An investigation of long memory in various measures of stock market volatility, using wavelets and aggregate series

Using methods based on wavelets and aggregate series, long memory in the absolute daily returns, squared daily returns, and log squared daily returns of the S&P 500 Index are investigated. First, we estimate the long memory parameter in each series using a method based on the discrete wavelet tr...

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Bibliographic Details
Published in:Journal of economics and finance 2008-04, Vol.32 (2), p.136-147
Main Authors: DiSario, Robert, Saraoglu, Hakan, McCarthy, Joseph, Li, H. C.
Format: Article
Language:English
Subjects:
Economics
Economics and Finance
Finance
G10
G12
Investigations
Long memory
Macroeconomics/Monetary Economics//Financial Economics
Returns
Securities markets
Studies
Volatility
Wavelet transforms
Wavelets
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Summary:Using methods based on wavelets and aggregate series, long memory in the absolute daily returns, squared daily returns, and log squared daily returns of the S&P 500 Index are investigated. First, we estimate the long memory parameter in each series using a method based on the discrete wavelet transform. For each series, the variance method and the absolute value method based on aggregate series are then employed to investigate long memory. Our findings suggest that these methods provide evidence of long memory in the volatility of the S&P 500 Index.
ISSN:1055-0925
1938-9744
DOI:10.1007/s12197-007-9010-6