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Correlation surprise

Author

Listed:
  • Will Kinlaw
  • David Turkington

    (State Street Associates)

Abstract

Soon after Harry Markowitz published his landmark 1952 article on portfolio selection, the correlation coefficient assumed vital significance as a measure of diversification and an input to portfolio construction. However, investors typically overlook the potential for correlation patterns to help predict subsequent return and risk. Kritzman and Li (2010) introduced what is perhaps the first measure to capture the degree of multivariate asset price ‘unusualness’ through time. Their financial turbulence score spikes when asset prices ‘behave in an uncharacteristic fashion, including extreme price moves, decoupling of correlated assets, and convergence of uncorrelated assets.’ We extend Kritzman and Li’s study by disentangling the volatility and correlation components of turbulence to derive a measure of correlation surprise. We show how correlation surprise is orthogonal to volatility and present empirical evidence that it contains incremental forward-looking information. On average, after controlling for volatility, we find that periods characterized by correlation surprise lead to higher risk and lower returns to risk premia than periods characterized by typical correlations. This result holds across many markets including US equities, European equities and foreign exchange. Our results corroborate the predictive capacity of turbulence and suggest that its decomposition may also prove fruitful in forecasting investment performance.

Suggested Citation

  • Will Kinlaw & David Turkington, 2013. "Correlation surprise," Journal of Asset Management, Palgrave Macmillan, vol. 14(6), pages 385-399, December.
    • Handle: RePEc:pal:assmgt:v:14:y:2013:i:6:d:10.1057_jam.2013.27
    DOI: 10.1057/jam.2013.27
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    References listed on IDEAS

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    1. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
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