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Manuscript ID : FEJ-2002-2510 (R1) Visit : 177 Page: 343 - 372

20.1001.1.22519165.1399.11.45.15.2

Article Type: Original Research

Nonlinear Dynamic Modeling of Factors Affecting the Stock Market: Baysian Quantile Threshold Regression - GARCH approach

Subject Areas : Financial engineering

Reza Tehrani 1 ( Department of Finance and Insurance Management , Faculty of Management, University of Tehran, Tehran, Iran )
Mohammad Osoolian 2 ( Department of Finance and Insurance, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran )
Saeed Bajalan 3 ( Department of Finance and Insurance Management Faculty of Management, University of Tehran, Tehran, Iran )
Vahid Abbasion 4 ( Department of Finance and Insurance Management, Faculty of Management, University of Tehran, Tehran, Iran )

Received: 2020-02-22 Accepted : 2020-04-25 Published : 2020-12-21

Keywords: Stock market, bayesian approach, Quantile Threshold Regression, Conditional Heteroscedasticity,

Abstract :

During the last decade, studies on the factors affecting stock market returns have reached a peak with the advances of financial economics in the field of statistics and mathematics, and modeling is of great importance in this regard. Accordingly, this study seeks to present a new approach to modeling the nonlinear relationship between financial variables and stock returns.This paper employs Bayesian Markov chain Monte Carlo (MCMC) sampling methods for updating the estimates and quantile threshold regression with heteroscedasticity. To study and model this approach, we used returns of the Tehran Stock Exchange, Coin Price, Oil, and Gold Price from 2011 to 2019. The results show that these variables have different effects under low and upper quantile levels. Also, the impact of the financial variables on the stock returns is different under higher and lower threshold amount for each quantile levels. Based on the result, we can say that stock returns have a nonlinear relationship with other markets in the bullish and bearish market.

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_||_

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  15. Han, L., Liu, Y. & Yin, L. (2019). "Uncertainty and currency performance: A quantile-on-quantile approach", The North American Journal of Economics and Finance, vol. 48, pp. 702-729.
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  17. Hansen, Bruce E. (2000), "Sample Splitting and Threshold Estimation", Journal of Econometrics 68(3), pp. 575-603.
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  18. Jones, C.M. & Kaul, G. (1996) Oil and the stock markets, J. Finance 51. 463–491.
    19.
  19. Koenker, R. & Bassett, G. (1978), Regressions Quantiles, Econometrica, vol.46, pp. 33-50
    20.
  20. Lucey, B. M., Sharma, S. S., & Vigne, S. A. (2017). Gold and inflation (s)–A time-varying relationship. Economic Modelling, 67, 88-101.
    21.
  21. Maghyereh, A. I., Awartani, B., & Tziogkidis, P. (2017). Volatility spillovers and cross-hedging between gold, oil and equities: Evidence from the Gulf Cooperation Council countries. Energy Economics, 68, 440-453.
    22.
  22. Mensi, W., Beljid, M., Boubaker, A., & Managi, S. (2013). Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold. Economic Modelling, 32, 15-22.
    23.
  23. Mensi, W., Hammoudeh, S., Reboredo, J.C. & Nguyen, D.K. (2014). Do global factors impact BRICS stock markets? A quantile regression approach, Emerging Markets Review 19, pp. 1-17.
    24.
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    25.
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    26.
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    27.
  27. Nandha, M., & Brooks, R. (2009). Oil prices and transport sector returns: an international analysis. Review of Quantitative Finance and Accounting, 33(4), 393.
    28.
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    29.
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