ARMA representation of integrated and realized variances

By Nour Meddahi. 2003

74 citations recorded by the source corpus.

Subfields: Finance, Genetics, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/197070b6ab95e5ee327d4577f8603132ced9d0f6

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Citations in the corpus

Forecasting Daily Variability of the S&P 100 Stock Index Using Historical, Realised and Implied Volatility Measurements (2005, unknown): “It follows that the autoregressive parameter φj equals e −λjh while Meddahi (2003) shows that”
Forecasting Volatility Using High-Frequency Data (2011, background): “Meddahi (2003) derive the ARMA representation of both integrated and realized variances when the spot variance depends linearly on two autoregressive factors.”
Variance Dynamics: Joint Evidence from Options and High-Frequency Returns (2011, unknown): “in the particular quadratic variation estimator (Meddahi, 2003).”
Realized GARCH: a joint model for returns and realized measures of volatility (2012, unknown): “( G a u ss i a n i nn o v a t i o n s a r e n o t e ss e n t i a l f o r a n y o f t h e m o d e l s ) .”
Impact of jumps on returns and realised variances: econometric analysis of time-deformed Lévy processes (2006, methodology): “…literature on volatility forecasting by Andersen and Bollerslev (1998), Andersen, Bollerslev, and Meddahi (2002a) and Andersen, Bollerslev, and Meddahi (2002b) which has shown that the volatility of financial markets is much more predictable than is widely believed in the academic literature.”
Semi-Parametric Comparison of Stochastic Volatility Models using Realized Measures (2006, methodology): “The proposed tests rely on some recent results of Barndorff-Nielsen and Shephard (2001, 2002), Andersen et al. (2004), and Meddahi (2003), establishing the moments and the autocorrelation structure of integrated volatility.”
Data-based ranking of realised volatility estimators (2011, methodology): “Existing results on the ARMA processes for integrated variance (IV) implied by various continuous- time stochastic volatility models, see Barndor¤-Nielsen and Shephard (2002) and Meddahi (2003), are derived under a no leverage assumption, whereas our simulated process exhibits a leverage e¤ect.”
Hawkes Process: Fast Calibration, Application to Trade Clustering, and Diffusive Limit (2013, background): “Lastly, let us also mention that our closed-form solutions make it possible to perform forecast experiments as they are usually done for the realized volatility (see Meddahi (2003)).”