Do Stock Prices and Volatility Jump? Reconciling Evidence from Spot and Option Prices

By Bjørn Eraker. 2004

912 citations recorded by the source corpus.

Subfields: Finance

View Paper: https://www.semanticscholar.org/paper/c044cf827016d3ae16119a84d0a5e1852ae40355

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Citations in the corpus

Variance Dynamics: Joint Evidence from Options and High-Frequency Returns (2011, methodology): “The specification becomes thexponentially distributed compound Poisson jump specification used in Eraker, Johannes, and Polson (2003) and Eraker (2004).”
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).”
Realizing smiles: Options pricing with realized volatility (2013, neutral): “…Merton (1976), Bates (1996), Bates (2000), Pan (2002), Huang (2004) and Bates (2006) in continuous-time and Maheu and McCurdy (2004), Duan et al. (2006) and Christoffersen et al. (2010b) in discrete-time) and jumps in volatility (see Eraker (2004), Eraker et al. (2003) and Broadie et al. (2007)).”
Bayesian inference for irreducible diffusion processes using the pseudo-marginal approach (2011, methodology): “This approximation has been used in Aı̈tSahalia and Kimmel (2007), Johannes et al. (2009), Chernov et al. (2003), Eraker (2004), and Jones (2003).”
Understanding Systematic Risk: A High‐Frequency Approach (2020, neutral): “6Empirical studies supporting this hypothesis include Bollerslev, Li, and Todorov (2016), Alexeev, Dungey, and Yao (2017), Pan (2002), Eraker, Johannes, and Polson (2003), Eraker (2004), Bollerslev and Todorov (2011) and Gabaix (2012).”
Pricing Currency Option in a Mixed Fractional Brownian Motion with Jumps Environment (2014, background): “Nevertheless, the experimental investigation on asset return shows that discontinuities or jumps are believed to be an indispensable element of financial stock pricing; see [12–17].”
Stochastic Volatility: Origins and Overview (2008, methodology): “Moreover, the task of forecasting volatility conditional on the past observed data (akin to filtering in MCMC) or extracting volatility given the full data series (akin to smoothing in MCMC) may be undertaken in the EMM setting through the reprojection method developed and illustrated in Gallant and”
A Bayesian Analysis of Return Dynamics with Lévy Jumps (2008, neutral): “For example, without jumps in returns and volatility, the above model reduces to the stochastic volatility model of Heston (1993). With MJs only in returns, we have the stochastic volatility and Merton jump model of Bates (1996) and Andersen, Benzoni and Lund (2002).”
Model-free hedge ratios and scale-invariant models (2007, background): “…as scale-invariant including: mixture diffusions (such as in Brigo and Mercurio, 2002), uncertain volatility models (Avellaneda et al., 1995), double jump models (Naik, 1993; Duffie et al., 2000; Eraker, 2004), and Lévy processes (Schoutens, 2003) if the drift and Lévy density are dimensionless.”
Estimating the Parameters of Stochastic Volatility Models Using Option Price Data (2015, methodology): “Eraker (2004), on the other hand, reported an estimate of −0.086%, which is much closer to zero.”
Macroeconomic News, Announcements, and Stock Market Jump Intensity Dynamics (2011, neutral): “From an empirical point of view, Eraker (2004) argues that none of these models have proved to be entirely successful.”
Forecasting the volatility of Bitcoin: The importance of jumps and structural breaks (2020, neutral): “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).”