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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
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

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.

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