A discrete-time model for daily S &amp; P500 returns and realized variations: Jumps and leverage effects

Bollerslev, Tim; Kretschmer, Uta; Pigorsch, Christian; Tauchen, George

doi:10.1016/j.jeconom.2008.12.001

Cited by 168 publications

(68 citation statements)

References 71 publications

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“…11 9 A similar leverage or volatility feedback e ect could in principle work through the jump component. However, the related empirical evidence in Bollerslev, Kretschmer, Pigorsch & Tauchen (2008) suggests that the asymmetry works almost exclusively through the di usive component. 10 A corresponding "business-time" sampling scheme for pure jump processes has previously is used by Oomen (2006), while Zhou (1998) refers to similarly sampled returns as de-volatized.…”

Section: Leverage and Volatility Feedback E Ectsmentioning

confidence: 99%

Continuous‐time models, realized volatilities, and testable distributional implications for daily stock returns

Andersen

Bollerslev

Frederiksen³

et al. 2010

J of Applied Econometrics

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174

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractWe provide an empirical framework for assessing the distributional properties of daily speculative returns within the context of the continuous-time jump di usion models traditionally used in asset pricing nance. Our approach builds directly on recently developed realized variation measures and non-parametric jump detection statistics constructed from high-frequency intraday data. A sequence of simple-to-implement moment-based tests involving various transformations of the daily returns speak directly to the importance of di erent distributional features, and may serve as useful diagnostic tools in the speci cation of empirically more realistic continuous-time asset pricing models. On applying the tests to the thirty individual stocks in the Dow Jones Industrial Average index, we nd that it is important to allow for both time-varying di usive volatility, jumps, and leverage e ects to satisfactorily describe the daily stock price dynamics.JEL Classi cations: C1, G1.

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Section: Leverage and Volatility Feedback E Ectsmentioning

confidence: 99%

Continuous‐time models, realized volatilities, and testable distributional implications for daily stock returns

Andersen

Bollerslev

Frederiksen³

et al. 2010

J of Applied Econometrics

Self Cite

174

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“…Christie (1982); Campbell and Hentschel (1992); Glosten et al (1989) and more recently ), Bollerslev et al (2009 and Martens et al (2009). We extend the heterogeneous structure to the standard leverage effect by including lagged negative returns at different frequencies as explanatory variables to forecast volatility.…”

Section: Introductionmentioning

confidence: 99%

Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling

Corsi

Renò

2012

Journal of Business & Economic Statistics

275

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Citation: Corsi, F. and Reno, R. (2012). Discrete-time volatility forecasting with persistent leverage effect and the link with continuous-time volatility modeling. Journal of Business and Economic Statistics, 30(3), pp. 368-380. doi: 10.1080/07350015.2012.663261 This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract We first propose a reduced form model in discrete time for S&P500 volatility showing that the forecasting performance can be significantly improved by introducing a persistent leverage effect with a long-range dependence similar to that of volatility itself. We also find a strongly significant positive impact of lagged jumps on volatility, which however is absorbed more quickly. We then estimate continuous-time stochastic volatility models which are able to reproduce the statistical features captured by the discrete-time model. We show that a single-factor model driven by a fractional Brownian motion is unable to reproduce the volatility dynamics observed in the data, while a multi-factor Markovian model fully replicates the persistence of both volatility and leverage effect. The impact of jumps can be associated with a common jump component in price and volatility. Permanent

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“…Jarrow and Rosenfeld, 1984;Liu, Longstaff, and Pan, 2003). Andersen, Bollerslev, and Diebold (2007) and Bolleslev, Kretschmer, Pigorsch, and Tauchen (2009) also point to its importance for the empirical modeling of asset price dynamics and forecasting of volatility.…”

Section: Introductionmentioning

confidence: 99%

Realised quantile-based estimation of the integrated variance

Christensen

Oomen

Podolskij

2010

Journal of Econometrics

103

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Please cite this article as: Christensen, K., Oomen, R., Podolskij, M., Realised quantile-based estimation of the integrated variance. Journal of Econometrics (2010Econometrics ( ), doi:10.1016Econometrics ( /j.jeconom.2010 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT Realised Quantile-Based Estimation of the Integrated Variance Kim ChristensenRoel Oomen Mark Podolskij * January 2010 AbstractIn this paper, we propose a new jump robust quantile-based realised variance measure of ex-post return variation that can be computed using potentially noisy data. The estimator is consistent for the integrated variance and we present feasible central limit theorems which show that it converges at the best attainable rate and has excellent efficiency. Asymptotically, the quantile-based realised variance is immune to finite activity jumps and outliers in the price series, while in modified form the estimator is applicable with market microstructure noise and therefore operational on high-frequency data. Simulations show that it has superior robustness properties in finite sample, while an empirical application illustrates its use on equity data.Keywords: Finite activity jumps; Market microstructure noise; Order statistics; Outliers; Realised variance.

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A discrete-time model for daily S & P500 returns and realized variations: Jumps and leverage effects

Cited by 168 publications

References 71 publications

Continuous‐time models, realized volatilities, and testable distributional implications for daily stock returns

Continuous‐time models, realized volatilities, and testable distributional implications for daily stock returns

Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling

Realised quantile-based estimation of the integrated variance

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