A Simple Approximate Long-Memory Model of Realized Volatility

By Fulvio Corsi. 2008

2,439 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/df90bf933faf0fc0da5e7a672c004bbdd9454e0e

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Citations in the corpus

Do Jumps Matter for Volatility Forecasting? Evidence from Energy Markets (2016, methodology): “We employ six distinct loss functions to analyze the accuracy (1)See Corsi (2009) for an excellent treatment of the HAR model.”
The Analysis of Stochastic Volatility in the Presence of Daily Realised Measures (2011, background): “Andersen, Bollerslev, Diebold, and Labys (2003), Andersen, Bollerslev, and Diebold (2007a), Corsi (2009) and Hillebrand and Medeiros (2010), among others, suggest other reduced form approaches for modelling and forecasting realised volatility.”
Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility (2015, methodology): “Separate estimation of the models on the individual firms’ realized variance is feasible, but does not provide a direct method to assess the significance of the average effect, and so we estimate a pooled unbalanced panel HAR with a fixed effect to facilitate inference on the average value of…”
Cross-Stock Comparisons of the Relative Contribution of Jumps to Total Price Variance (2012, methodology): “They extend the HAR-RV model of Corsi (2009)—which parameterizes future realized variance as a linear combination of one-day, one-week, and one-month lagged variances—to include J t as a regressor.”
Measuring and Forecasting Volatility in Chinese Stock Market Using HAR-CJ-M Model (2013, methodology): “[10], Corsi [9] pointed out that the different participants are likely to settle for different prices and decide to execute their transactions in different market situations; hence they create volatility.”
Harnessing jump component for crude oil volatility forecasting in the presence of extreme shocks (2019, methodology): “The HAR model is proposed by Corsi (2009) and has gained large popularity in RV forecasting literature due to its simplicity.”
Outlyingness weighted covariation (2011, positive): “Like in Corsi et al. (2009), we find that jumps induce a relatively large finite sample 21”
Forecasting realized volatility of agricultural commodities (2020, methodology): “Finally, we use a leverage variant of the HAR-CJ, which is proposed by Corsi & Renò (2012). This model separates the aggregated negative daily log-returns over the corresponding periods to account for leverage effects.”
Realized volatility forecasting and market microstructure noise (2011, methodology): “This estimator has also previously been analyzed by Zumbach, Corsi and Trapletti (2002).”
New HEAVY Models for Fat-Tailed Realized Covariances and Returns (2017, methodology): “Note that there is a slight difference between (5) and the models of Jin and Maheu (2013) and Corsi (2009) as we put the “HARdynamics” on Vt rather than on RCt .”
Tails, Fears and Risk Premia (2010, methodology): “the HAR-RV modeling approach of Corsi (2009) and Andersen et al. (2007) and restrict all but the daily, weekly, and monthly lags, or lag 1, 5, and 22, respectively, to be equal to zero.”