Real and Spurious Long-Memory Properties of Stock-Market Data
The authors test for the presence of long memory in daily stock returns and their squares using a robust semiparametric procedure of I. Lobato and P. M. Robinson (1997). Spurious results can be produced by nonstationarity and aggregation. The authors address these problems by analyzing subperiods of returns and using individual stocks. The test results show no evidence of long memory in the returns. By contrast, there is strong evidence in the squared returns.
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Volume (Year): 16 (1998)
Issue (Month): 3 (July)
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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Brock, William A. & Kleidon, Allan W., 1992. "Periodic market closure and trading volume : A model of intraday bids and asks," Journal of Economic Dynamics and Control, Elsevier, vol. 16(3-4), pages 451-489.
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- Ananth Madhavan & Matthew Richardson & Mark Roomans, 1996. "Why Do Security Prices Change? A Transaction-Level Analysis of NYSE Stocks," New York University, Leonard N. Stern School Finance Department Working Paper Seires 96-34, New York University, Leonard N. Stern School of Business-.
- repec:att:wimass:9523 is not listed on IDEAS
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- Grossman, Sanford J & Zhou, Zhongquan, 1996. " Equilibrium Analysis of Portfolio Insurance," Journal of Finance, American Finance Association, vol. 51(4), pages 1379-1403, September.
- Hidalgo, Javier & Robinson, Peter M., 1996. "Testing for structural change in a long-memory environment," Journal of Econometrics, Elsevier, vol. 70(1), pages 159-174, January.
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- 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.
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"Testing the covariance stationarity of heavy-tailed time series: An overview of the theory with applications to several financial datasets,"
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