Business Risk & Simulation Modelling In

Michael Rees

• 21 augustus 2015
• 9781118904053

Samenvatting:

Thousands of professionals have already gained a mastery of the fundamental concepts and latest tools of the trade from the training methods in Business Risk and Simulation Modelling in Practice now it's your turn to bring game-changing insight to your organisation's boardroom.

Whether you're looking at budgeting, forecasting, corporate finance valuation, portfolio composition and optimisation, or cash flow analysis, this practical guide shows you the best ways to approach risk assessment and the most efficient techniques to analyse and present the hard data that business leaders need to make profitable decisions. Business planners and financial analysts receive guidance to risk modelling that is grounded in real-life solutions and that includes advice on how to overcome the challenges of instilling a more risk-aware culture in organisations with established processes and incentives. Rigorous, yet highly accessible coverage uses non-mathematical material to provide you with:

• The key principles of simulation methods and the relationships between simulation and other modelling techniques such as sensitivity, scenario, and optimisation analysis.
• The core aspects of risk-model design, including important similarities and differences to traditional static modelling, alignment with general risk assessment process, and integration into existing models.
• A discussion of more than 20 distributions, along with their uses and processes, as well as frameworks for selecting the proper ones, and methods to create random samples using Excel macros created with Visual Basic Applications (VBA).
• Excel template models for running simulations, creating versatile ways to store and analyse results, generating correlated random numbers and increasing simulation speed.
• A discussion of the benefits of Palisade's @RISK Excel add-in, including its sophisticated and flexible graphics capabilities; tools for rapidly building, testing, and modifying models; a larger set of available distributions and parameters; and greater capability to control simulation and random number selection.