Financial Simulation Modeling in Excel

CHAPTER 7

Dealing with Data Deficiencies and Other Issues

At this point we have covered not only the basic mathematical components of a simulation, but also how to apply them to either a single asset or a pool of assets. We also have covered some steps to streamline and speed your analysis. However, if you are attempting to implement a new simulation, these concerns will probably require a minority of your time. The vast majority will likely be spent collecting, formatting, and debugging your data.

Having a dependable, complete data set from which meaningful statistical analysis can be obtained is of prime importance for a practitioner. While academics have the option of choosing what papers they will write based on the availability and completeness of datasets that are available to them, traders, portfolio managers, and risk managers have a limited ability to choose which types of assets they are going to allocate capital to or analyze, and even traditional assets such as bank loans may lack market or financial data that passes muster for full analysis.

Unfortunately, the research on methods for handling imperfect data is scant; data-handling issues have not occupied as central a role in the academy as they do in commercial applications. Most of the methods discussed in this chapter would likely be frowned upon by academics. However, they are tricks and methods that we have learned or developed over the years in order to overcome barriers to analysis. Handle them carefully. Fortunately, ...