News Arrival, Jump Dynamics and Volatility Components for Individual Stock Returns

By John M. Maheu, Thomas H. McCurdy. 2004

578 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

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Citations in the corpus

Deep Learning Stock Volatility with Google Domestic Trends (2015, background): “More-over, news analytics [23] and arrival dynamics [24] could be of particular interest given their non-linear nature and neural networks’ ease of expressing non-linearities.”
Realizing smiles: Options pricing with realized volatility (2013, unknown): “…to include jumps in returns (see 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…”
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).”
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, unknown): “(1998), Eraker (2004), Eraker, Johannes and Polson (2003), Johannes (2004), Maheu and McCurdy (2004) , and Pan (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”
Properties of Realized Variance Under Alternative Sampling Schemes (2006, background): “Yet, a number of recent (and less recent) papers provide compelling evidence that these type of processes can be applied successfully to many issues in finance ranging from the modeling of low frequency returns (e.g. Ball and Torous, 1983; Carr, Geman, Madan, and Yor, 2002; Maheu and McCurdy, 2004) ”
Modeling financial contagion using mutually exciting jump processes (2015, unknown): “Relative to the classical jump-diffusion model in finance, which originated in Merton (1976), we allow the jumps to be mutually exciting in the fashion of Hawkes (1971b) as a means of capturing the contagion phenomenon: that is, a jump somewhere raises the likelihood of getting a jump elsewhere in t”
MCMC ESTIMATION OF LÉVY JUMP MODELS USING STOCK AND OPTION PRICES (2011, unknown): “Earlier studies, such as Jacquier, Polson, and Rossi (1994), Kim, Shephard, and Chib (1998), Chib, Nardari, and Shephard (2002), and Maheu and McCurdy (2004) among others, apply MCMC methods to estimate discrete-time stochastic volatility models.”
Empirical Characteristic Function Estimation and Its Applications (2004, unknown): “4 Stable Distribution The stable distribution was first proposed by Mandelbrot (1963) and Fama (1965) to model stock returns.”
Frontiers in VaR forecasting and backtesting (2016, methodology): “Chiu et al. (2005) compare the VaR forecasts obtained when an asymmetric GARCH model is fitted to represent the conditional variance with those obtained fitting the model proposed Maheu and McCurdy (2004) which includes jumps.”
Asset Pricing with Second-Order Esscher Transforms (2010, background): “…is used, the number of jumps per period is a Poisson distribution, with potentially time-varying intensity, and therefore the jump component is a particular infinite mixture of conditionally Gaussian processes [see Maheu and McCurdy (2004), Christoffersen, Jacobs and Ornthanalai (2008)].”
Some Like it Smooth, and Some Like it Rough: Untangling Continuous and Jump Components in Measuring, Modeling, and Forecasting Asset Return Volatility (2003, unknown): “…Andersen, Benzoni and Lund, 2002; Bates, 2002; Chan and Maheu, 2002; Chernov, Gallant, Ghysels, and Tauchen, 2003; Drost, Nijman and Werker, 1998; Eraker, Johannes and Polson, 2003; Johannes, Kumar and Polson, 1999; Maheu and McCurdy, 2003; Khalaf, Saphores and Bilodeau (2003; and Pan, 2002).”
Tails, Fears and Risk Premia (2010, background): “The discrete-time GARCH model estimates for individual stocks in Maheu and McCurdy (2004) also support the notion of time-varying jump intensities.”