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International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 28 | Number 1 | Year 2015 | Article Id. IJMTT-V28P501 | DOI : https://doi.org/10.14445/22315373/IJMTT-V28P501

An Economic Regression Model to Predict Market Movements


Timothy A. Smith, Andrew Hawkins
Abstract

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.

Keywords
Partial differential equations, regression analysis, stochastic, financial mathematics.
References

[1] Black, F. and Scholes, M. “The Pricing of Options and Corporate Liabilities,” Journal of Political Economy 81 (3), 1973.
[2] Moyaert, T. & Petitjean, M .”The performance of popular stochastic volatility option pricing models during the subprime crisis.” Applied Financial Economics. 21(14), 2011.
[3] Smith, T. “A regression model to investiage the performance of the Black-Sholes using macroeconomic predictors.” International Journal of Mathematics Trends and Technology, 2013.

Citation :

Timothy A. Smith, Andrew Hawkins, "An Economic Regression Model to Predict Market Movements," International Journal of Mathematics Trends and Technology (IJMTT), vol. 28, no. 1, pp. 1-3, 2015. Crossref, https://doi.org/10.14445/22315373/IJMTT-V28P501

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