A Closer Look at the Relation between GARCH and Stochastic Autoregressive Volatility

By Jeff Fleming, C. Kirby. 2003

76 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ea1d32d28dc77cdcef695a83e5c9caa7c5316057

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Citations in the corpus

• An Economic Evaluation of Empirical Exchange Rate Models (2009, unknown): “For instance, Fleming and Kirby (2003) nd that SV models only marginally outperform GARCH models.”
• Frontiers in VaR forecasting and backtesting (2016, methodology): “Some applications of SV models to the estimation of VaR can be found in Lehar et al. (2002), Fleming and Kirby (2003), Gónzalez-Rivera et al. (2004) and Chen, Gerlach, Lin and Lee (2012).”
• Bayesian semiparametric stochastic volatility modeling (2010, methodology): “Drawing the latent volatilities is difficult and has attracted the attention of the profession (see Jacquier et al., 1994; Pitt and Shephard, 1997; Kim et al., 1998; Chib et al., 2002; Fleming and Kirby, 2003).”
• Periodic Stochastic Volatility and Fat Tails (2005, methodology): “Like GARCH-type models, they are designed to capture the persistent and hence predictable component of daily volatility (for a comparison of GARCH and SV models see Fleming and Kirby (2003)).”
• Maximum Likelihood Estimation of Latent Affine Processes (2006, unknown): “1See Nelson (1992), Nelson and Foster (1994), and Fleming and Kirby (2003) for filtration interpretations of GARCH models.”
• A comprehensive review of Value at Risk methodologies (2014, methodology): “Some applications of the SV model to measure VaR can be found in Fleming and Kirby (2003), Lehar et al. (2002), Chen et al. (2011) and González-Rivera et al. (2004).”
• Dynamic jump intensities and risk premiums: Evidence from S&P500 returns and options (2012, unknown): “As suggested in Fleming and Kirby (2003), we directly use GARCH processes as filters for the unobservable state variables.”
• A Practical Guide to Forecasting Financial Market Volatility (2005, methodology): “3 This possibly explains why GJR was the worst-performing model in Franses and Van Dijk (1996) because they use MedSE (median standard error) as their sole evaluation criterion.”