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Some comments on risk

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  • Clive W. J. Granger

    (Department of Economics, University of California, San Diego, La Jolla, CA 92093-0508 USA)

Abstract

Investor risk is a complicated concept in practice and is not well captured by measures of volatility as is well understood by uncertainty theory. Rather than asking statisticians to attempt to measure risk, it may be better to listen to decision theorists, but their suggestions are not very practical. Diversification is clearly helpful in reducing risk but the risk level of one portfolio cannot be measured without knowing the risks of other major portfolios. A meta-analysis can be used to compare alternative volatility measures in terms of their forecasting utility. Copyright © 2002 John Wiley & Sons, Ltd.

Suggested Citation

  • Clive W. J. Granger, 2002. "Some comments on risk," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 447-456.
    • Handle: RePEc:jae:japmet:v:17:y:2002:i:5:p:447-456
    DOI: 10.1002/jae.687
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    References listed on IDEAS

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    Cited by:

    1. Antonio A. F. Santos, 2021. "Bayesian Estimation for High-Frequency Volatility Models in a Time Deformed Framework," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 455-479, February.
    2. Kumar, Dilip, 2015. "Sudden changes in extreme value volatility estimator: Modeling and forecasting with economic significance analysis," Economic Modelling, Elsevier, vol. 49(C), pages 354-371.
    3. Gustavo Cabrera González, 2019. "Modeling and Projection of the Mexican Exchange Rate (Peso/Dollar): a Bayesian Approach for Model Selection," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 14(2), pages 203-219, Abril-Jun.
    4. Dilip Kumar, 2016. "Sudden changes in crude oil price volatility: an application of extreme value volatility estimator," American Journal of Finance and Accounting, Inderscience Enterprises Ltd, vol. 4(3/4), pages 215-234.
    5. Alex Luiz Ferreira, 2004. "Leaning Against the Parity," Studies in Economics 0413, School of Economics, University of Kent.
    6. Gonzalo, Jesús & Olmo, José, 2008. "Testing downside risk efficiency under market distress," UC3M Working papers. Economics we084321, Universidad Carlos III de Madrid. Departamento de Economía.
    7. Granger, Clive W.J., 2005. "The past and future of empirical finance: some personal comments," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 35-40.
    8. Rua, António & Nunes, Luis C., 2012. "A wavelet-based assessment of market risk: The emerging markets case," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(1), pages 84-92.
    9. Yang Yang & Tae-Hwy Lee, 2004. "Bagging Binary Predictors for Time Series," Econometric Society 2004 Far Eastern Meetings 512, Econometric Society.
    10. Nieto, María Rosa & Ruiz Ortega, Esther, 2008. "Measuring financial risk : comparison of alternative procedures to estimate VaR and ES," DES - Working Papers. Statistics and Econometrics. WS ws087326, Universidad Carlos III de Madrid. Departamento de Estadística.
    11. Gloria González-Rivera & Tae-Hwy Lee, 2007. "Nonlinear Time Series in Financial Forecasting," Working Papers 200803, University of California at Riverside, Department of Economics, revised Feb 2008.
    12. Chiarella, Carl & Gao, Shenhuai, 2004. "The value of the S&P 500--A macro view of the stock market adjustment process," Global Finance Journal, Elsevier, vol. 15(2), pages 171-196, August.
    13. Kumar, Dilip & Maheswaran, S., 2014. "A new approach to model and forecast volatility based on extreme value of asset prices," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 128-140.
    14. Kumar, Dilip & Maheswaran, S., 2014. "A reflection principle for a random walk with implications for volatility estimation using extreme values of asset prices," Economic Modelling, Elsevier, vol. 38(C), pages 33-44.
    15. Tae-Hwy Lee & Yong Bao & Burak Saltoglu, 2006. "Evaluating predictive performance of value-at-risk models in emerging markets: a reality check," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(2), pages 101-128.
    16. Lee, Tae-Hwy & Yang, Yang, 2006. "Bagging binary and quantile predictors for time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 465-497.
    17. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    18. Pedro Henrique Melo Albuquerque & Yaohao Peng & João Pedro Fontoura da Silva, 2022. "Making the whole greater than the sum of its parts: A literature review of ensemble methods for financial time series forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1701-1724, December.
    19. Thanh Tran & Haresh Gurnani & Ramarao Desiraju, 2018. "Optimal Design of Return Policies," Marketing Science, INFORMS, vol. 37(4), pages 649-667, August.
    20. Grosche, Stephanie, 2012. "Limitations of Granger Causality Analysis to assess the price effects from the financialization of agricultural commodity markets under bounded rationality," Discussion Papers 121868, University of Bonn, Institute for Food and Resource Economics.
    21. Santosh Mishra & Gloria Gonzalez-Rivera & Tae-Hwy Lee, 2004. "Jumps in Rank and Expected Returns. Introducing Varying Cross-sectional Risk," Econometric Society 2004 North American Winter Meetings 356, Econometric Society.

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