Whittle estimation of EGARCH and other exponential volatility models
The strong consistency and asymptotic normality of the Whittle estimate of the parameters in a class of exponential volatility processes are established. Our main focus here are the EGARCH model of [Nelson, D. 1991. Conditional heteroscedasticity in asset pricing: A new approach. Econometrica 59, 347-370] and other one-shock models such as the GJR model of [Glosten, L., Jaganathan, R., Runkle, D., 1993. On the relation between the expected value and the volatility of the nominal excess returns on stocks. Journal of Finance, 48, 1779-1801], but two-shock models, such as the SV model of [Taylor, S. 1986. Modelling Financial Time Series. Wiley, Chichester, UK], are also comprised by our assumptions. The variable of interest might not have finite fractional moment of any order and so, in particular, finite variance is not imposed. We allow for a wide range of degrees of persistence of shocks to conditional variance, allowing for both short and long memory.
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- Peter M. Robinson & Carlos Velasco, 2000. "Whittle pseudo-maximum likelihood estimation for nonstationary time series," LSE Research Online Documents on Economics 2273, London School of Economics and Political Science, LSE Library.
- 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.
- Paolo Zaffaroni, 2003. "Gaussian inference on certain long-range dependent volatility models," Temi di discussione (Economic working papers) 472, Bank of Italy, Economic Research and International Relations Area.
- 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, "undated". "RATS program to replicate Bollerslev-Mikkelson(1996) FIEGARCH models," Statistical Software Components RTZ00173, Boston College Department of Economics.
- Rohit Deo & Clifford Hurvich & Yi Lu, 2005.
"Forecasting Realized Volatility Using a Long Memory Stochastic Volatility Model: Estimation, Prediction and Seasonal Adjustment,"
- Deo, Rohit & Hurvich, Clifford & Lu, Yi, 2006. "Forecasting realized volatility using a long-memory stochastic volatility model: estimation, prediction and seasonal adjustment," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 29-58.
- Zaffaroni, Paolo & d'Italia, Banca, 2003. "Gaussian inference on certain long-range dependent volatility models," Journal of Econometrics, Elsevier, vol. 115(2), pages 199-258, August.
- Yin-Wong Cheung & Francis X. Diebold, 1993.
"On maximum-likelihood estimation of the differencing parameter of fractionally integrated noise with unknown mean,"
93-5, Federal Reserve Bank of Philadelphia.
- Cheung, Yin-Wong & Diebold, Francis X., 1994. "On maximum likelihood estimation of the differencing parameter of fractionally-integrated noise with unknown mean," Journal of Econometrics, Elsevier, vol. 62(2), pages 301-316, June.
- Yin-Wong Cheung & Francis X. Diebold, 1990. "On maximum-likelihood estimation of the differencing parameter of fractionally integrated noise with unknown mean," Discussion Paper / Institute for Empirical Macroeconomics 34, Federal Reserve Bank of Minneapolis.
- Brandt, Michael W. & Jones, Christopher S., 2006. "Volatility Forecasting With Range-Based EGARCH Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 470-486, October.
- Giraitis, Liudas & Robinson, Peter M., 2001. "Whittle Estimation Of Arch Models," Econometric Theory, Cambridge University Press, vol. 17(03), pages 608-631, June.
- Nour Meddahi, 2000.
"Temporal Aggregation of Volatility Models,"
Econometric Society World Congress 2000 Contributed Papers
1903, Econometric Society.
- Granger, C. W. J. & Andersen, Allan, 1978. "On the invertibility of time series models," Stochastic Processes and their Applications, Elsevier, vol. 8(1), pages 87-92, November.
- Andrew Harvey & Esther Ruiz & Neil Shephard, 1994. "Multivariate Stochastic Variance Models," Review of Economic Studies, Oxford University Press, vol. 61(2), pages 247-264.
- Hidalgo, J. & Yajima, Y., 2002. "Prediction And Signal Extraction Of Strongly Dependent Processes In The Frequency Domain," Econometric Theory, Cambridge University Press, vol. 18(03), pages 584-624, June.
- 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.
- Robinson, P. M., 1978. "Alternative models for stationary stochastic processes," Stochastic Processes and their Applications, Elsevier, vol. 8(2), pages 141-152, December.
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