net income as the difference between revenue and expenses. ROE is then estimated as net income divided by capital invested. In the case of risk budgets, a risk "charge"-defined as VaR or some other proxy for "risk expense"-can be associated with each line item of projected revenue and expense. Hence, a RORC (return on risk capital) can be associated with each activity as well as for the aggregation of all activities. In the case of both financial and risk budgets, ROE and RORC must exceed some minimum levels for them to be deemed acceptable. Both statistics are concerned with whether the organization is sufficiently compensated-in cost/benefit terms-for the expenses and/or risks associated with generating revenues. Finally, both RORC and ROE can and should be estimated over all time intervals that are deemed relevant. If we accept the premise that risk capital is a scarce commodity, it follows that monitoring controls should exist to ensure that risk capital is used in a manner consistent with the risk budget. Material variances from risk budget are threats to the investment vehicle's ability to meet its ROE and RORC targets. If excessive risk is used, unacceptable levels of loss may result. If too little risk is spent, unacceptable shortfalls in earnings may result. Risk monitoring is required to ensure that material deviations from risk budget are detected and addressed in a timely fashion. The chapter introduces the concept of an independent risk management unit (RMU) as a best practice in risk monitoring space. It discusses its objectives and provides examples of how it might operate in practice. The final part of the chapter deals with performance measurement tools and related theory. Performance tools are especially robust when they confirm a priori expectations regarding the quality of returns. Among the objectives of these tools are: II To determine whether a manager generates consistent excess risk-adjusted performance vis a vis a benchmark. II To determine whether a manager generates superior risk-adjusted performance vis a vis the peer group. II To determine whether the returns achieved are sufficient to compensate for the risk assumed in cost/benefit terms. II To provide a basis for identifying those managers whose processes generate high-quality excess risk-adjusted returns. We believe that consistently superior risk-adjusted performance results suggest that a manager's processes, and the resulting performance, can be replicated in the future, making the returns high-quality. The chapter then describes tools to measure the nature of performance. Unusual volatility and performance results can be identified by categorizing each outcome as statistically expected fa green zone outcome), somewhat unusual fa yellow zone outcome), and statistically improbable (a red zone outcome). Other performance tools that are explored include return attribution, the Sharpe and information ratios, and portfolio manager alpha versus the benchmark and versus a peer group. In each case, strengths and weaknesses of the performance measurement tool are briefly discussed. Appendix B provides a more mathematical treatment of account performance measurement.