Continuous-Time Models, Realized Volatilities, and Testable Distributional Implications for Daily Stock Returns
By Torben G. Andersen, Tim Bollerslev, Per Frederiksen, Morten Ørregaard Nielsen. 2010
236 citations recorded by the source corpus.
We provide an empirical framework for assessing the distributional properties of daily speculative returns within the context of the continuous-time jump diffusion models traditionally used in asset pricing finance. Our approach builds directly on recently developed realized variation measures and non-parametric jump detection statistics constructed from high-frequency intra-day data. A sequence of simple-to-implement moment-based tests involving various transformations of the daily returns speak directly to the importance of different distributional features, and may serve as useful diagnostic tools in the specification of empirically more realistic continuous-time asset pricing models. On applying the tests to the 30 individual stocks in the Dow Jones Industrial Average index, we find that it is important to allow for both time-varying diffusive volatility, jumps, and leverage effects to satisfactorily describe the daily stock price dynamics. Copyright © 2009 John Wiley & Sons, Ltd.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/08f862b28f18271705e7c5e43b4282a31fc44457
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Stavros Degiannakis *
- George Filis
- Vipin Arora
- D. Broadstock
- Ioannis Chatziantoniou
- Perry Sadorsky
- Nikolaos Antonakakis
- R. Brooks
In Finance
Citations in the corpus
- Forecasting oil price realized volatility using information channels from other asset classes (2017, background): “1 See, for instance, Hamilton, 1983; Jones and Kaul (1996), Kilian and Park (2009), Filis (2010), Arouri et al. (2011), Filis et al. (2011), Degiannakis et al. (2013), Rahman and Serletis (2011), Baumeister and Peersman (2013), Filis and Chatziantoniou (2014). 2 See, Bollerslev et al.”
- Do High-Frequency Data Improve High-Dimensional Portfolio Allocations? (2013, unknown): “This approach is in line with Andersen et al. (2010) treating overnight returns as deterministic jumps.”
- A blocking and regularization approach to high dimensional realized covariance estimation (2012, unknown): “Diebold, and Labys (2001), Ait-Sahalia and Mancini (2008) and Andersen, Bollerslev, Frederiksen, and Nielsen (2009) .”