Alternative models for stock price dynamics
By Mikhail Chernov, A. R. Gallant, Eric Ghysels, George Tauchen. 2003
550 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/fd2694605dbebe5d0041eea25a21b034d1e8a5a3
Citations in the corpus
- Simulation Methods for Lévy-Driven Continuous-Time Autoregressive Moving Average (CARMA) Stochastic Volatility Models (2006, unknown): “One approach to capture dependence is to use the Lévy copula introduced by Tankov (2003) and Cont and Tankov (2004) and studied further by Barndorff-Nielsen and Lindner (2004).”
- Continuous-Time Models, Realized Volatilities, and Testable Distributional Implications for Daily Stock Returns (2010, unknown): “A number of recent studies have argued for the importance of explicitly allowing for jumps, or q t 6D 0, when modeling speculative rates of return; see, e.g., Andersen et al. (2002), Bates (1996, 2000), Chernov et al. (2003), Eraker et al. (2003), Eraker (2004), and Johannes (2004).”
- A High Frequency Assessment of the ECB Securities Markets Programme (2017, unknown): “by D’Amico and King (2010) suggests that SMP stock effects may have played for those countries where the share of purchases to the total outstanding amount was sizeable.”
- SMC2: an efficient algorithm for sequential analysis of state space models (2011, unknown): “In such cases a Kalman filter and a Baum filter respectively can be associated with each θ-particle to evaluate efficiently the likelihood increments; see for example Chopin (2007). In contrast, sequential inference for both parameters and latent states for a generic state space model is a much more”
- Model Specification and Risk Premia: Evidence from Futures Options (2007, unknown): “In the SV model, Chernov and Ghysels (2000) estimate ηv = −0.001, Pan (2002) estimates ηv = −0.0301, Jones (2003) estimates ηv = 0.0326 using data from 1987 to 2003 and ηv = −0.0294 using post-1987 data, and Eraker (2004) estimates ηv = −0.01 and reports that the parameter is marginally significant.”
- Threshold Bipower Variation and the Impact of Jumps on Volatility Forecasting (2010, unknown): “Similar estimates have been obtained by Bates (2000); Pan (2002); Chernov et al. (2003).”