Assessing Budget Risk with Monte Carlo and Time Series Bootstrap

Abstract

Budgets are important management tools recognized for their help in planning, communication, monitoring the expense performance, and even motivating collaborators. However, recently there has been criticism of the traditional Budgeting Process due to its cumbersomeness, long duration, and eventual diversion of the focus from the day-to-day activities. Thus, improving the Budgeting Process by incorporating Expense component uncertainties is of uttermost importance to accelerate its approval. This paper presents a methodology for companies to assess their budget risk based on their historical Expense data by applying Monte Carlo Simulation and Time Series Bootstrapping Techniques. Besides, some state-of-the-art sensitivity Importance Measures are also implemented to help evaluate the relative importance of the Expense components. The methodology proposed, based on a real case study with data from a major Portuguese retailer, has the advantage of being objective and supported by data, thus not being subject to bias from the management.

Monte Carlo simulation in risk assessment in mathematical generation of long data

“…If for the purposes of the model they need to be transformed into data on a monthly or annual basis, the following transposition formulas can be used : per month=(Volatility Index n weekly)*SQRT(1/7*30) (5) for a year =(Volatility Index n monthly)*SQRT(12) (6) Having the volatility for the selected time slice (for example e week) and the correlation between the factors is a base to use standart excel formulas to generate the adequate long line of variables that can run reliable Monte Carlo simulation. 1 st step of the final part of the model is generates long line of Non-correlated normal distributed randoms using: =NORM.S.INV(RAND()) (7) It is a pre-built integrated function in Excel that is categorized under statistical functions in Excel. The NORM.S.INV: -The normal distribution is the most widely used in statistics.…”

“…And of course, it can also show what would happen if decisions were made "in the middle of the road". This is especially useful for managers who want to analyze possible options [6,7,8].…”