Evidence for and the Impact of Jumps in Volatility and Returns

By Bjørn Eraker, Michael Johannes, Nicholas G. Polson. 2002

354 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0c7249457366af767662554b2c905bfc9fb2cb53

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Citations in the corpus

Bayesian Mixed Frequency VARs (2015, methodology): “In a similar vein, the Bayesian approach can easily incorporate heavy tailed error distributions in the form of mixture of normals or t distributions, Markov mixtures as in Albert and Chib (1993) or jumps as in Eraker et al. (2003).”
Forecasting the volatility of Bitcoin: The importance of jumps and structural breaks (2020, unknown): “Jumps have a long history in finance and have traditionally been estimated from daily data (for instance Andersen et al 2002; Eraker et al 2003; Eraker 2004).”
Maximum likelihood estimation of the double exponential jump-diffusion process (2007, background): “As Eraker et al. (2003) and others have shown, stochastic volatility is an important component of the return process and should be integrated into the DEJD specification.”
Volatility dynamics for the S&P 500: Further evidence from non-affine, multi-factor jump diffusions (2012, methodology): “Motivated by findings in the time-series literature (most prominently Eraker et al., 2003), Eraker (2004) and Broadie et al. (2007) investigate the role of variance jumps for pricing S&P 500 index and futures option contracts, respectively.”
Bayesian forecasting in economics and finance: A modern review (2022, methodology): “Estimation of and forecasting with such models is nevertheless computationally feasible via Bayesian methods, with MCMC algorithms of one form or another forming the backbone of the early treatments (Eraker, 2001; Eraker et al., 2003; Eraker, 2004; Forbes et al., 2007; Johannes et al., 2009).”