Volume 11, Issue 41 (10-2020)
jemr 2020, 11(41): 197-229 |
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Rostami M, Makiyan S N. Modeling Stock Return Volatility Using Symmetric and Asymmetric Nonlinear State Space Models: Case of Tehran Stock Market. jemr 2020; 11 (41) :197-229
URL: http://jemr.khu.ac.ir/article-1-1912-en.html
URL: http://jemr.khu.ac.ir/article-1-1912-en.html
1- Yazd University
2- University of Yazd ,nmakiyan@yazd.ac.ir
2- University of Yazd ,
Abstract: (3853 Views)
Volatility is a measure of uncertainty that plays a central role in financial theory, risk management, and pricing authority. Turbulence is the conditional variance of changes in asset prices that is not directly observable and is considered a hidden variable that is indirectly calculated using some approximations. To do this, two general approaches are presented in the literature of financial economics for modeling and calculating volatility. In the first approach, conditional variance is modeled as a function of the square of the past shocks of return on assets. Models of the GARCH type fall into this category. In the alternative approach, volatility is assumed to be a random variable, which evolves using nonlinear patterns of Gaussian state space. This type of model is known as Stochastic Volatility (SV). Because, SV models include two kinds of noise processes, one for observations and another for hidden, volatility, thus, they are more realistic and more flexible in calculating volatility than GARCH type. This study attempts to analyze the volatility in stock returns of 50 companies, which are active in Tehran Stock Market using symmetric and asymmetric methods of Stochastic Volatility, which is different in the presence of leverage effect. The empirical comparison of these two models by calculating the posterior probability of accuracy of each model using the MCMC Bayesian method represents a significant advantage of the ASV model. The results in both symmetric and asymmetric methods represent the very high stability of the volatility generated by the shocks on stock returns; therefore, the Tehran Stock market changes in returns due to this high sustainability will be predictable.
Keywords: Symmetric and Asymmetric Nonlinear States, Stochastic Volatility, Stock market, Bayesian Method
Type of Study: Applicable |
Subject:
پولی و مالی
Received: 2020/03/6 | Accepted: 2020/11/21 | Published: 2021/01/10
Received: 2020/03/6 | Accepted: 2020/11/21 | Published: 2021/01/10
References
1. Adabi firouzjaee B, Mehrara M, Mohammadi S. Estimation and Evaluation of Tehran Stock Exchange Value at Risk Based on Window Simulation Method. jemr. 2016; 6 (23) :35-73(in Persian) [DOI:10.18869/acadpub.jemr.6.23.35]
2. Behradmehr N, mehrara M, mazraati M, dadafarid H. Forecasting Risk Premium in Crude Oil futures Market with BVAR. jemr. 2017; 8 (29) :7-35(in Persian) [DOI:10.29252/jemr.8.29.7]
3. Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327. [DOI:10.1016/0304-4076(86)90063-1]
4. Bollerslev, T., Engle, R.F. and Nelson, D.B. (1994). ARCH Models, in R.F. Engle and D. McFadden (Eds.), Handbook of Econometrics Vol. IV, Amsterdam: North-Holland, PP. 2959-3038. [DOI:10.1016/S1573-4412(05)80018-2]
5. Chou, R.Y. (1988). Volatility Persistence and Stock Valuations: Some Empirical Evidence Using GARCH, Journal of Applied Econometrics, 3, 279-294. [DOI:10.1002/jae.3950030404]
6. Engle RF (2004). Risk and Volatility: Econometric Models and Financial Practice. The American Economic Review, 94(3), and 405{420. DOI: 10.1257/0002828041464597. [DOI:10.1257/0002828041464597]
7. Engle, R.F. and Ng, V. (1993). Measuring and Testing the Impact of News on Volatility, Journal of Finance, 48, 1749-1778. [DOI:10.1111/j.1540-6261.1993.tb05127.x]
8. Engle, R.F., Ng, V.K. and Rothschild, M. (1990a). Asset Pricing with a Factor-ARCH Covariance Structure, Journal of Econometrics, 45, 235-237. [DOI:10.1016/0304-4076(90)90099-F]
9. Fama, E.F. (1965). The Behavior of Stock-Market Prices, Journal of Business, 38, 34-105. [DOI:10.1086/294743]
10. Geweke, J. and Terui, N. (1993). Bayesian Threshold Auto-Regressive Models for Nonlinear Time Series, Journal of Time Series Analysis, 14, 441-454. [DOI:10.1111/j.1467-9892.1993.tb00156.x]
11. Glosten LR, Jaganathan R, Runkle DE (1993). On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. Journal of Finance, 48(5), 1779{1801. [DOI:10.1111/j.1540-6261.1993.tb05128.x]
12. Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14 (4), 382-417. [DOI:10.1214/ss/1009212519]
