Long memory and Periodicity in Intraday Volatility
AbstractIntraday return volatilities are characterized by the contemporaneous presence of periodicity and long memory. This paper proposes two new parameterizations of the intraday volatility: the Fractionally Integrated Periodic EGARCH and the Seasonal Fractional Integrated Periodic EGARCH, which provide the required flexibility to account for both features. The periodic kurtosis and periodic autocorrelations of power transformations of the absolute returns are computed for both models. The empirical application shows that volatility of the hourly Emini S&P 500 futures returns are characterized by a periodic leverage effect coupled with a statistically significant long-range dependence. An out-of-sample forecasting comparison with alternative models shows that a constrained version of the FI-PEGARCH provides superior forecasts. A simulation experiment is carried out to investigate the effects that sample frequency has on the fractional differencing parameter estimate.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by University of Pavia, Department of Economics and Management in its series DEM Working Papers Series with number 015.
Length: 41 pages
Date of creation: Nov 2012
Date of revision:
Intraday volatility; Long memory; FI-PEGARCH; SFI-PEGARCH; Periodicmodels.;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
- G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-11-17 (All new papers)
- NEP-ECM-2012-11-17 (Econometrics)
- NEP-ETS-2012-11-17 (Econometric Time Series)
- NEP-FOR-2012-11-17 (Forecasting)
- NEP-MST-2012-11-17 (Market Microstructure)
- NEP-RMG-2012-11-17 (Risk Management)
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.:
- Beltratti, Andrea & Morana, Claudio, 1999. "Computing value at risk with high frequency data," Journal of Empirical Finance, Elsevier, vol. 6(5), pages 431-455, December.
- Neil Shephard & Kevin Sheppard, 2010.
"Realising the future: forecasting with high-frequency-based volatility (HEAVY) models,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 25(2), pages 197-231.
- Neil Shephard & Kevin Sheppard, 2009. "Realising the future: forecasting with high frequency based volatility (HEAVY) models," Economics Papers 2009-W03, Economics Group, Nuffield College, University of Oxford.
- Neil Shephard & Kevin Sheppard, 2009. "Realising the future: forecasting with high frequency based volatility (HEAVY) models," OFRC Working Papers Series 2009fe02, Oxford Financial Research Centre.
- Neil Shephard & Kevin Sheppard, 2009. "Realising the future: forecasting with high frequency based volatility (HEAVY) models," Economics Series Working Papers 438, University of Oxford, Department of Economics.
- Bollerslev, T. & Ghysels, E., 1994.
"Periodic Autoregressive Conditional Heteroskedasticity,"
Cahiers de recherche
9408, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
- Bollerslev, Tim & Ghysels, Eric, 1996. "Periodic Autoregressive Conditional Heteroscedasticity," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(2), pages 139-51, April.
- Bollerslev, T. & Ghysels, E., 1994. "Periodic Autoregressive Conditional Heteroskedasticity," Cahiers de recherche 9408, Universite de Montreal, Departement de sciences economiques.
- Arteche, Josu, 2004.
"Gaussian semiparametric estimation in long memory in stochastic volatility and signal plus noise models,"
Journal of Econometrics,
Elsevier, vol. 119(1), pages 131-154, March.
- Arteche González, Jesús María, 2002. "Gaussian Semiparametric Estimation in Long Memory in Stochastic Volatility and Signal Plus Noise Models," BILTOKI 2002-02, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
- Bollerslev, Tim & Ole Mikkelsen, Hans, 1996.
"Modeling and pricing long memory in stock market volatility,"
Journal of Econometrics,
Elsevier, vol. 73(1), pages 151-184, July.
- Tom Doan, . "RATS program to replicate Bollerslev-Mikkelson(1996) FIEGARCH models," Statistical Software Components RTZ00173, Boston College Department of Economics.
- Josu Arteche & Peter M. Robinson, 1998. "Semiparametric inference in seasonal and cyclical long memory processes," LSE Research Online Documents on Economics 2203, London School of Economics and Political Science, LSE Library.
- Andrew Patton, 2006.
"Volatility Forecast Comparison using Imperfect Volatility Proxies,"
Research Paper Series
175, Quantitative Finance Research Centre, University of Technology, Sydney.
- Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
- Siem Jan Koopman & Marius Ooms & M. Angeles Carnero, 2005.
"Periodic Seasonal Reg-ARFIMA-GARCH Models for Daily Electricity Spot Prices,"
Tinbergen Institute Discussion Papers
05-091/4, Tinbergen Institute.
- Koopman, Siem Jan & Ooms, Marius & Carnero, M. Angeles, 2007. "Periodic Seasonal Reg-ARFIMAGARCH Models for Daily Electricity Spot Prices," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 16-27, March.
- Ilias Tsiakas, 2004.
"Periodic Stochastic Volatility and Fat Tails,"
wp04-09, Warwick Business School, Finance Group.
- Bordignon, Silvano & Caporin, Massimiliano & Lisi, Francesco, 2007. "Generalised long-memory GARCH models for intra-daily volatility," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5900-5912, August.
- 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.
- Philip Hans Franses & Richard Paap, 2000. "Modelling day-of-the-week seasonality in the S&P 500 index," Applied Financial Economics, Taylor & Francis Journals, vol. 10(5), pages 483-488.
- Ruiz, Esther & Veiga, Helena, 2008.
"Modelling long-memory volatilities with leverage effect: A-LMSV versus FIEGARCH,"
Computational Statistics & Data Analysis,
Elsevier, vol. 52(6), pages 2846-2862, February.
- Esther Ruiz & Helena Veiga, 2006. "Modelling Long-Memory Volatilities With Leverage Effect: Almsv Versus Fiegarch," Statistics and Econometrics Working Papers ws066016, Universidad Carlos III, Departamento de Estadística y Econometría.
- Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
- Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996.
"Fractionally integrated generalized autoregressive conditional heteroskedasticity,"
Journal of Econometrics,
Elsevier, vol. 74(1), pages 3-30, September.
- Tom Doan, . "RATS programs to replicate Baillie, Bollerslev, Mikkelson FIGARCH results," Statistical Software Components RTZ00009, Boston College Department of Economics.
- Barbara Bedowska-Sojka, 2011. "The Impact of Macro News on Volatility of Stock Exchanges," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 11, pages 99-110.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Alice Albonico).
If references are entirely missing, you can add them using this form.