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Information flow between volatilities across time scales

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  • Gencay, Ramazan
  • Selcuk, Faruk
  • Whitcher, Brandon

Abstract

Conventional time series analysis, focusing exclusively on a time series at a given scale, lacks the ability to explain the nature of the data generating process. A process equation that successfully explains daily price changes, for example, is unable to characterize the nature of hourly price changes. On the other hand, statistical properties of monthly price changes are often not fully covered by a model based on daily price changes. In this paper, we simultaneously model regimes of volatilities at multiple time scales through wavelet-domain hidden Markov models. We establish an important stylized property of volatility across different time scales. We call this property asymmetric vertical dependence. It is asymmetric in the sense that a low volatility state (regime) at a long time horizon is most likely followed by low volatility states at shorter time horizons. On the other hand, a high volatility state at long time horizons does not necessarily imply a high volatility state at shorter time horizons. Our analysis provides evidence that volatility is a mixture of high and low volatility regimes, resulting in a distribution that is non-Gaussian. This result has important implications regarding the scaling behavior of volatility, and consequently, the calculation of risk at different time scales.

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File URL: http://mpra.ub.uni-muenchen.de/10355/
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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 10355.

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Date of creation: Oct 2004
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Handle: RePEc:pra:mprapa:10355

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Related research

Keywords: Discrete wavelet transform; wavelet-domain hidden Markov trees; foreign exchange markets; stock markets; multiresolution analysis; scaling;

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References

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  1. Andersen T. G & Bollerslev T. & Diebold F. X & Labys P., 2001. "The Distribution of Realized Exchange Rate Volatility," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 42-55, March.
  2. Gençay, Ramazan & Selçuk, Faruk & Whitcher, Brandon, 2001. "Differentiating intraday seasonalities through wavelet multi-scaling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 289(3), pages 543-556.
  3. John M. Maheu & Thomas H. McCurdy, 2002. "Nonlinear Features of Realized FX Volatility," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 668-681, November.
  4. Gençay, Ramazan & Selçuk, Faruk & Whitcher, Brandon, 2001. "Scaling properties of foreign exchange volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 289(1), pages 249-266.
  5. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
  6. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
  7. Fung, William & Hsieh, David A., 2000. "Measuring the market impact of hedge funds," Journal of Empirical Finance, Elsevier, vol. 7(1), pages 1-36, May.
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Cited by:
  1. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 7(2), pages 174-196, Spring.

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