تبیین رابطه ترکیب ریسک نامطلوب و ریسک مطلوب در پیش بینی نوسانات بازده بازار

محورهای موضوعی : مهندسی مالی

حسین راد کفترودی ¹ (گروه مدیریت.واحد رشت، دانشگاه آزاد اسلامی، رشت، ایران.)
محمدحسن قلی زاده ² (گروه مدیریت، دانشکده ادبیات و علوم انسانی، دانشگاه گیلان، رشت،ایران)
مهدی فدایی ³ (گروه مدیریت، واحدرشت، دانشگاه آزاداسلامی، رشت، ایران)

تاریخ دریافت : 1398/11/04 تاریخ پذیرش : 1399/03/13 تاریخ انتشار : 1399/10/01

کلید واژه: مدل خود رگرسیون برداری, ریسک نامطلوب, ریسک مطلوب, پیش بینی نوسانات بازده بازار,

چکیده مقاله :

هدف از انجام این تحقیق تبیین رابطه ریسک نامطلوب و ریسک مطلوب در پیش بینی نوسانات بازده بازار می باشد. این تحقیق از لحاظ ماهیت از نوع توصیفی و از لحاظ هدف کاربردی است. جامعه آماری تحقیق ، شرکت های پذیرفته شده در بورس اوراق بهادار تهران و نمونه مورد نظر شرکت های پذیرفته شده در صنعت سیمان هستند که داده های مورد نیاز تحقیق از آن ها قابل استخراج است. دوره زمانی تحقیق، از سال 1392 تا سال 1397 می باشد. این تحقیق دارای مدلی نظری است و برای آزمون فرضیه ها از مدل خود رگرسیون برداری استفاده گردید. در صنعت سیمان با توجه به آماره t و جهت ضریب آن مشخص می شود متغیر پیش بینی نوسانات بازده بازار با ریسک نامطلوب و ریسک مطلوب ایجاد همبستگی می کند. همچنین مقدار ضریب تعیین تعدیل شده در این رابطه 51 درصد می باشد که میزان این تاثیرگذاری را نشان می دهد.

چکیده انگلیسی:

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.

منابع و مأخذ:

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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.


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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.


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19. Gourieroux, C., Sufana, R.(2010). Derivative pricing with Wishart multivariate stochastic volatility. J. Bus. Econom. Statist. 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). Modelling 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.

 

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.