International Journal of Mathematics Trends and Technology
Volume 57 | Number 4 | Year 2018 | Article Id. IJMTT-V57P531 | DOI : https://doi.org/10.14445/22315373/IJMTT-V57P531
In mathematical finance, regression models can be used to determine the value of an asset based on its underlying traits and/or returns relative to the overall market performance. In prior work [6-7] a regression model was created to predict the value of the S&P 500 based on macroeconomic indicators. In the current study the model is updated with the addition of recent data, and then applied to define a new measure to model market volatility. The results are compared to the S&P 500’s implied volatility in a simulation utilizing the BlackSholes model attempting to predict the value of the S&P 500 one year in the future. While no definition could be expected to perfectly predict the market volatility, the new definitions of volatility did outperform the currently utilized implied volatility.
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Timothy A. Smith, with Alex Caligiuri, J Rhet Montana, "Using a Multiple Linear Regression Model to Calculate Stock Market Volatility," International Journal of Mathematics Trends and Technology (IJMTT), vol. 57, no. 4, pp. 220-224, 2018. Crossref, https://doi.org/10.14445/22315373/IJMTT-V57P531
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