This paper uses estimation techniques related to those of Galbraith and Zinde-Walsh (2000) for ARCH and GARCH models, based on realized volatility (Andersen and Bollerslev 1998, and others), to estimate the conditional quantiles of daily volatility in samples of equity index and foreign exchange data. These techniques in principle allow us to characterize the entire conditional distribution of volatility, conditioning on past realized volatility and past squared returns. We take samples of daily and intra-day returns on the Toronto Stock Exchange 35 index, the DM/$ US exchange rate and the Yen/$ US exchange rate. In addition to information about the conditional extremes of volatility, we find some evidence that lower percentiles of the conditional distribution rise proportionately less in high-volatility periods than do the higher percentiles.
Nous utilisons des techniques d'estimation de modèle reliées à ceux de Galbraith et Zinde-Walsh (2000) pour les modèles ARCH et GARCH, basées sur la realized volatility (Andersen et Bollerslev 1998, et autres), afin d'obtenir les quantiles conditionnels de volatilité quotidienne dans les données provenant des marchés boursiers et des marchés de devises étrangères. Ces méthodes nous permettent en principe de caractériser la distribution entière de volatilité en utilisant la volatilité réalisée et les retours carrés. Nous prenons des échantillons de rendements quotidiens et intrajournaliers de l'indice 35 du TSE, et des taux de change DM/$ US et Yen/$ US. Nos résultats montrent également que les percentiles inférieurs de la distribution conditionnelle augmentent proportionnellement moins en périodes de volatilité extrême que les percentiles supérieurs.
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Find related papers by JEL classification: C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
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