Long memory in stochastic volatility

By Andrew S. Harvey. 2007

210 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/039cf23da078938797c9674c44b93d1053a2d91b

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Citations in the corpus

Modeling and forecasting stock return volatility using a random level shift model (2010, methodology): “EGARCH models, while Breidt, Crato and De Lima (1998) and Harvey (1998) proposed long”
Pseudo-maximum likelihood estimation of ARCH(∞) models (2005, background): “However, it has a number of disadvantages, as discussed by Giraitis and Robinson [14]: it is not only asymptotically inefficient under Gaussian εt, but never asymptotically efficient; it requires finiteness of fourth moments of yt for consistency and of eighth moments for asymptotic normality, which”
Consistent estimation of the memory parameter for nonlinear time series (2006, unknown): “A special case f(nt) ¼ exp(nt), where (nt) is a linear sequence was discussed in Breidt et al. (1998), Harvey (1998) and Surgailis and Viano (2002).”
Estimation and pricing under long-memory stochastic volatility (2012, background): “In this direction, in discrete time, Breidt et al. (1998, [4]) and Harvey (1998, [ 16 ]) proposed independently the Long-Memory SV (LMSV) model, where the log-volatility is modeled as an ARFIMA(p; d; q) process.”
Leverage and Volatility Feedback Effects in High-Frequency Data (2006, methodology): “21 Related discrete-time long-memory stochastic volatility models have previously been developed and estimated by Breidt, Crato, and de Lima (1998) and Harvey (1998). 22 Interesting ongoing work along these lines includes the ADS-ACD model in Ryan (2006), the Lévydriven stochastic volatility models ”
Local Polynomial Whittle Estimation of Perturbed Fractional Processes (2008, methodology): “This generalizes the usual LMSV model introduced by Breidt, Crato & de Lima (1998) and Harvey (1998), rt = p eytut; (3) by arguing that allowing for di¤erent short-lived news impacts, while imposing a common long memory component, may provide a better characterization of the joint volume-volatility…”
A comprehensive review of Value at Risk methodologies (2014, methodology): “…alternative and more complex models have been developed for the stochastic volatility models to allow for the pattern of both the large memory (see the model of Harvey (1998) and Breidt et al. (1998)) and the leverage effect (see the models of Harvey and Shephard (1996) and So et al. (2002)).”
Local Whittle Analysis of Stationary Fractional Cointegration and the Implied–Realized Volatility Relation (2007, background): “…(1991), Ding, Granger, and Engle (1993), Baillie et al. (1996), Comte and Renault (1996), Andersen and Bollerslev (1997a, b), Breidt et al. (1998), Harvey (1998), Andersen, Bollerslev, Diebold, and Ebens (2001), Andersen, Bollerslev, Diebold, and Labys (2001, 2003), and Deo and Hurvich (2003),…”
Recent Advances in ARCH Modelling (2007, methodology): “To estimate the change-point τ∗ Kokoszka and Leipus (2000) have used the estimator τ̂ = k̂/N , which is based on the CUSUM statistic (60), setting”
Stochastic Volatility: Origins and Overview (2008, background): “de Lima (1998) and Harvey (1998) study discrete-time models where log volatility is modeled as a”