Forecasting Volatility in Financial Markets: A Review

By F. T. Magiera. 2004

1,230 citations recorded by the source corpus.

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Bad Environments, Good Environments: A Non-Gaussian Asymmetric Volatility Model (2015, neutral): “However, time-varying volatility models with Gaussian innovations generally do not generate suffi cient unconditional non-Gaussianity to match some financial asset return data (see, e.g., Poon and Granger, 2003).”
Forecasting Daily Variability of the S&P 100 Stock Index Using Historical, Realised and Implied Volatility Measurements (2005, neutral): “However, the likelihood function of the SV model can be constructed using simulation methods such as the ones developed by Shephard and Pitt (1997) and Durbin and Koopman (1997).”
On the origin of power-law tails in price fluctuations (2003, background): “Realistic error bars for the average market impact are difficult to assess as volatility is a long-memory process [7,8].”
Nonparametric Risk Management With Generalized Hyperbolic Distributions (2005, background): “The volatility in the future is unavailable, but empirical studies have found that volatility time series appear to have a unit root (Poon and Granger 2003).”
Realized Volatility: A Review (2008, background): “Some of the surveys are rather broad, and review volatility in general, such Poon and Granger (2003), Andersen et al. (2006a,b).”
Evaluating Predictive Performance of Value-at-Risk Models in Emerging Markets: A Reality Check (2006, background): “See Poon and Granger (2003) for an excellent survey and references therein.”