Karen Nunez Project Title: Reporting Frequency, Sample Structure, and the Effectiveness of Fair Value Net Income as a Measure of Risk This project examines the properties of Fair Value Net Income (FVNI) as a measure of business risk. It also relates these properties to important implementation issues such as measurability, reporting frequency, and cost. Our analysis is developed in the context of bank trading portfolios, which are currently accounted for at market value and thus represent an ideal framework for examining ‘fair value’ based risk metrics. While explicit findings address the risks of bank trading portfolios, the issues addressed are also relevant to proposed extensions of fair value reporting methods to broader classes of assets and liabilities. The methodological contributions of the study include the application of analytic and simulation methods to overcome sample limitations encountered in conventional empirical and experimental approaches. VAR and earlier portfolio risk measures such as ‘maturity gap’ and duration originated as ways to reveal risks not apparent from conventional GAAP accounting methods. The utility of these methods is evidenced by their widespread use by financial institutions, typically going far beyond the measures now mandated by regulation. With the broad acceptance of these methods, we can now speak of at least three distinct ‘dimensions’ of financial information used by banking institutions: accrual method net income, cash flow, and risk measures grounded in fair value methodology. The introduction of fair value as an alternative basis for net income complicates the distinctions between these three established dimensions of management information and control. Fair value net income appears to compete directly with the accrual income concept as the central indicator of firm performance. Risk information conveyed by fair value net income also overlaps with results from established VAR methods. While VAR carries the conceptual advantage of reflecting only current exposures, FVNI potentially conveys similar information in a manner that is far less dependent on esoteric modeling processes. The risk informativeness of FVNI, however, is highly dependent on the sample period over which it is observed. Rate information can largely overcome limitations in FVNI sample variability, since effects of small movements in the risk driver can be used to roughly estimate exposure to larger changes. Unfortunately, outside the financial sector, we generally lack explicit measures for primary drivers of business risk. For example, risks to the FVNI of a software firm, broadly construed, have little to do with interest rates. Lacking explicit information on risk drivers, FVNI must be observed over a sufficiently long, diverse and commensurable historical period to reveal latent risks of the firm. Meanwhile, market dynamics may change significantly, shortening the useful time period for measuring the firm's current economic exposures. Observation frequency is also a key contributor to the informativeness of FVNI. Frequent observations allow us to infer risk from a shorter time period, helping assure our measurements speak to the firm’s current position. In general, however, frequent measurement and reporting is quite costly. Current GAAP net income, with its complex rules and dependence on estimates such as depreciable life and loss reserves, probably ranks among the more expensive of financial metrics to produce and audit. The fair value of liquid trading portfolios is relatively easy to calculate and audit, but expansion of fair value methods beyond their relatively narrow current domain will involve significant costs. Consequently, the relationship between reporting frequency and the information content of FVNI is highly relevant to the future shape and direction of accounting standards and risk measurement practices. Our research will seek to address the following issues in turn: 1. Elucidate some fundamental principles regarding the relationship between sample structure and information content, as well as the interactions between risk ‘drivers’ and stochastic outcomes. 2. Empirically assess the informativeness of FVNI streams, appropriately weighted for contemporaneous rate movements, for risk assessment in the special case of bank trading portfolios. We compare these results to alternative metrics such as VAR, building on earlier work by Jorion, Liu, and Hirst. 3. Discuss some practical implications of our findings for the expansion of fair value methods to broader areas of financial reporting and enterprise risk management.
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