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Portfolio management implications of volatility shifts: Evidence from simulated data

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Author Info
Viviana Fernandez
Brian M Lucey ()

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Abstract

Based on weekly data of the Dow Jones Country Titans, the CBT-municipal bond, spot and futures prices of commodities for the period 1992-2005, we analyze the implications for portfolio management of accounting for conditional heteroskedasticity and structural breaks in long-term volatility. In doing so, we first proceed to utilize the ICSS algorithm to detect volatility shifts, and incorporate that information into PGARCH models fitted to the returns series. At the next stage, we simulate returns series and compute a wavelet-based value at risk, which takes into consideration the investor’s time horizon. We repeat the same procedure for artificial data generated from distribution functions fitted to the returns by a semi-parametric procedure, which accounts for fat tails. Our estimation results show that neglecting GARCH effects and volatility shifts may lead us to overestimate financial risk at different time horizons. In addition, we conclude that investors benefit from holding commodities as their low or even negative correlation with stock indices contribute to portfolio diversification.

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Paper provided by Centro de Economía Aplicada, Universidad de Chile in its series Documentos de Trabajo with number 219.

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Date of creation: 2006
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Handle: RePEc:edj:ceauch:219

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  1. Giot, Pierre & Laurent, Sebastien, 2004. "Modelling daily Value-at-Risk using realized volatility and ARCH type models," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 379-398, June. [Downloadable!] (restricted)
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  2. Lamoureux, Christopher G & Lastrapes, William D, 1990. "Persistence in Variance, Structural Change, and the GARCH Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 225-34, April.
  3. Shinn-Juh Lin & Maxwell Stevenson, 2001. "Wavelet Analysis of the Cost-of-Carry Model," Studies in Nonlinear Dynamics & Econometrics, Berkeley Electronic Press, vol. 5(1), pages 87-102. [Downloadable!] (restricted)
  4. Viviana Fernandez, 2005. "The International CAPM and a wavelet-based decomposition of Value at Risk," The Institute for International Integration Studies Discussion Paper Series iiisdp075, IIIS. [Downloadable!]
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  5. Enrico Capobianco, 2004. "Multiscale Analysis of Stock Index Return Volatility," Computational Economics, Springer, vol. 23(3), pages 219-237, 04. [Downloadable!]
  6. Fernandez, Viviana, 2006. "The impact of major global events on volatility shifts: Evidence from the Asian crisis and 9/11," Economic Systems, Elsevier, vol. 30(1), pages 79-97, March. [Downloadable!] (restricted)
  7. Ser-Huang Poon & Clive W. J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
  8. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June. [Downloadable!] (restricted)
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  9. Ane, Thierry, 2006. "An analysis of the flexibility of Asymmetric Power GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1293-1311, November. [Downloadable!] (restricted)
  10. Fernandez, Viviana, 2006. "The CAPM and value at risk at different time-scales," International Review of Financial Analysis, Elsevier, vol. 15(3), pages 203-219. [Downloadable!] (restricted)
  11. Ramsey, James B. & Zhang, Zhifeng, 1997. "The analysis of foreign exchange data using waveform dictionaries," Journal of Empirical Finance, Elsevier, vol. 4(4), pages 341-372, December. [Downloadable!] (restricted)
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