The Explanation of the Relationship between Downside Risk and Upside Risk combination in predicting Market Return Volatility
Subject Areas : Financial engineering
hossein
rad kaftroudi
1
(Department of management. rasht branch, Islamic Azad University, Rasht. Iran)
mohammadhasan
gholizadeh
2
(Department of management Faculty of Literature and Humanities. Guilan University. Rasht. Iran)
mahdi
fadaei
3
(Department of management. rasht branch, Islamic Azad University, Rasht. Iran)
Keywords: Downside risk, upside Risk, Forecasting Volatility of Market Returns, Vector Auto-Regression Model,
Abstract :
The volatility of financial returns plays an important role in many empirical applications, such as portfolio allocation, risk management and derivative pricing. The purpose of this research is to explain the relationship between undesirable risk and desirable risk in predicting market return volatility. The research is descriptive in nature and applied in purpose. The statistical population of the study is the companies listed in Tehran Stock Exchange and the target sample of the companies listed in the cement industry from which the required research data can be extracted. The research period is from 1392 to 1397. This research has a theoretical model and the self-regression model was used to test the hypotheses. In the cement industry, according to the t-statistic and its coefficient of determination, it is clear that the predictor of market yield fluctuations correlates with undesirable and desirable risk. Also, the adjusted coefficient of determination is 51%, which indicates this effect.
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1. Roudpashti, Fereydoun, Hemti, Asiabarghi Mehdi, Shabani, Barzegar Laleh, Khaksarian, Fatemeh (2016). Mean-variance test based on downside risk theoretical framework using vector autoregression (VAR) model. Investment Knowledge Journal, Summer 2016, Volume 6, Number 22 #l00284; From page 29 to page 48.
2. Sheikh, Reza, Ameri Rad Kayseri, Behnaz. (2015). Analysis of asset and debt management with a fuzzy multi-objective group decision-making approach. Asset Management and Financing, 4(4), 61-78. doi: 10.22108/amf.2016.21113
3. Alizadeh, S., Brandt, M., & Diebold, F. (2002). Range-based estimation of stochastic volatility models. Journal of Finance, 57, 1047–1092.
4. Ang, A., Chen, J. (2002). Asymmetric correlations of equity portfolios. J. Financ. Econ. 63, 443–494.
5. Barndorff-Nielsen, O., Shephard, N. (2004). Econometric analysis of realized covariance: high frequency covariance, regression and correlation in financial economics. Econometrica 72, 885–925.
6. Bollerslev, T., Mikkelsen, H.O. (1996). Long-memory modeling and pricing in stock market volatility. Journal of Econometrics 73, 151–184.
7. Cerny, A., Kallsen, J. (2008). Mean-variance hedging and optimal investment in Heston's model with correlation. Math. Finance 18 (3), 473–492.
8. Chacko, G., Viceira, L., (2005). Dynamic consumption and portfolio choice with stochastic volatility in incomplete markets. Rev. Financ. Stud. 18, 1369–1402.
9. Chen, P., Yang, H., Yin, G. (2008). Markowitz's mean-variance asset-liability management with regime switching: a continuous-time model. Insurance Math. Econom. 43, 456–465.
10. Chiu, M.C., Wong, H.Y., (2013). Mean-variance principle of managing co-integrated risky assets and random liabilities. Oper. Res. Lett. 41, 98–106.
11. Chiu, M.C., Wong, H.Y.(2012). Mean-variance asset-liability management: cointegrated assets and insurance liabilities. European J. Oper. Res. 223, 785–793.
12. Chiu, M.C., Li, D. (2009). Asset-liability management under the safety-first principle. J. Optim. Theory Appl. 143, 455–478.
13. Chiu, M.C., Wong, H.Y., Zhao, J.(2014). Commodity derivatives pricing with cointegration and stochastic covariances. Working Paper of the Chinese University of Hong Kong.
14. Christoffersen, P.F., Heston, S., Jacobs, K. (2009). The shape and term structure of the index option smirk: why multifactor stochastic volatility models work so well. Manage. Sci. 55, 1914–1932.
15. Degiannakis, S., & Livada, A. (2013). Realized volatility or price range: Evidence from a discrete simulation of the continuous time diffusion process. Economic Modelling, 30, 212–216.
16. Dutta, Goutam; V. Rao, Harish ; Basu, Sankarshan ; Tiwari, Manoj Kr(2019). Asset liability management model with decision support system for life insurance companies: Computational results. Computers & Industrial Engineering Volume 128, February 2019, Pages 985-998.
17. Fernández, José L & M. Ferreiro-Ferreiro, Rodríguez, Ana & José A. García & Vázquez, Carlos (2018). GPU parallel implementation for asset-liability management in insurance companies. Journal of Computational Science Volume 24, January 2018, Pages 232-254.
18. Gourieroux, C., Joan, J., Sufana, R. (2009). The Wishart autoregressive process of multivariate stochastic volatility. J. Econometrics 150, 167–181.
19. Gourieroux, C., Sufana, R. (2010). Derivative pricing with Wishart multivariate stochastic volatility. J. Bus. Econom. Statistics. 28, 438–451.
20. Li, Danping; Shen, Yang & Zeng, Yan(2018). Dynamic derivative-based investment strategy for mean-variance asset-liability management with stochastic volatility. Insurance: Mathematics and Economics Volume 78, January 2018, Pages 72-86.
21. Longin, F., Solnik, B. (1995). Is the correlation in international equity returns constant: 1960-1990? J. Int. Money Finance 14, 3–26.
22. Moskowitz, T. (2003). An analysis of covariance risk and pricing anomalies. Rev. Financ. Stud. 16, 417–457.
23. Parkinson, M. (1980). The extreme value method for estimating the variance of the rate of return. Journal of Business, 53, 61–65.
24. Taylor, S. J. (1986). Modeling financial time series. Chichester: John Wiley and Sons.
25. Wang, J., Forsyth, P.A. (2011). Continuous time mean variance asset allocation: a time consistent strategy. European J. Oper. Res. 209, 184–201.
26. Zhou, X. Y., Li, D. (2000). Continuous-time mean–variance portfolio selection: a stochastic LQ framework. Appl. Math. Optim. 42, 19–33.