IDEAS home Printed from https://ideas.repec.org/a/nax/conyad/v59y2014i3p165-195.html
   My bibliography  Save this article

Análisis del riesgo de mercado de los fondos de pensión en México Un enfoque con modelos autorregresivos

Author

Listed:
  • Martínez Preece Marissa R.

    (Universidad Autónoma Metropolitana)

  • Venegas Martínez Francisco

    (Instituto Politécnico Nacional)

Abstract

The aim of this paper is to analyze the market risk of two types of investment funds, Basic SIEFORE 1 (SB1) and Basic SIEFORE 2 (SB2). To do this, we propose a performance index that will be used in ARIMA-GARCH models and some of its extensions, with the purpose of examining the dynamic behavior of the returns and their volatility on such investment funds. Moreover, the risk premium of both types of funds is analyzed. One of the relevant research results is that yields obtained by these funds in the period studied, are not sufficient to offset the additional risk assumed by the pension funds including equity components. Finally, some remarks are made, on investment policy, about the market risk and how it is being measured and managed in these funds.

Suggested Citation

  • Martínez Preece Marissa R. & Venegas Martínez Francisco, 2014. "Análisis del riesgo de mercado de los fondos de pensión en México Un enfoque con modelos autorregresivos," Contaduría y Administración, Accounting and Management, vol. 59(3), pages 165-195, julio-sep.
  • Handle: RePEc:nax:conyad:v:59:y:2014:i:3:p:165-195
    as

