Volume 12, Issue 44 (7-2021)                   jemr 2021, 12(44): 191-212 | Back to browse issues page


XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Khodam M, Nosratian Nasab M, Jafari Samimi A. Expected Shortfall in Tehran Stock Exchange (Dynamic Semi-Parametric Approach). jemr 2021; 12 (44) :191-212
URL: http://jemr.khu.ac.ir/article-1-2089-en.html
1- Islamic Azad University Khomein Branch
2- University of Mazandaran
3- University of Mazandaran , jafarisa@umz.ac.ir
Abstract:   (1488 Views)
Considering the challenges related to estimating and forecasting the expected Shortfall dynamically and with a semi-parametric approach, in this study, providing a general framework, dynamic semi-parametric models in forecasting Expected Shortfall in Tehran Stock Exchange be introduced and evaluated. In this regard, the data of the period 2008.12.04-2020.08.26 and Generalized Autoregressive Score (GAS) approach are used to introducing dynamic semi-parametric models (GAS-2F, GAS-1F, GARCH-FZ and hybrid). Then expected Shortfall (ES) in Tehran Stock Exchange be estimated  and forecasting performance of these models are compared with traditional models in this field, including GARCH models and rolling window models based on backtesting their results. The results of this study indicate better performance of dynamic semi-parametric models in forecasting the expected Shortfall (ES) than competing models. In addition, the GAS-1F model has shown the best performance among all models.
Full-Text [PDF 1106 kb]   (483 Downloads)    
Type of Study: Applicable | Subject: پولی و مالی
Received: 2021/02/28 | Accepted: 2021/11/23 | Published: 2022/01/25

