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

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  • John M. Maheu
  • Thomas H. McCurdy

    ()

Abstract

This paper models different components of the return distribution which are assumed to be directed by a latent news process. The conditional variance of returns is a combination of jumps and smoothly changing components. This mixture captures occasional large changes in price, due to the impact of news innovations such as earnings surprises, as well as smoother changes in prices which can result from liquidity trading or strategic trading as information disseminates. Unlike typical SV-jump models, previous realizations of both jump and normal innovations can feedback asymmetrically into expected volatility. This is a new source of asymmetry (in addition to good versus bad news) that improves forecasts of volatility particularly after large moves such as the '87 crash. A heterogeneous Poisson process governs the likelihood of jumps and is summarized by a time varying conditional intensity parameter. The model is applied to returns from individual companies and three indices. We provide empirical evidence of the impact and feedback effects of jump versus normal return innovations, contemporaneous and lagged leverage effects, the time-series dynamics of jump clustering, and the importance of modeling the dynamics of jumps around high volatility episodes. Cet article modélise les différentes composantes de la distribution des rendements qui sont supposés être régis par un processus latent de nouvelles. La variance conditionnelle des rendements est une combinaison de sauts et de composantes qui varient continûment. Ce mélange permet de capter les grands changements occasionnels de prix qui sont dus à l'impact des nouvelles, telles que des surprises dans les revenus d'une compagnie, aussi bien que des changements plus lisses des prix qui peuvent résulter de transactions de liquidité ou de transactions stratégiques au fur et à mesure que l'information est disséminée. À la différence des modèles classique de sauts SV, les réalisations précédentes des sauts et des innovations normales peuvent intervenir asymétriquement dans la volatilité espérée. Il s'agit d'une nouvelle source d'asymétrie qui améliore les prévisions de volatilité, en particulier après de grands mouvements tels que le crash de 87. Un processus de Poisson hétérogène régit la probabilité des sauts et est représenté par un paramètre d'intensité conditionnelle qui varie dans le temps. Le modèle est appliqué aux rendements de différentes compagnies et à trois indices. Nous montrons ainsi empiriquement l'impact et les effets de rétroaction des sauts par rapport aux innovations normales, les effets de leviers simultanés et décalés, la dynamique de série temporelle du groupement des sauts, et l'importance de modéliser la dynamique des sauts dans les périodes de volatilité élevée.

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Bibliographic Info

Paper provided by CIRANO in its series CIRANO Working Papers with number 2003s-38.

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Date of creation: 01 Jun 2003
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Handle: RePEc:cir:cirwor:2003s-38

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Keywords: volatility components; news impacts; conditional jump intensity; jump size; leverage effects; filter; composantes de volatilité; impact des nouvelles; intensité conditionnelle des sauts; taille des sauts; effets de levier; filtre;

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