# Multivariate Model

## What Is the Multivariate Model?

The multivariate model is a popular statistical tool that uses multiple variables to forecast possible outcomes. Research analysts use multivariate models to forecast investment outcomes in different scenarios in order to understand the exposure that a portfolio has to particular risks. This allows portfolio managers to mitigate better the risks identified through the multivariate modeling analysis.

### Key Takeaways

• A multivariate model is a statistical tool that uses multiple variables to forecast outcomes.
• One example is a Monte Carlo simulation that presents a range of possible outcomes using a probability distribution.
• Black swan events rendering the model meaningless even if the data sets and variables being used are good.
• Insurance companies often use multivariate models to determine the probability of having to pay out claims.

## Understanding the Multivariate Model

Multivariate models assist with decision making by allowing the user to test out the different scenarios and their probable impact. The Monte Carlo simulation is a widely used multivariate model that creates a probability distribution that helps define a range of possible investment outcomes. Multivariate models are used in many fields of finance.

For example, a particular investment can be run through scenario analysis in a multivariate model to see how it will impact the whole portfolio return in different market situations, such as a period of high inflation or low-interest rates. This same approach can be used to evaluate a company’s likely performance, value stock options, and even evaluate new product ideas. As firm data points are added to the model, such as same-store sales data being released prior to earnings, the confidence in the model and its predicted ranges increase.

## Special Considerations

Insurance companies are users of multivariate models. The pricing of an insurance policy is based on the probability of having to pay out a claim. Given only a few data points, such as the age of the applicant and the home address, insurers can add that into a multivariate model that pulls from additional databases that can narrow in on the appropriate policy pricing strategy. The model itself will be populated with confirmed data points (age, sex, current health status, other policies owned, etc.) and refined variables (average regional income, average regional lifespan, etc.) to assign predicted outcomes that will be used to price the policy.