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Manuscript ID : FEJ-1912-2449 (R1) Visit : 232 Page: 24 - 43

20.1001.1.22519165.1399.11.44.2.7

Article Type: Original Research

One-way and two-way risk filtering using generalized dynamic factor model in Tehran Stock Exchange

Subject Areas : Financial engineering

amir sarabadani 1 , Ali Baghani 2 , Mohsen Hamidian 3 , Ghodratollah Emamverdi 4 , Norooz Noorolahzadea 5

1 - Department of Accounting,Tehran South Branch, Islamic Azad University, Tehran, Iran
2 - Department of Accounting,Tehran South Branch, Islamic Azad University, Tehran, Iran
3 - Department of Accounting,Tehran South Branch, Islamic Azad University, Tehran, Iran
4 - Department of Theoretical Economics, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
5 - Department of Accounting,Tehran South Branch, Islamic Azad University, Tehran, Iran

Received: 2019-12-17 Accepted : 2020-03-07 Published : 2020-09-22

Keywords: One-side and Two-side Risk Filtering, Generalized Dynamic Factor Model, Time Series Specific Component, Time Series Joint Component,

Abstract :

AbstractAccording to statistics, risk estimation makes unusual predictions without focusing on the relevant factors and only focusing on a set of equations. In this study, we used a spreadsheet data set of time series and a new method for risk estimation. This estimation was based on a generalized dynamic factor model (GDFM) and daily data series obtained from different measures of Tehran Stock Exchange over a 10-year period during 2008 to 2018. we first utilized a generalized dynamic factor model proposed by Forni et al in order to determine statistic and dynamic factors. In the second step, by using MATLAB, we estimated the joint component of the study series as Tehran Stock Exchange risk. Next, using the generalized least squares (GLS) method, we examined the impact of each of the filtered risks on the index returns. The results showed that although both risks estimated through one-side and two-side filtering substantially and significantly explain the changes in the performance of the studied indices, but the risk estimated through two-side filtering using GDFM can explain the returns changes much better and more accurate than the one-side filter using the same model.

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_||_

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  1. Barigozzi, M., & Hallin, M. (2017). Generalized dynamic factor models and volatilities: estimation and forecasting. Journal of Econometrics, 201(2), 307-321.
    2.
  2. Chulia, H., Guillen, M., & Uribe, J. M. (2016). Modeling longevity risk with generalized dynamic factor models and vine-copulae. ASTIN Bulletin: The Journal of the IAA, 46(1), 165-190.
    3.
  3. Chuliá, H., Guillén, M., & Uribe, J. M. (2017). Measuring uncertainty in the stock market. International Review of Economics & Finance, 48, 18-33.
    4.
  4. Forni, M., & Lippi, M. (2001). The generalized dynamic factor model: representation theory. Econometric theory, 17(6), 1113-1141.
    5.
  5. Forni, M., & Reichlin, L. (1998). Let's get real: a factor analytical approach to disaggregated business cycle dynamics. The Review of Economic Studies, 65(3), 453-473.
    6.
  6. Forni, M., Hallin, M., Lippi, M., & Reichlin, L. (2000). The generalized dynamic-factor model: Identification and estimation. Review of economics and statistics, 82(4), 540-554.
    7.
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    8.
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  9. Forni, M., Hallin, M., Lippi, M., & Zaffaroni, P. (2015). Dynamic factor models with infinite-dimensional factor spaces: One-sided representations. Journal of econometrics, 185(2), 359-371.
    10.
  10. Gao, Q., & Hu, C. (2009). Dynamic mortality factor model with conditional heteroskedasticity. Insurance: Mathematics and Economics, 45(3), 410-423.
    11.
  11. Gkillas, K., Tsagkanos, A., & Vortelinos, D. I. (2019). Integration and risk contagion in financial crises: Evidence from international stock markets. Journal of Business Research, 104, 350-365.
    12.
  12. Hallin, M., & Liška, R. (2007). Determining the number of factors in the general dynamic factor model. Journal of the American Statistical Association, 102(478), 603-617.
    13.
  13. Hallin, M., Mathias, C., Pirotte, H., & Veredas, D. (2011). Market liquidity as dynamic factors. Journal of econometrics, 163(1), 42-50.
    14.
  14. Jin, X., & De Simone, F. D. A. N. (2014). Banking systemic vulnerabilities: A tail-risk dynamic CIMDO approach. Journal of Financial Stability, 14, 81-101.
    15.
  15. Nieuwenhuyze, C. V. (2005). A Generalized Dynamic Factor Model for the Belgian Economy. Journal of Business Cycle Measurement and Analysis, 2005(2), 213-247.
    16.
  16. Nieuwenhuyze, C. V. (2006). A generalized dynamic factor model for the Belgian economy-Useful business cycle indicators and GDP growth forecasts. National Bank of Belgium Working Paper, (80).
    17.
  17. Sargent, T. J., & Sims, C. A. (1977). Business cycle modeling without pretending to have too much a priori economic theory. New methods in business cycle research, 1, 145-168.
    18.
  18. Stock, J.H., and M.W. Watson (2002b), “Macroeconomic Forecasting Using Diffusion Indices,” Journal of Business and Economic Statistics, 20, 147-162.
    19.
  19. Stock, J.H., and M.W. Watson, (2002a), “Forecasting Using Principal Components from a Large Number of Predictors,” Journal of the American Statistical Association, 97, 1167-1179.
    20.
  20. Stock, J.H., and M.W. Watson. (2011). Dynamic factor models. Oxford handbook on economic forecasting, 2011.
    21.

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