Consistent Ranking of Volatility Models

By P. R. Hansen, Asger Lunde. 2006

377 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/01932454e09f5c29b12e00453295907e228a4399

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Citations in the corpus

Variation, jumps, market frictions and high frequency data in financial econometrics (2005, positive): “Hansen and Lunde (2005c) have studied how one can use high-frequency information to estimate the QV throughout the day, taking into account closed periods.”
Multi-Period Forecasts of Volatility: Direct, Iterated, and Mixed-Data Approaches (2009, methodology): “We use the MSFE because, in addition to being the most widely used loss function, it also has some desirable properties in the context of out-of-sample variance forecasts (Hansen and Lunde (2006) and Patton (2007)).”
A Test for Superior Predictive Ability (2005, methodology): “This phenomenon was also discussed Hansen and Lunde (2005b), who compared a large number of volatility models using the methods of this article.”
Realized Variance and Market Microstructure Noise (2006, methodology): “Kernel-based estimators were explored in Zhou (1996), Hansen & Lunde (2003), and Barndorff-Nielsen, Hansen, Lunde & Shephard (2004), and the closely related subsample-based estimators were used in an unpublished paper by Müller (1993), and in Zhou (1996), Zhang et al.”
Data-based ranking of realised volatility estimators (2011, background): “Assumption T2: t = 0 + Pp i=1 i t i + t, with E [ tjFt 1] = 0; 1 6= 0; the matrix dened in equation (35) is invertible, and 1; :::; p 0 is such that t is covariance stationary.”
A Discrete-Time Model for Daily S & P500 Returns and Realized Variations: Jumps and Leverage Effects (2009, positive): “…the serial correlation induced by the market microstructure noise as in, e.g., Andersen et al. (2001a), Bollen and Inder (2002), Corsi et al. (2001), Hansen and Lunde (2006), and Zhou (1996), along with sub-sampling schemes designed to adjust for the bias and inconsistency in the simple realized…”
A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)? (2005, positive): “The models are evaluated out-of-sample using six different loss functions, superior predictive ability (SPA) of Hansen (2001) and the reality check for data snooping by White (2000) to benchmark the 330 volatility models to the GARCH(1,1) of Bollerslev (1986).”
Realized Volatility: A Review (2008, methodology): “Hansen and Lunde (2006b) showed that the previous tick method is a sensible way to sample prices in calendar time.”
Garch forecasting performance under different distribution assumptions (2006, background): “However, not only is it important to use a proxy with low noise to measure the absolute degree of predictability but also the relative forecasting performance between models can be affected by the noise in the proxy, as shown in Hansen and Lunde (2006).”
Volatility Forecast Comparison Using Imperfect Volatility Proxies (2006, positive): “Further , the MSE loss function also satisfies the sufficient condition of Hansen and Lunde (2006), and thus MSE is a ''robust'' loss function .”
Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models (2020, positive): “As shown in Hansen and Lunde (2006), the ranking of competing one-step-ahead volatility forecasts based on the R21 of the MZ regression is robust to using the proxy 𝜀 2 1,t+1 instead of the latent conditional variance 𝜎21,t+1 as the dependent variable.”
Optimal combinations of realised volatility estimators (2009, methodology): “Our second class of realised volatility estimators is the first-order autocorrelation-adjusted RV estimator (RVAC1) presented by French et al. (1987) and Zhou (1996), and studied by Bandi and Russell (2008) and Hansen and Lunde (2006a), amongst others.”