    Download full text from publisher

    File URL: http://www.cya.unam.mx/index.php/cya/article/view/78
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    2. Nicholas Barr & Peter Diamond, 2009. "Reforming pensions: Principles, analytical errors and policy directions," International Social Security Review, John Wiley & Sons, vol. 62(2), pages 5-29, April.
    3. Marco A. Espinosa-Vega & Tapen Sinha, 2000. "A primer and assessment of social security reform in Mexico," Economic Review, Federal Reserve Bank of Atlanta, vol. 85(Q1), pages 1-23.
    4. Engle, Robert F & Lilien, David M & Robins, Russell P, 1987. "Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model," Econometrica, Econometric Society, vol. 55(2), pages 391-407, March.
    5. Robert Holzmann, 1997. "Pension Reform, Financial Market Development, and Economic Growth: Preliminary Evidence from Chile," IMF Staff Papers, Palgrave Macmillan, vol. 44(2), pages 149-178, June.
    6. Blake, David & Cairns, Andrew J. G. & Dowd, Kevin, 2001. "Pensionmetrics: stochastic pension plan design and value-at-risk during the accumulation phase," Insurance: Mathematics and Economics, Elsevier, vol. 29(2), pages 187-215, October.
    7. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Yuming, 1998. "Expected stock returns, risk premiums and volatilities of economic factors1," Journal of Empirical Finance, Elsevier, vol. 5(2), pages 69-97, June.
    2. Alagidede, Paul & Panagiotidis, Theodore, 2009. "Modelling stock returns in Africa's emerging equity markets," International Review of Financial Analysis, Elsevier, vol. 18(1-2), pages 1-11, March.
    3. Chang, Chia-Lin & Hsu, Hui-Kuang, 2013. "Modelling Volatility Size Effects for Firm Performance: The Impact of Chinese Tourists to Taiwan," MPRA Paper 45691, University Library of Munich, Germany.
    4. repec:wyi:journl:002087 is not listed on IDEAS
    5. Asai, Manabu & McAleer, Michael, 2015. "Leverage and feedback effects on multifactor Wishart stochastic volatility for option pricing," Journal of Econometrics, Elsevier, vol. 187(2), pages 436-446.
    6. Christensen, Bent Jesper & Nielsen, Morten Ørregaard & Zhu, Jie, 2015. "The impact of financial crises on the risk–return tradeoff and the leverage effect," Economic Modelling, Elsevier, vol. 49(C), pages 407-418.
    7. Piotr Fiszeder & Witold Orzeszko, 2012. "Nonparametric Verification of GARCH-Class Models for Selected Polish Exchange Rates and Stock Indices," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 62(5), pages 430-449, November.
    8. Levent, Korap, 2009. "Enflasyon ve enflasyon belirsizliği ilişkisi için G7 ekonomileri üzerine bir inceleme [An investigation for the inflation and inflation uncertainty relationship upon the G7 economies]," MPRA Paper 19478, University Library of Munich, Germany.
    9. Bauer, Rob M M J & Nieuwland, Frederick G M C & Verschoor, Willem F C, 1994. "German Stock Market Dynamics," Empirical Economics, Springer, vol. 19(3), pages 397-418.
    10. Christensen, Bent Jesper & Nielsen, Morten Ørregaard & Zhu, Jie, 2010. "Long memory in stock market volatility and the volatility-in-mean effect: The FIEGARCH-M Model," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 460-470, June.
    11. Hunjra, Ahmed Imran & Azam, Muhammad & Niazi, Ghulam Shabbir Khan & Butt, Babar Zaheer & Rehman, Kashif-Ur- & Azam, Rauf i, 2010. "Risk and return relationship in stock market and commodity prices: a comprehensive study of Pakistani markets," MPRA Paper 40662, University Library of Munich, Germany.
    12. Nora Abu Asab & Juan Carlos Cuestas & Alberto Montagnoli, 2018. "Inflation targeting or exchange rate targeting: Which framework supports the goal of price stability in emerging market economies?," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-21, August.
    13. Li, Qi & Yang, Jian & Hsiao, Cheng & Chang, Young-Jae, 2005. "The relationship between stock returns and volatility in international stock markets," Journal of Empirical Finance, Elsevier, vol. 12(5), pages 650-665, December.
    14. Mehmet Sahiner, 2022. "Forecasting volatility in Asian financial markets: evidence from recursive and rolling window methods," SN Business & Economics, Springer, vol. 2(10), pages 1-74, October.
    15. De Santis, Giorgio & imrohoroglu, Selahattin, 1997. "Stock returns and volatility in emerging financial markets," Journal of International Money and Finance, Elsevier, vol. 16(4), pages 561-579, August.
    16. Papantonis, Ioannis & Rompolis, Leonidas & Tzavalis, Elias, 2023. "Improving variance forecasts: The role of Realized Variance features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1221-1237.
    17. Sheriffdeen A. Tella & Olumuyiwa G. Yinusa & Ayinde Taofeek Olusola & Saban Celik, 2011. "Global Economic Crisis And Stock Markets Efficiency: Evidence From Selected Africa Countries," Bogazici Journal, Review of Social, Economic and Administrative Studies, Bogazici University, Department of Economics, vol. 25(1), pages 139-169.
    18. Lütkepohl,Helmut & Krätzig,Markus (ed.), 2004. "Applied Time Series Econometrics," Cambridge Books, Cambridge University Press, number 9780521547871.
    19. Ilu, Ahmad Ibraheem, 2020. "Exchange Rate Pass through to Stock Prices: A Multi GARCH Approach," MPRA Paper 98442, University Library of Munich, Germany.
    20. Jorge Caiado, 2004. "Modelling And Forecasting The Volatility Of The Portuguese Stock Index Psi-20," Portuguese Journal of Management Studies, ISEG, Universidade de Lisboa, vol. 9(1), pages 3-21.
    21. Christian M. Dahl & Emma M. Iglesias, 2008. "The limiting properties of the QMLE in a general class of asymmetric volatility models," CREATES Research Papers 2008-38, Department of Economics and Business Economics, Aarhus University.

    More about this item

    Keywords

    fondos de pensiones; SIEFORES; esquema de jubilación; riesgo de mercado; premio al riesgo;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nax:conyad:v:59:y:2014:i:3:p:165-195. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Alberto García-Narvaez (Technical Editor) (email available below). General contact details of provider: https://edirc.repec.org/data/fcunamx.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.