Fat Tail Analysis on S&P 100 Stocks-before and after US President Election
Keywords:Heavy Tailed event, Non-Gaussian, US Election
The main aim of this paper is to determine whether the volatility in the stocks can be created by events like the US Election and whether it leads to Fat Tail in the stocks. Fat Tail analysis is a key factor in determining volatility and has been used in the economy as well as in many other fields like climate and health. Log return has been used to determine the Fat Tail. To make the work more reliable, two Presidential election periods, that of Barack Obama and Donald Trump is selected and is compared for volatility and Fat Tail. For this study, stocks from the S&P 100 are selected and observed. The results show that the US economy is not at all driven by who comes in power and when but rather by the present economic condition. Stocks showing heavy tails during the Obama presidency are primarily because the economy was under Sub Prime Crisis too.
Ascari, G., Fagiolo, G., & Roventini, A. (2015). Fat-tail distributions and business-cycle models. Macroeconomic Dynamics, 19(2), 465-476.
Beleidy, S. El, Bommareddy, S., Narapareddy, S., & Yoner, N. (2014). Applications of fat tail models to financial markets proposal. Unpublished Project Report, 1(1), 1–19. George Mason University.
Birău, F. R. (2013). Analyzing fat-tailed distributions in emerging capital markets. Cks Bucuresti, 3, 1026–1032.
Cook Pine Capital LLC. (2008). Study of Fat-tail risk. Cook Pine Capital LLC., 1–11.
Crime, T., & America, L. (2005). The unholy trinity: Transnational. Resources for the Future, XI(2), 33.
Elkatawneh, H. H. (2016). Bridging theory and practice leadership/Barack Obama. SSRN Electronic Journal, (January 2016). https:// doi.org/ 10.2139/ ssrn.2867772
Gay, R. (2005). Premium calculation for fat-tailed risk. ASTIN Bulletin: The Journal of the IAA, 35(1), 163-188.
Ghosh, B., & Kozarević, E. (2019). Multifractal analysis of volatility for detection of herding and bubble: evidence from CNX Nifty HFT. Investment Management & Financial Innovations, 16(3), 182-193.
Ghosh, B., Krishna, M., Rao, S., Kozarević, E., & Pandey, R. K. (2018). Predictability and herding of bourse volatility: An econophysics analogue. Investment Management and Financial Innovations, 15(2), 317–326.
Ghosh, B. (2017). FRA-CDS-VDAX based credit crash model: A German conundrum. International Journal of Economic Research, 14(8), 221–228.
Ghosh, Bikramaditya, & Krishna, M. C. (2019). Power law in tails of bourse volatility – Evidence from India. Investment Management and Financial Innovations, 16(1), 291–298.
Huisman, R., Pownall, R., & Koedijk, K. (1998). VaR-x: Fat tails in financial risk management. The Journal of Risk, 1(1), 47–61.
Immelman, A. (2017). The Leadership Style of U. S. President Donald J . Trump. Management Consultants and Their Leadership Style: A Contingency Perspective, 1–6.
Lebaron, B., & Samanta, R. (2005). Extreme value theory and fat tails in equity markets. SSRN Electronic Journal, 1(November 2005), 32.
Lynch, T. (2017). President Donald Trump: A case study of spectacular power. Political Quarterly, 88(4), 612–621.
Pan, R. K., & Sinha, S. (2008). Inverse-Cubic Law of index fluctuation distribution in Indian markets. Physica A: Statistical Mechanics and Its Applications, 387(8-9), 2055-2065. https://doi. org/10.1016/ j.physa. 2007.11.031”.
Ramadhani, F. S. Z. A. (2014). The Leadership of Barack Obama (p. 6). Retrieved from https:// www.researchgate.net/ publication/ 333485726_THE_LEADERSHIP_OF_BARACK_OBAMA
Washington, G., Adams, J., Jefferson, T., Madison, J., Monroe, J., Adams, J. Q., Bush, G. W. (2017). Barack Obama Barack Obama.
Whitt, A., & Feldman, A. (1998). Fitting mixtures of exponentials to long-tail distributions to analyze network performance models. Performance Evaluation, 31(3-4), 245–279.
Wickett, X. (Ed). (2017). America’s international role under Donald Trump. Royal Institute of International Affairs, (X), 8–12.