A Simple Long Memory Model of Realized Volatility

By Fulvio Corsi. 2004

281 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/729fddf893eb00084860a50af8da9a52159e1a6b

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Citations in the corpus

Realized Volatility: A Review (2008, methodology): “…modeled as an ARFIMA(p, d , q) process (see Andersen et al., 2000a,b, 2001a, 2003) for some examples).6 Recently, Corsi et al. (2001) and Corsi (2003) proposed the Heterogeneous AutoRegressive Realized Volatility (HAR-RV) model, based on the Heterogeneous ARCH (HARCH) model of Müller et…”
Quantile forecasts of daily exchange rate returns from forecasts of realized volatility (2008, unknown): “…s=0 s+1L s ! log(RV 1=2 t s 1;t s) + "t+h: (3) We consider two regression models that use alternative measures of intraday variation as explanatory variables: the Heterogenous Autoregressive model (HAR) proposed by Corsi (2004), and the MIxed Data Sampling (MIDAS) approach of Ghysels et al. (2004).”
Multi-Period Forecasts of Volatility: Direct, Iterated, and Mixed-Data Approaches (2009, background): “The literature on the topic hinges on the trade-off between bias and estimation variance that exists in multi-period forecasts (Findley (1983), Findley (1985), Lin and Granger (1994), Clements and Hendry (1996), Bhanzali (1999), and Chevillon and Hendry (2005)).”
Volatility in Equilibrium: Asymmetries and Dynamic Dependencies (2009, unknown): “…better, than specific parametric modeling approaches designed to forecast the integrated volatility,25 we will here rely on the HAR-RV model structure first proposed by Corsi (2009), and subsequently used by Andersen et al. (2007a) among many others, in approximating the objective expectation.”
Some Like it Smooth, and Some Like it Rough: Untangling Continuous and Jump Components in Measuring, Modeling, and Forecasting Asset Return Volatility (2003, unknown): “- 8 Instead of these relatively complicated estimation procedure, we will here rely on the simple HAR-RV class of volatility models recently proposed by Corsi (2003). This model is based on a straightforward extension of the so-called Heterogeneous ARCH, or HARCH, class of models first introduced by”
Realized volatility forecasting and market microstructure noise (2011, methodology): “These theoretical results directly corroborate the empirical findings reported in Andersen, Bollerslev, Diebold and Labys (2003), Areal and Taylor (2002), Corsi (2003), Deo, Hurvich and Lu (2006), Koopman, Jungbacker and Hol (2005), Martens (2002), Pong, Shackleton, Taylor and Xu (2004), and Thomako”
Optimal combinations of realised volatility estimators (2009, methodology): “Using the simple HAR model of Corsi (2004) to obtain one-step-ahead forecasts, we find that, unsurprisingly, better estimation accuracy generally leads to better forecast accuracy, although the rankings are not identical.”
MIDAS Regressions: Further Results and New Directions (2007, methodology): “These MIDAS regressions are inspired by the HAR model of Corsi ( 2003 ), which was also used in Andersen et al.”
Do High-Frequency Measures of Volatility Improve Forecasts of Return Distributions? (2011, unknown): “flrst model uses a heterogeneous autoregressive (HAR) speciflcation ( Corsi (2003) , Andersen,”
A Discrete-Time Model for Daily S & P500 Returns and Realized Variations: Jumps and Leverage Effects (2009, unknown): “…and Zein (2004), Pong et al. (2004), and Thomakos and Wang (2003) among others, have advocated the use of ARFIMA type models, along with approximate long-memory component type structures in Andersen et al. (2007) and Corsi (2004), for modeling the dynamic dependencies in realized volatilities.”
Variation, jumps, market frictions and high frequency data in financial econometrics (2005, unknown): “Curci and Corsi (2003) have argued that before computing realised QV we should prefilter the data using a discrete sine transformation to the returns in order to reduce the impact of market frictions. This is efficient when the data X is a Gaussian random walk Y plus independent Gaussian noise ε mod”
Modeling and predicting the CBOE market volatility index (2014, methodology): “the long memory that typically characterizes both options-implied and realized volatility measures (Corsi, 2004; Koopman et al., 2005; Bandi and Perron, 2006).”
Do Bonds Span Volatility Risk in the U.S. Treasury Market? A Specification test for Affine Term Structure Models (2010, unknown): “A convenient candidate model along these lines is the HAR-RV form studied in Andersen et al. (2006a), Corsi (2003), and Müller et al. (1997): vyτ (t + h, h)2 h = β0 + βD vyτ (t, 1) 2 + βW vyτ (t, 5)2 5 + βM vyτ (t, 21)2 21 + ε(t + h) , (36) where the left-hand-side variable is the daily (h = 1),…”
The role of implied volatility in forecasting future realized volatility and jumps in foreign exchange, stock, and bond markets (2007, unknown): “Instead, it may be more relevant to place higher weight on recent squared returns than on squared returns that are more distant in the past, and one way to do so in a parsimonious fashion is to apply the heterogeneous autoregressive (HAR) model proposed by Corsi (2004). When applying this only to re”