13. Jeffreys, H. (1939). Theory of Probability. Oxford: Oxford University Press.
14. Kim, S., Shephard, N., & Chib, S. (1998). Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models. Review of Economic Studies, 65, 361-393. [DOI:10.1111/1467-937X.00050]
15. Lee, S. W., & Hansen, B. E. (1994). Asymptotic Theory for the GARCH (1, 1) Quasi-Maximum Likelihood Estimator. Econometric Theory, 10, 29-52. [DOI:10.1017/S0266466600008215]
16. Mandelbrot, B. (1963). The Variation of Certain Speculative Prices, Journal of Business, 36, 394-419. [DOI:10.1086/294632]
17. Mandelbrot, B. (1963). The Variation of Certain Speculative Prices, Journal of Business, 36, 394-419. [DOI:10.1086/294632]
18. Nelson D.B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347{370. [DOI:10.2307/2938260]
19. Nelson, D.B. (1991). Conditional Heteroscedasticity in Asset Returns: A New Approach, Econometrica, 59, 347-370. [DOI:10.2307/2938260]
20. Nelson, D.B. and Foster, D.P. (1994). Asymptotic Filtering Theory for Univariate ARCH Models, Econometrica, 62, 1-41. [DOI:10.2307/2951474]
21. Osiewalski, J. (2001). Ekonometria Bayesowska w Zastosowaniach, [Bayesian Econometrics in Applications]. Cracow: Cracow University of Economics.
22. Schwert, G.W. (1989). Why Does Stock Market Volatility Change Over Time? Journal of Finance, 44, 1115-1153. [DOI:10.1111/j.1540-6261.1989.tb02647.x]
23. Sims, C.A. (1988), Bayesian Skepticism on Unit Root Econometrics, Journal of Economic Dynamics and Control, 12, 463-474. [DOI:10.1016/0165-1889(88)90050-4]
24. Stock, J.H. and Richardson, M.P. (1989). Drawing Inferences from Statistics Based on Multi-Year Asset Returns, Journal of Financial Economics, 25, 323-348. [DOI:10.1016/0304-405X(89)90086-X]
25. Taylor, S.J (1986). Modelling Financial Time Series. John Wiley, New York.
26. Withers, S. D. (2002). Quantitative Methods: Bayesian Inference, Bayesian Thinking, Progress in Human Geography, 26 (4), 553-566. [DOI:10.1191/0309132502ph386pr]
27. Zhong, M., Darrat, A. F., & Anderson, D. C. (2003). Do US stock prices deviate from their fundamental values? Some new evidence. Journal of banking & finance, 27(4), 673-697. [DOI:10.1016/S0378-4266(01)00259-X]
28. Chen, L., Zerilli, P., & Baum, C. F. (2019). Leverage effects and stochastic volatility in spot oil returns: A Bayesian approach with VaR and CVaR applications. Energy Economics, 79, 111-129. [DOI:10.1016/j.eneco.2018.03.032]
29. Lin, Y., Xiao, Y., & Li, F. (2020). Forecasting crude oil price volatility via a HM-EGARCH model. Energy Economics, 104693 [DOI:10.1016/j.eneco.2020.104693]
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