References
1. Adabi firouzjaee B, Mehrara M, Mohammadi S. (2016). Estimation and Evaluation of Tehran Stock Exchange Value at Risk Based on Window Simulation Method. Journal of Economic Modeling Research. 6 (23), 35-73. (in Persian) [DOI:10.18869/acadpub.jemr.6.23.35]
2. Andersen, T.G., Bollerslev, T., Christo¤ersen, P., Diebold, F.X. (2006). Volatility and Correlation Forecasting, in (ed.s) G. Elliott, C.W.J. Granger, and A. Timmermann, Handbook of Economic Forecasting, Vol. 1. Elsevier, Oxford. [DOI:10.1016/S1574-0706(05)01015-3]
3. Asayesh K, Fallahshams M, Jahangirnia H, Gholami Jamkarani R. (2020) Explaining the Systemic Risk Model Using the Marginal Expected Shortfall Approach (MES) for the Banks Listed on the Tehran Stock Exchange. JPBUD; 25 (2) :115-134. (in Persian) [DOI:10.52547/jpbud.25.2.115]
4. Babalooyan, S., & Nikoomaram, H., & Vakilifard, H., & Rahnamay Roodposhti, F. (2020). Evaluating Value at Risk and Expected Shortfall for Tehran and International Stock Markets (Conditional Extreme Value Theory). JOURNAL OF FINANCIAL ECONOMICS (FINANCIAL ECONOMICS AND DEVELOPMENT), 14(52 ), 55-80. (in Persian)
5. Bu, D., Liao, Y., Shi, J., & Peng, H. (2019). Dynamic expected shortfall: A spectral decomposition of tail risk across time horizons. Journal of Economic Dynamics & Control, 108 (2019) 103753. [DOI:10.1016/j.jedc.2019.103753]
6. Creal, D.D., S.J. Koopman, and A. Lucas (2013). Generalized Autoregressive Score Models with Applications, Journal of Applied Econometrics, 28(5), 777-795. [DOI:10.1002/jae.1279]
7. Creal, D.D., S.J.Koopman, A. Lucas, & M. Zamojski (2015). Generalized Autoregressive Method of Moments, Tinbergen Institute Discussion Paper, TI 2015-138/III. [DOI:10.2139/ssrn.2718186]
8. Diebold, F.X. and R.S. Mariano. (1995). Comparing predictive accuracy, Journal of Business & Economic Statistics,13(3), 253.263. [DOI:10.1080/07350015.1995.10524599]
9. Engle, R.F. and S. Manganelli (2004a). CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles, Journal of Business & Economic Statistics, 22, 367-381. [DOI:10.1198/073500104000000370]
10. Fallahshams, M., Saghafi, A., Naserpoor, A. (2016). Futures Contracts Margin Setting by General Pareto Distribution VaR, Journal of Securities Exchange, 9(33), 25-45. (in Persian)
11. Fissler, T., and J. F. Ziegel. (2016) Higher order elicitability and Osband.s principle, Annals of Statistics, 44(4), 1680-1707. [DOI:10.1214/16-AOS1439]
12. Gerlach,R., Wang,C. (2020). Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting, incorporating realized measures, International Journal of Forecasting, 36, (2), 489-506. [DOI:10.1016/j.ijforecast.2019.07.003]
13. Gneiting, T. (2011). Making and evaluating point forecasts. Journal of the American Statistical Association, 106(494), 746-762. [DOI:10.1198/jasa.2011.r10138]
14. Gregoriou, Greg.N. (2009). The VaR Implementation Handbook, Volue I, McGraw-Hill,Inc.
15. Heidari, H., K. Haddad, G. (2017). Ranking of Parametric Value at Risk Models with Consideration of Trader Position (Application of Asymmetric Distribution Functions in GARCH Models). Economics Research, 17(66), 151-178. (in Persian)
16. Hallin, Marc & Trucíos, Carlos . (2020). Forecasting Value-at-Risk and Expected Shortfall in Large Portfolios: a General Dynamic Factor Approach, Working Papers ECARES 2020-50, Universite Libre de Bruxelles. [DOI:10.2139/ssrn.3748736]
17. Meharani, A., Najafi Moghadam, A., Baghani, A. (2021). Estimation value at risk (VAR) and conditional value at risk (CoVaR) at Tehran Stock Exchange by approach to using Fréchet distribution (FD). Financial Engineering and Protfolio Management, 12(46),449-475. (in Persian)
18. Naderi Nooreini, M. (2018). The Best Methodology of Estimation of Value-at-Risk in Iranian Mutual Funds. Asset Management and Financing, 6(1), 159-180. (in Persian)
19. Naseri S A, Jabal Ameli F, Barkhordary Dorbash S. (2020). Investigating the Correlation of Selected Banks with Dynamic Conditional Correlation (DCC) Model and Identifying Systemically Important Banks with Conditional Value at Risk and Shapley Value Method. Journal of Economic Modeling Research. 11 (41) :145-196. (in Persian)
20. Nikola Radivojevi , Milena Cvjetkovi ,Saša Stepanov. (2016). The new hybrid value at risk approach based on the extreme value theory, Estudios the Economia.43, 29-52. [DOI:10.4067/S0718-52862016000100002]
21. Patton, A.J. , Ziegel, J.F. , Chen, R. (2018). Dynamic semiparametric models for expected shortfall (and Value-at-Risk). J. Econom. 211 (2), 388-413 [DOI:10.1016/j.jeconom.2018.10.008]
22. R. Roodposhti, F., Klantari Dehaghi, M. (2014). Investigation of Multifractaly Models in Finance. Financial Knowledge of Securities Analysis, 7(24), 25-47. (in Persian)
23. Saranj, A., Nourahmadii, M. (2016). Estimating of value at risk and expected shortfall by using conditional extreme value approach in Tehran Securities Exchange. Financial Research Journal, 18(3), 437-460. (in Persian)
24. Storti, G. and C. Wang (2021). Nonparametric expected shortfall forecasting incorporating weighted quantiles. International Journal of Forecasting. In press. [DOI:10.1016/j.ijforecast.2021.04.004]

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2024 CC BY-NC 4.0 | Journal of Economic Modeling Research

Designed & Developed by : Yektaweb