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Financial Modeling
Financial services organizations make extensive use of forecasting techniques. There are several reasons for this. The first is that all businesses utilize forecasting to estimate demand and costs. Forecasts are not only useful in setting budgets but they are useful for control as well. In addition, financial services companies are highly dependent on changes to the macroeconomic environment. Therefore, forecasting becomes more important for such companies, as it allows them to better understand the conditions that will affect demand, and will affect interest rates as well, which is their cost of capital. That financial services companies have their cost of capital determined in part by macroeconomic conditions makes them unique among businesses, making more important the role of forecasting.
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There are a number of different forecasting techniques. Optimization techniques are an important part of risk management. Optimization techniques seek to determine the course of action that has the best expected outcomes on average. Such techniques usually require quantitative...
Mun (2006) notes the optimization is usually on a risk-adjusted basis, so when the values are entered into the algorithm, a course of action is revealed that will optimize performance.
Monte Carlo theory is often utilized in optimization techniques. Monte Carlo simulation is based on probabilities (Riskamp.com, 2014). This technique weighs the potential outcomes along with the probabilities of that outcome occurring. The method should yield the course of action that delivers the greatest expected payoff, but Monte Carlo relies heavily on its assumptions about future payoffs and the likelihood of that outcome coming to pass. With bad assumptions, the simulation becomes worthless. Another forecasting technique is sensitivity analysis.
Sensitivity analysis is a technique that helps to mitigate the assumptions used in Monte Carlo or other forecasts. The sensitivity analysis tests the change in outcomes to changes in stimuli. So if there is a change to an input, how will that affect an output. For a financial services organization that has assumed a Fed…
References
Mun, J. (2006). Modeling risk: Applying Monte Carlo simulation, real options analysis. John Wiley & Sons.
Riskamp. (2014). What is Monte Carlo simulation? Risk amp.com. Retrieved March 6, 2014 from http://www.thumbstacks.com/files/RiskAMP%20-%20Monte%20Carlo%20Simulation.pdf
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