Value Risk Assessment of Stock Indexes Based on Parametric, Quasi-Parametric and Nonparametric Approaches (Tehran Stock Exchange Study)
Subject Areas : Financial engineering
ebrahim
ghanbari memeshi
1
(Department of Finance, Babol branch, Islamic Azad University, Mazandaran, Iran.)
seyyed ali
nabavi chashmi
2
(Department of Finance, Babol branch, Islamic Azad University, Mazandaran, Iran.)
erfan
memarian
3
(Department of Economy, Babol branch, Islamic Azad University, Mazandaran, Iran)
Keywords: Value at Risk, stock exchange, parametric, Semi-Parametric, Quasi-Parametric,
Abstract :
The purpose of the present study is to evaluate the value at risk of stock indexes based on parametric, quasi-parametric and non-parametric approaches in Tehran Stock Exchange on the basis of data collected during the period of 2009-2010. The purpose of this study is practical. On the other hand, the present study is empirically oriented epistemologically, its inductive reasoning system, and field-library study using causal-historical information (ie, past information). In this regard, the performance of each of the above approaches was evaluated and finally the accuracy of accuracy was evaluated by the Basel Committee test and Bin, POF and TUFF frequency tests. The results show that parametric, quasi-parametric and semi-parametric models have priority in terms of efficiency and accuracy, respectively. In addition, the results from another perspective show that non-parametric and semi-parametric models based on error ratio and post hoc tests have overestimated the value of risk exposure, although the contribution of nonparametric model is higher
_||_
1. Adabi Firouzjaei, Bagher, Mehrara, Mohsen, Mohammadi, Shapour, (2015), forecasting and evaluating the value at risk one step ahead of Tehran Stock Exchange using Monte Carlo Markov chain simulation method, Journal of Financial Engineering and Securities Management, number twenty-six.
2. Bastanzad, Hossein, Davodi, Pedram, (2016), Investigating the mechanism of risk transfer between the currency, housing and stock markets of the Iranian economy (using the parametric and non-parametric approach of value at risk), asset management and financing. Fifth year, winter 2016, number 4 (consecutive 19)
3. Heydari, Hadi, Keshavarz, Gholamreza, (2016), Ranking parametric models of value at risk by taking into account the trading position of the shareholder (use of asymmetric distribution functions in family models (GARCH), Economic Research Quarterly, Volume: 17, No. : 66.
4. Dehghan Monshadi, Samaneh, Abdul Rahimian, Mohammad Hossein, (2016), Application of Value at Differential Risk (IVaR), in calculating the risk of investment portfolio using the previous and posterior approach, Strategic Management Thought, 11th year, Fall and Winter 2016 , number 2 (22 in a row)
5. Rai, Reza and Ahmad Telangi (2004), Advanced Investment Management, Samit Publications, Tehran.
6. Rahnema Roudpashti, Fereydoun, Kandahari, Sharareh, (2014), estimation of value at risk based on constraints on performance evaluation of active portfolio management in Tehran Bahadur Stock Exchange, Journal of Financial Engineering and Securities Management, number twenty-four.
7. Shahiki Tash, Mohammadnabi, Moulai, Saber and Halajzadeh, Zainab (2012), forecasting the general level of prices and inflation in Iran using a neural network, Strategic and Macro Policy Quarterly, (4), 51-67
8. Fallah Shams, Mirfaiz, Thaghafi, Ali, Nasserpour, Alireza, (2016), Estimating the security of futures contracts with a value-at-risk approach and emphasizing the generalized Pareto distribution, Stock Exchange, 9th year, Spring 2015, no. 33.
9. Fallah Shams, Mirfaiz, (2018), a comparative study of the effectiveness of the risk measurement model and the GARCH econometric model in predicting market risk in the Tehran Stock Exchange, Journal of Financial Engineering and Securities Management, number five.
10. Naderi Nur Aini, Mohammad Mahdi, (2017), choosing the best method for calculating the value at risk of investment funds, asset management and financing, volume 6, number 1 - serial number 20, spring 2018, page 159-180, 10.22108/amf 2017.21353 DOI:
11. Nikumram, Hashem, Zamardian, Gholamreza, (2013), Investigating the explanatory power of econometric models in measuring the amount of value at risk of investment companies' portfolios to determine the optimal portfolio in the Iranian capital market, Investment Knowledge Quarterly, third year, no. twelfth.
12. Barone-Adesi, G., Giannopoulos, K., Vosper, L., (1999). VaR without correlations for nonlinear portfolios. Journal of Futures Markets 19, 583–602
13. Estrada, J. (2007), Mean-semivariance behavior: Downside risk and capital asset pricing, International Review of Economics and Finance, vol. 16, pp. 169-185.
14. Gregoriou, Greg.N, (2009), The VaR Implementation Handbook, Volume I, McGraw-Hill, Inc.
15. Glosten L.R., Jagannathan R., and Runkle DE. 1993. On the relationship between expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48: 1779–1801.
16. LI, VIVIANA (2012) Assessing the Performance of Value at Risk Models in Hang Seng Index and China Securities Index. Dissertation University of Nottingham.
17. NursultanAbdrashev, (2016), Comparison of Islamic and conventional bank stocks by Value-At-Risk method, Moscow State University - Faculty of Economics.
18. Zakoian J.M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18: 931–995.
