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Forecasting Realized Volatility Using a Long Memory Stochastic Volatility Model: Estimation, Prediction and Seasonal Adjustment

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Author Info

  • Rohit Deo

    (New York University)

  • Clifford Hurvich

    (New York University)

  • Yi Lu

    (New York University)

Abstract

We study the modeling of large data sets of high frequency returns using a long memory stochastic volatility (LMSV) model. Issues pertaining to estimation and forecasting of large datasets using the LMSV model are studied in detail. Furthermore, a new method of de-seasonalizing the volatility in high frequency data is proposed, that allows for slowly varying seasonality. Using both simulated as well as real data, we compare the forecasting performance of the LMSV model for forecasting realized volatility to that of a linear long memory model fit to the log realized volatility. The performance of the new seasonal adjustment is also compared to a recently proposed procedure using real data.

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File URL: http://128.118.178.162/eps/em/papers/0501/0501002.pdf
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Bibliographic Info

Paper provided by EconWPA in its series Econometrics with number 0501002.

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Length: 46 pages
Date of creation: 07 Jan 2005
Date of revision:
Handle: RePEc:wpa:wuwpem:0501002

Note: Type of Document - pdf; pages: 46
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Web page: http://128.118.178.162

Related research

Keywords: Realized Volatility; Long Memory Stochastic Volatility Model; High Frequency Data; Seasonal Adjustment;

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References

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  1. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
  2. Andersen, Torben G. & Bollerslev, Tim, 1997. "Intraday periodicity and volatility persistence in financial markets," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 115-158, June.
  3. Chen, Willa W. & Deo, Rohit S., 2006. "Estimation of mis-specified long memory models," Journal of Econometrics, Elsevier, vol. 134(1), pages 257-281, September.
  4. 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.
  5. Martin Martens & Yuan-Chen Chang & Stephen J. Taylor, 2002. "A Comparison of Seasonal Adjustment Methods When Forecasting Intraday Volatility," Journal of Financial Research, Southern Finance Association & Southwestern Finance Association, vol. 25(2), pages 283-299.
  6. Chen, Willa W. & Hurvich, Clifford M. & Lu, Yi, 2006. "On the Correlation Matrix of the Discrete Fourier Transform and the Fast Solution of Large Toeplitz Systems for Long-Memory Time Series," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 812-822, June.
  7. Breidt, F. Jay & Crato, Nuno & de Lima, Pedro, 1998. "The detection and estimation of long memory in stochastic volatility," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 325-348.
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