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Valuing Volatility Spillovers

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  • George Milunovich

    (Macquarie University)

  • Susan Thorp

    (UTS)

Abstract

We measure the reduction in realized portfolio risk that can be achieved by allowing for volatility spillover in forecasts of equity covariance. The conditional second moment matrix of equity returns for pairs of major European equity markets is estimated via two asymmetric dynamic conditional correlation models (A-DCC): the unrestricted model includes volatility spillover e¤ects and the restricted model does not. Data are daily returns on the London, Frankfurt and Paris equity market price indices synchronized at London 16:00 time. Covariance forecasts from the restricted and unrestricted models are combined with assumed expected returns to compute e¢ cient three-asset portfolios (two equity indices and the risk-free asset). The impact of expected return choice on out-of-sample portfolio e¢ ciency is minimized via the polar co-ordinates method of Engel and Colacito (2004), which allows expected equity returns to span all relatives. Out-of-sample realized portfolio returns and variances from e¢ cient portfolios are computed and tested. Allowing for volatility spillover e¤ects produces small, statistically signi.cant reductions in portfolio risk. Portfolio standard deviations for the unrestricted model are at most one per cent smaller than standard deviations for restricted models. Significant risk reductions persist across daily, weekly, and monthly rebalancing horizons. Tests for second degree stochastic dominance indicate that realized returns from portfolios based on the volatility spillover model would be preferred by risk averse agents.

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Bibliographic Info

Paper provided by EconWPA in its series International Finance with number 0506008.

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Date of creation: 22 Jun 2005
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Handle: RePEc:wpa:wuwpif:0506008

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Keywords: forecasting; adcc; volatility spillovers; valuing;

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References

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Cited by:
  1. Chialin Chang & Michael McAleer & Roengchai Tansuchat, 2010. "Analyzing and Forecasting Volatility Spillovers and Asymmetries in Major Crude Oil Spot, Forward and Futures Markets," CIRJE F-Series CIRJE-F-718, CIRJE, Faculty of Economics, University of Tokyo.
  2. Ekin Tokat & Hakkı Arda Tokat, 2010. "Shock and Volatility Transmission in the Futures and Spot Markets: Evidence from Turkish Markets," Emerging Markets Finance and Trade, M.E. Sharpe, Inc., vol. 46(4), pages 92-104, January.
  3. Chia-Lin Chang & Michael McAleer & Roengchai Tansuchat, 2009. "Forecasting Volatility and Spillovers in Crude Oil Spot, Forward and Futures Markets," CARF F-Series CARF-F-163, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
  4. Chan, Leo & Lien, Donald & Weng, Wenlong, 2008. "Financial interdependence between Hong Kong and the US: A band spectrum approach," International Review of Economics & Finance, Elsevier, vol. 17(4), pages 507-516, October.
  5. Chia-Lin Chang & Michael McAleer & Roengchai Tansuchat, 2009. "Volatility Spillovers Between Crude Oil Futures Returns and Oil Company Stocks Return," CARF F-Series CARF-F-157, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
  6. Andrikopoulos, Andreas & Angelidis, Timotheos & Skintzi, Vasiliki, 2012. "Illiquidity, return and risk in G7 stock markets: interdependencies and spillovers," MPRA Paper 40003, University Library of Munich, Germany.
  7. Chang, Chia-Lin & McAleer, Michael & Tansuchat, Roengchai, 2010. "Analyzing and forecasting volatility spillovers, asymmetries and hedging in major oil markets," Energy Economics, Elsevier, vol. 32(6), pages 1445-1455, November.
  8. Susan Thorp & George Milunovich, 2005. "Asymmetric Risk and International Portfolio Choice," Research Paper Series 160, Quantitative Finance Research Centre, University of Technology, Sydney.

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