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Identifying common spectral and asymmetric features in stock returns

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Author Info
Caiado, Jorge
Crato, Nuno

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Abstract

This paper proposes spectral and asymmetric-volatility based methods for cluster analysis of stock returns. Using the information about both the periodogram of the squared returns and the estimated parameters in the TARCH equation, we compute a distance matrix for the stock returns. Clusters are formed by looking to the hierarchical structure tree (or dendrogram) and the computed principal coordinates. We employ these techniques to investigate the similarities and dissimilarities between the "blue-chip" stocks used to compute the Dow Jones Industrial Average (DJIA) index. For reference, we investigate also the similarities among stock returns by mean and squared correlation methods.

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Publisher Info
Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 6607.

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Date of creation: Dec 2007
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Handle: RePEc:pra:mprapa:6607

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Related research
Keywords: Asymmetric effects Cluster analysis DJIA stock returns Periodogram Threshold ARCH model Volatility

Find related papers by JEL classification:
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models
G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

This paper has been announced in the following NEP Reports:

References listed on IDEAS
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  1. Schwert, G William, 1989. " Why Does Stock Market Volatility Change over Time?," Journal of Finance, American Finance Association, vol. 44(5), pages 1115-53, December. [Downloadable!] (restricted)
    Other versions:
  2. Engle, Robert F & Ng, Victor K, 1993. " Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-78, December. [Downloadable!] (restricted)
    Other versions:
  3. Kroner, Kenneth F & Ng, Victor K, 1998. "Modeling Asymmetric Comovements of Asset Returns," Review of Financial Studies, Oxford University Press for Society for Financial Studies, vol. 11(4), pages 817-44.
  4. Lorenzo Cappiello & Robert F. Engle & Kevin Sheppard, 2006. "Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns," Journal of Financial Econometrics, Oxford University Press, vol. 4(4), pages 537-572. [Downloadable!] (restricted)
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  5. Ang, Andrew & Chen, Joseph, 2002. "Asymmetric correlations of equity portfolios," Journal of Financial Economics, Elsevier, vol. 63(3), pages 443-494, March. [Downloadable!] (restricted)
  6. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September. [Downloadable!] (restricted)
  7. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March. [Downloadable!] (restricted)
  8. Lawrence R. Glosten & Ravi Jagannathan & David E. Runkle, 1993. "On the relation between the expected value and the volatility of the nominal excess return on stocks," Staff Report 157, Federal Reserve Bank of Minneapolis. [Downloadable!]
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  9. Caiado, Jorge & Crato, Nuno & Pena, Daniel, 2006. "A periodogram-based metric for time series classification," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2668-2684, June. [Downloadable!] (restricted)
  10. Bekaert, Geert & Wu, Guojun, 2000. "Asymmetric Volatility and Risk in Equity Markets," Review of Financial Studies, Oxford University Press for Society for Financial Studies, vol. 13(1), pages 1-42.
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  11. repec:cup:etheor:v:11:y:1995:i:1:p:122-50 is not listed on IDEAS
  12. Yu, Chih-Hsien & Wu, Chunchi, 2001. "Economic sources of asymmetric cross-correlation among stock returns," International Review of Economics & Finance, Elsevier, vol. 10(1), pages 19-40. [Downloadable!] (restricted)
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