Why Do Absolute Returns Predict Volatility So Well

By Lars Forsberg, Eric Ghysels. 2006

381 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/6528e7f17b8d8855a41f6d550b95ee4cffdb38c4

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Citations in the corpus

A Simple Approximate Long-Memory Model of Realized Volatility (2008, background): “11In this sense, the HAR can be related to the MIDAS regression of Ghysels, Santa-Clara, and Valkanov (2006), Ghysels, Sinko, and Valkanov (2006), and Forsberg and Ghysels (2007), although the standard MIDAS with the estimated Beta function lag polynomial cannot reproduce the HAR step function weigh”
Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility (2007, result): “…defined by the sum of absolute high-frequency returns raised to powers less than two might afford additional gains over and above the improvements provided by the bipower variation and significant jump measures used here; the recent empirical results in Forsberg and Ghysels (2004) are suggestive.”
Quantile forecasts of daily exchange rate returns from forecasts of realized volatility (2008, neutral): “ Forsberg and Ghysels (2004) argue for RAV as a predictor of the volatility of stock returns, on”
A Component Model for Dynamic Correlations (2011, methodology): “The idea was carried a step further in Ghysels, Santa-Clara, and Valkanov (2006) and Forsberg and Ghysels (2004), both papers focusing on predicting volatility at various horizons with high frequency financial data using MIDAS.”
Threshold Bipower Variation and the Impact of Jumps on Volatility Forecasting (2010, methodology): “In this study we start from an apparent puzzle contained in the study of Andersen et al. (2007) (henceforth ABD) and Forsberg and Ghysels (2007); Giot and Laurent (2007); Busch et al. (2006).”
Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling (2012, positive): “Forsberg and Ghysels (2007) proposed the use of realized absolute variation (RAV) which shows a more persistent dynamics than realized volatility being more robust to microstructure noise and jumps.”
Testing for Granger Causality in Large Mixed-Frequency VARs (2016, neutral): “(2007), these HAR-type restrictions are a special case of MIDAS with step functions introduced in Forsberg and Ghysels (2007). Considering partial sums of regressors x as Xt(K,m) = ∑K i=0 x (m) t−i/m, a MIDAS regression with M steps reads as”
Dynamic spillovers between commodity and currency markets (2015, background): “(6)Moreover, Forsberg and Ghysels (2007) find that absolute returns predict volatility quite well and show that regressors involving volatility measures based on absolute returns are better at predicting future volatility for the following reasons: (i) desirable population predictability features, (”
HAR Modeling for Realized Volatility Forecasting (2012, neutral): “By substitution, this gives: where ε t is iid noise depending on ε In this sense, the HAR can be related to the MIDAS regression of [47], [48], and [45], although the standard MIDAS with the estimated Beta function lag polynomial cannot reproduce the HAR step function weights.”
Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models (2019, methodology): “The MIDAS model was initially applied to forecast volatility and got good results (Forsberg and Ghysels, 2007; Corsi, 2009; Chen et al., 2011).”
State Space Models and MIDAS Regressions (2013, background): “…on MIDAS focused on volatility predictions, see e.g. Alper, Fendoglu, and Saltoglu (2008), Chen and Ghysels (2009), Engle, Ghysels, and Sohn (2008), Forsberg and Ghysels (2006), Ghysels, Santa-Clara, and Valkanov (2005), Ghysels, Santa-Clara, and Valkanov (2006), León, Nave, and Rubio (2007),…”