Empirical volatility analysis: feature detection and signal extraction with function dictionaries
We aim to investigate the potential usefulness of wavelets for representing and decomposing financial volatility processes. Our strategy relies on the empirical analysis of high-frequency intradaily stock index returns by using adaptive signal-processing techniques which exploit the approximation and computational power of wavelet transforms. We first deal with data pre-processing and pre-smoothing, before addressing the statistical model building stage. We thus introduce a flexible parametric model that yields an effective empirical volatility analysis tool, capable of handling and detecting latent periodicities, and consequently delivering more accurate signal estimates. We extract the structure of volatility through the information content of projected signals obtained by representing and approximating the observed returns with special function dictionaries that may significantly contribute to reduce the risk that standard volatility models might fail to achieve meaningful statistical inference.
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Volume (Year): 319 (2003)
Issue (Month): C ()
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- Drost, F.C. & Nijman, T.E., 1992.
"Temporal Aggregation of Garch Processes,"
9240, Tilburg - Center for Economic Research.
- Drost, F.C. & Nijman, T.E., 1990. "Temporal aggregation of GARCH processes," Discussion Paper 1990-66, Tilburg University, Center for Economic Research.
- Drost, F.C. & Nijman, T.E., 1992. "Temporal aggregation of GARCH processes," Discussion Paper 1992-40, Tilburg University, Center for Economic Research.
- Drost, F.C. & Nijman, T.E., 1990. "Temporal Aggregation Of Garch Processes," Papers 9066, Tilburg - Center for Economic Research.
- Drost, F.C. & Nijman, T.E., 1993. "Temporal aggregation of GARCH processes," Other publications TiSEM 0642fb61-c7f4-4281-b484-4, Tilburg University, School of Economics and Management.
- 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.
- Tim Bollerslev, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
EERI Research Paper Series
EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
- Enrico Capobianco, 2002. "Multiresolution approximation for volatility processes," Quantitative Finance, Taylor & Francis Journals, vol. 2(2), pages 91-110.
- Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
- Torben G. Andersen & Tim Bollerslev, 1997. "Answering the Critics: Yes, ARCH Models Do Provide Good Volatility Forecasts," NBER Working Papers 6023, National Bureau of Economic Research, Inc.
- 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.
- Ding, Zhuanxin & Granger, Clive W. J., 1996. "Modeling volatility persistence of speculative returns: A new approach," Journal of Econometrics, Elsevier, vol. 73(1), pages 185-215, July.
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