# 34 smart data structures the use of smart data

• 18

Course Hero uses AI to attempt to automatically extract content from documents to surface to you and others so you can study better, e.g., in search results, to enrich docs, and more. This preview shows page 13 - 15 out of 18 pages.

3.4"Smart" data structuresThe use of "smart" data structures can significantly improve the cost-benefit tradeoff infavor of a rigorous (i.e., full revaluation Monte Carlo) risk measurement system. A "smart" datastructure for storing financial transactions would have some or all of the following characteristics:1.the financial instrument knows to which market risk factors its value is sensitive;2.the financial instrument knows both a "more exact" and a "less exact" valuation method foritself;3.the financial instrument knows what error is introduced at different times by its differentvaluation methods.Exploiting such a "smart" data structure could significantly reduce the computational burden ofrevaluing each instrument in a portfolio N times to calculate a Monte Carlo Value at Risk.If a financial instrument knows to which market risk factors its value is sensitive, it canavoid recalculating its value for some of the N Monte Carlo draws. If a DEM/USD currency swap isasked to revalue itself for several draws in which all market risk factors that would affect its value(presumably USD and DEM interest rates and the DEM/USD exchange rate) are identical, the valuewill be the same for all such draws and the computational burden can be reduced accordingly.Many derivatives are typically valued using numerical methods. A simple example would bean American option, which could be priced exactly using a lattice as in Cox, Ross, andRubenstein (1979). For risk measurement purposes, if there are many American options in theportfolio, it may be too time-consuming to value each American option on a lattice. Barone-Adesi andWhaley (1987) give an approximate analytic valuation method for an American option. A "smart"American option would know both valuation methods and would know how much error is introducedby the approximation at different times. This last feature would allow the risk managementapplication to track how much uncertainty has been added to the estimate of firmwide Value at Riskby using "less exact" valuation techniques, as well as to set up a threshold for approximation error thatwould force the use of a "more exact" technique if a "less exact" technique gave a particularly bad17The information systems demands of risk management are driving thirty percent of UK banks to replace legacysystems, according to a recent survey. "Risk Management is Driving Banks to Replace Legacy Systems,"RiskManagementOperations, 16th December, 1996.
128approximation for a certain set of changes in market risk factors.18Our impression is that firms havebegun to adopt "smart" techniques on an ad hoc basis, but their use has not yet become standardpractice.3.5Credit risk measurementThere is no single measurement concept for credit risk, unlike Value at Risk for market risk,which has become widely accepted in the market. The current view of "best practice" for credit risk,as expressed in the Group of Thirty's (1993) report, is to measure both current exposure and potential

Course Hero member to access this document

Course Hero member to access this document

End of preview. Want to read all 18 pages?

Course Hero member to access this document

Term
Spring
Professor
Maher AbuBaker
Tags