Conditional Jump Dynamics in Stock Market Returns

By W. Chan, John M. Maheu. 2002

368 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/9c76342cd1517036a4a7e3931fa55529f9658c93

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Citations in the corpus

News Arrival, Jump Dynamics and Volatility Components for Individual Stock Returns (2004, positive): “defined as an autoregressive conditional jump intensity (ARJI) model in Chan and Maheu (2002). In this model the conditional jump intensity λt is assumed to be autoregressive and related to last period’s conditional intensity as well as to an intensity residual ξt−1. It seems likely that the probabi”
Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility (2007, positive): “The recent discrete-time parametric model estimates reported in Chan and Maheu (2002) and McCurdy and Maheu (2004) also point to the existence of time-varying jump intensities in U.S. equity index returns.”
Backtesting Value-at-Risk Based on Tail Losses (2010, background): “Readers are referred to Chan and Maheu (2002) for a more flexible specification on the jump dynamics.”
Cross-Stock Comparisons of the Relative Contribution of Jumps to Total Price Variance (2012, background): “Another explanation may relate to the fact that the jump intensities themselves are time varying; there is some evidence for this phenomenon (Chan & Maheu, 2002; Maheu & McCurdy, 2004).”
Robust Forecasting of Dynamic Conditional Correlation GARCH Models (2013, methodology): “In the economic literature, the occurrence of additive jumps is mostly modeled by means of a Poisson distribution, such as in Vlaar and Palm (1993) and Chan and Maheu (2002).”
A Reduced Form Framework for Modeling Volatility of Speculative Prices Based on Realized Variation Measures (2007, methodology): “Discrete-time GARCH models incorporating Poisson jump processes with time-varying jump intensities based solely on daily data have also previously been estimated by Chan and Maheu (2002) , and Maheu and McCurdy (2004), while earlier work by Neely (1999) highlights the potential benefits from removi”
Semiparametric Conditional Quantile Models for Financial Returns and Realized Volatility (2013, background): “…stochastic volatility with possibly discontinuous sample paths (Todorov & Tauchen, 2011), the leverage effect characterized by negative correlation between volatility and price innovations (Bollerslev, Litvinova & Tauchen, 2006), time-varying jump intensity and jump sizes (Chan & Maheu, 2002), etc.”
No-Arbitrage Semi-Martingale Restrictions for Continuous-Time Volatility Models subject to Leverage Effects, Jumps and i.i.d. Noise: Theory and Testable Distributional Implications (2007, neutral): “…the importance of incorporating jumps in the price process; see, among others, Aı̈t-Sahalia (2002), Andersen et al. (henceforth ABL) (2002), Bates (2000), Chan and Maheu (2002), Chernov et al. (2003), Drost et al. (1998), Eraker (2004), Eraker et al. (2003), Johannes (2004), Maheu and McCurdy…”
Macroeconomic News, Announcements, and Stock Market Jump Intensity Dynamics (2011, neutral): “17Clark (2004) and Ashley, Granger, and Schmalensee (1980) have favored the approach of splitting the sample in two non-overlapping in-sample and out-of-sample portions to evaluate the predictive power of models and prevent overfitting.”
Empirical Characteristic Function Estimation and Its Applications (2004, positive): “The result suggests overwhelming evidence of jump clustering, consistent with the empirical findings recently documented in Chan and Maheu (2002) and Maheu and McCurdy (2003). Second, the ECF estimates of μQ, σ(2) Q are highly significant and indicate that jumps are present in the sample path of DJI”