Forecasting Volatility in Financial Markets: A Review

By Ser-Huang Poon, Clive W. J. Granger. 2003

1,008 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/6eb519acf55074d7602bc7fed1b1790de2c4d3e7

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Citations in the corpus

Forecasting carbon futures volatility using GARCH models with energy volatilities (2013, background): “Poon and Granger (2003) point out that, in a forecasting exercise, comparing the forecasting performance of the different methods is most important.”
How Does Oil Market Uncertainty Interact with Other Markets? An Empirical Analysis of Implied Volatility Index (2013, neutral): “It is because that implied volatilities not only contain the historical volatility information but also include investors’ expectation on future market conditions [16].”
Quantile forecasts of daily exchange rate returns from forecasts of realized volatility (2008, neutral): “See Poon and Granger (2003) for a recent review.”
Practical Issues in the Analysis of Univariate GARCH Models (2009, background): “There are also several comprehensive surveys that focus on the forecasting performance of GARCH models including Poon and Granger (2003), Poon (2005), and Andersen et al. (2006).”
Does implied volatility provide any information beyond that captured in model-based volatility forecasts? (2007, methodology): “This is not to say that the VIX is not useful for forecasting purposes, as has been demonstrated in previous research (see Poon and Granger 2003).”
Estimating stock market volatility using asymmetric GARCH models (2008, methodology): “The forecasting ability of GARCH models has been comprehensively discussed by Poon and Granger (2003).”
Forecasting and operational research: a review (2008, methodology): “…of these new methods, most noticeably, from statistics, Box and Jenkins’ development of Autoregressive Integrated Moving Average (ARIMA) models (Box et al, 1994), and from engineering, state-space models (Harvey, 1984), and Harrison and Stevens’ (1971) Bayesian multi-state Kalman filtering…”
Data-based ranking of realised volatility estimators (2011, background): “Ranking volatility forecasts, as opposed to estimators, has received a lot of attention in the econometrics literature, see Poon and Granger (2003) and Hansen and Lunde (2005) for two recent and comprehensive studies, and this is the natural starting point for considering the ranking realised…”
Closed-form transformations from risk-neutral to real-world distributions (2007, methodology): “Other approaches include maximum entropy densities [Buchen and Kelly (1996)], non-parametric estimates [Ait-Sahalia and Lo (1998)], multi- 1 Recent evidence is provided by Poon and Granger (2003), Martens and Zein (2004), Pong et al (2004) and Jiang and Tian (2005).”
Volatility puzzles: a simple framework for gauging return-volatility regressions (2006, neutral): “…for assessing the forecasting bias is based on regressing the ex-post realized volatility over some time period, say [t, t + ∆], on a constant and the time t implied volatility for an option maturing at t + ∆ (for a recent survey of this extensive empirical literature see Poon and Granger, 2003).”