A Regression Model to Predict Stock Market Mega Movements and/or Volatility using both Macroeconomic indicators & Fed Bank Variables

Timothy A. Smith; Alcuin Rajan

doi:https://doi.org/10.14445/22315373/IJMTT-V49P522

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 49 | Number 3 | Year 2017 | Article Id. IJMTT-V49P522 | DOI : https://doi.org/10.14445/22315373/IJMTT-V49P522

A Regression Model to Predict Stock Market Mega Movements and/or Volatility using both Macroeconomic indicators & Fed Bank Variables

Timothy A. Smith, Alcuin Rajan

Citation :

Timothy A. Smith, Alcuin Rajan, "A Regression Model to Predict Stock Market Mega Movements and/or Volatility using both Macroeconomic indicators & Fed Bank Variables," International Journal of Mathematics Trends and Technology (IJMTT), vol. 49, no. 3, pp. 165-167, 2017. Crossref, https://doi.org/10.14445/22315373/IJMTT-V49P522

Abstract

In finance, regression models or time series moving averages can be used to determine the value of an asset based on its underlying traits. In prior work we built a regression model to predict the value of the S&P 500 based on macroeconomic indicators such as gross domestic product, money supply, produce price and consumer price indices. In this present work this model is updated both with more data and an adjustment in the input variables to improve the coefficient of determination. A scheme is also laid out to alternately define volatility rather than using common tools such as the S&P’s trailing volatility index (VIX). As it is well known during times of increased volatility models like the Black-Scholes will be less reliable, hence, this work can be used to identify such times in a forward moving timeframe rather than using trailing economic indicators.

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] Colander, David and Föllmer, Hans and Haas, Armin and Goldberg, Michael D. and Juselius, Katarina and Kirman, Alan and Lux, Thomas and Sloth, Birgitte, “The Financial Crisis and the Systemic Failure of Academic Economics.” Univ. of Copenhagen Dept. of Economics Discussion Paper No. 09-03, (2009).
[3] Moyaert, T. & Petitjean, M . “The performance of popular stochastic volatility option pricing models during the subprime crisis.” Applied Financial Economics. 21(14), 2011.
[4] Smith, T. at al, “An Economic Regression Model to Predict Market Movements.” International Journal of Mathematics Trends and Technology, 2014.
[5] Smith, T. et al, “A regression model to investiage the performance of the Black-Sholes using macroeconomic predictors.” International Journal of Mathematics Trends and Technology, 2013.
[6] Park, Sam. “Reducing the Noise in Forecasting the SP 500,” Wentworth, 2005.