets vs CTRM Systems: Combining the best of both worlds Decision-support tools for asset-based
trading strategies need to strike a balance between flexibility and integration into
80 September 2011
Cash Flow Risk
Volumetric Risk
Credit Exposure
Performance Measurement
existing systems. Those tools need to be capable of taking multiple inputs coming from different systems, and also perform a large number of computations in simulation-based environments. The first column in Table 1 illustrates
the main problems with the current set up of decision support analytics at many firms, which includes a mix of individual spreadsheets design to solve specific issues, stand-alone solutions developed by quantitative teams that are not fully integrated or vetted (e.g. Matlab-based solutions) and tasks performed by larger systems. The second column suggests solutions to each of those problems. On one side, E/CTRM systems are usually designed to excel
at performing other tasks such as scheduling, nomination or accounting for physical trades. However, their limited offering in terms of trade-
New technologies exist which streamline the storage and consistent definition of data across the enterprise
support analytics and asset optimization have the limitation of being non-intuitive and inflexible to perform customized analysis. A flexible environment such as the one provided by spreadsheets as well as programming languages
Performance Measurement For Asset-Based Strategies
One of the areas where firms can reap tangible
benefits from deploying risk analytics for asset- based strategies is in the process of setting up performance benchmarks.
If a trading group relies on the firm’s assets to
meet their profit targets, but traders are not charged an explicit premium based on the optionality embedded on those assets, they are clearly operating at an advantage over other units. As a result, their raw performance is often stellar, but there may be reasons besides the skill or luck of the traders.
Setting up the baseline profitability based on
mechanical low risk trading strategies can level the playing field, and also assist in determining which traders are truly adding value.
For example, storage assets can be dynamically
hedged by optimizing the intrinsic value of the asset over time. That intrinsic value could be the baseline profit to measure the contribution of the traders, not the actual performance.
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