The memory of stochastic volatility models

By Peter M. Robinson. 2001

107 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/18bb0b48926c7e58a79c4572a3f90f935d1093b6

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In Economics and Econometrics

1. Tim Bollerslev
2. Torben G. Andersen
3. Francis X. Diebold
4. Esther Ruiz
5. Kevin Sheppard
6. N. Shephard
7. Andrew J. Patton
8. George Tauchen

In Finance

1. Tim Bollerslev
2. Torben G. Andersen
3. Liudas Giraitis
4. Donatas Surgailis
5. Gilles Teyssière
6. Francis X. Diebold
7. Remigijus Leipus
8. V. Todorov

In Global and Planetary Change

1. Gilles Teyssière
2. Alan Kirman
3. Banca d’Italia
4. Violetta Dalla
5. J. Hidalgo
6. Esther Ruiz
7. Carmen Broto
8. Jonathan H. Wright

Citations in the corpus

• Microeconomic Models for Long Memory in the Volatility of Financial Time Series (2001, background): “…Division of Statistics in the Department of Mathematics of the University of Liverpool, February 1999; the 1999 Econometric Society European Meeting in Santiago de Compostela, August 1999; the Society for Nonlinear Dynamics and Econometrics Annual Symposium in Atlanta, March 2000; the CeNDEF…”
• Estimation methods for stochastic volatility models: a survey (2004, background): “Shephard and Pitt (1997) have suggested an efficient multi-move algorithm that outperforms the single block approach in computational terms (Sandmann & Koopman, 1998; Wong, 2002).”
• Semiparametric estimation of long-memory volatility dependencies: The role of high-frequency data (2000, background): “Although the analysis in Robinson (1999) of instantaneous nonlinear transforms of long-memory measures does not formally apply to the squared temporally aggregated data, we conjecture that, for a wide class of distributions, the autocovariances of log-squared or absolute temporally aggregated…”
• Gaussian inference on certain long-range dependent volatility models (2003, methodology): “Empirical application..............................................................................28 7.”
• ARCH-type bilinear models with double long memory (2002, background): “The long memory, in the sense of the asymptotic behavior of the covariance function, was rigorously established for a class of stochastic volatility models which includes Gaussian subordinated models with arbitrary form of nonlinearity (Robinson, 1999) and a general class of exponential volatily mod”
• Recent Advances in ARCH Modelling (2007, background): “On the other hand, it has several important advantages, as explained in Robinson (2001).”
• Consistent estimation of the memory parameter for nonlinear time series (2006, background): “Robinson (2001) showed that a wide class of stochastic volatility models with Gaussian (nt) allow long memory behaviour in volatility.”
• Realized Beta: Persistence and Predictability (2004, background): “13 A partial list of references not written by the present authors includes Breidt and de Lima (1998), Comte and Renault (1998), Harvey (1998), and Robinson (2001), as well as many of the earlier papers cited in Baillie (1996).”