Correcting the errors: Volatility forecast evaluation using high-frequency data and realized volatilities

By Torben G. Andersen, Tim Bollerslev, Nour Meddahi. 2005

323 citations recorded by the source corpus.

We develop general model-free adjustment procedures for the calculation of unbiased volatility loss functions based on practically feasible realized volatility benchmarks. The procedures, which exploit recent nonparametric asymptotic distributional results, are both easy-to-implement and highly accurate in empirically realistic situations. We also illustrate that properly accounting for the measurement errors in the volatility forecast evaluations reported in the existing literature can result in markedly higher estimates for the true degree of return volatility predictability.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/c97bb53524ec6e39ba3cb6e73c02717e9cc79367

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Citations in the corpus

Optimal combinations of realised volatility estimators (2009, methodology): “, 1997), which has been shown to work well in volatility forecasting problems, see Andersen et al. (2007), for example.”
BOOTSTRAPPING REALIZED VOLATILITY (2009, background): “In contrast, V̂ ∗ given in (4) is a consistent estimator of V ∗.”
Properties of Realized Variance Under Alternative Sampling Schemes (2006, unknown): “Andersen, Bollerslev, Diebold, and Labys, 2003; Andersen, Bollerslev, and Meddahi, 2005; Bandi and Russell,”
Forecasting oil price realized volatility using information channels from other asset classes (2017, methodology): “For instance, authors such as Andersen et al. (2001), Thomakos and Wang (2003) or Wu (2011), opine that night-time periods and weekends could be ignored from the construction of the realized volatility.”
Forecasting global stock market implied volatility indices (2018, methodology): “…forecasting is important for investors, portfolio managers, asset valuation, hedging strategies, risk management purposes, as well as, policy makers (see, inter alia, Figlewski, 1997; Andersen et al., 2003,2005; Christodoulakis, 2007; Fuertes et al., 2009; Charles, 2010; Barunik et al., 2016).”
Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models (2020, unknown): “10See Andersen, Bollerslev, and Meddahi (2005) for a model-free adjustment procedure for the predictive R2.”
Exploiting the errors: A simple approach for improved volatility forecasting (2016, background): “Andersen et al. (2005) provide a simple adjustment for this unconditional bias in the R2 .”
Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility (2007, unknown): “18 Note that although the relative magnitudes of the R2’s for a given volatility series are directly comparable across the two models, as discussed in Andersen, Bollerslev, and Meddahi (2005), the measurement errors in the left-hand-side realized volatility invariably result in a systematic…”
Evaluating Volatility and Correlation Forecasts (2009, methodology): “A second alternative is to adjust the volatility proxy, either exactly or approximately, so as to make it unbiased for the quantity of interest,σt or logσt , and thus avoid any asymptotic size distortions [13, 18, 6].”