Submitting Campus

Daytona Beach

Department

Department of Mathematics

Document Type

Article

Publication/Presentation Date

12-2015

Abstract/Description

In finance, multiple linear regression models are frequently used to determine the value of an asset based on its underlying traits. We built a regression model to predict the value of the S&P 500 based on economic indicators of gross domestic product, money supply, produce price and consumer price indices. Correlation between the error in this regression model and the S&P’s volatility index (VIX) provides an efficient way to predict when large changes in the price of the S&P 500 may occur. As the true value of the S&P 500 deviates from the predicted value, obtained by the regression model, a growth in volatility can be seen that implies models like the Black-Scholes will be less reliable. During these periods of changing volatility we suggest that the user apply a regime switching approach and/or seek alternative prediction methods.

Publication Title

International Journal of Mathematics Trends and Technology

DOI

https://doi.org/10.14445/22315373/IJMTT-V28P501

Publisher

Seventh Sense Research Group

Share

COinS