Continuous-Time Models, Realized Volatilities, and Testable Distributional Implications for Daily Stock Returns

TY - UNPB

T1 - Continuous-Time Models, Realized Volatilities, and Testable Distributional Implications for Daily Stock Returns

AU - Andersen, Torben G.

AU - Bollerslev, Tim

AU - Frederiksen, Per Houmann

AU - Nielsen, Morten Ørregaard

PY - 2007

Y1 - 2007

N2 - We provide an empirical framework for assessing the distributional properties of daily specu-lative returns within the context of the continuous-time modeling paradigm traditionally usedin asset pricing finance. Our approach builds directly on recently developed realized variationmeasures and non-parametric jump detection statistics constructed from high-frequency intra-day data. A sequence of relatively simple-to-implement moment-based tests involving varioustransforms of the daily returns speak directly to the import of different features of the under-lying continuous-time processes that might have generated the data. As such, the tests mayserve as a useful diagnostic tool in the specification of empirically more realistic asset pricingmodels. Our results are also directly related to the popular mixture-of-distributions hypoth-esis and the role of the corresponding latent information arrival process. On applying oursequential test procedure to the thirty individual stocks in the Dow Jones Industrial Averageindex, the data suggest that it is important to allow for both time-varying diffusive volatility,jumps, and leverage effects in order to satisfactorily describe the daily stock price dynamics.At a broader level, the empirical results also illustrate how the realized variation measures andhigh-frequency sampling schemes may be used in eliciting important distributional features andasset pricing implications more generally.

AB - We provide an empirical framework for assessing the distributional properties of daily specu-lative returns within the context of the continuous-time modeling paradigm traditionally usedin asset pricing finance. Our approach builds directly on recently developed realized variationmeasures and non-parametric jump detection statistics constructed from high-frequency intra-day data. A sequence of relatively simple-to-implement moment-based tests involving varioustransforms of the daily returns speak directly to the import of different features of the under-lying continuous-time processes that might have generated the data. As such, the tests mayserve as a useful diagnostic tool in the specification of empirically more realistic asset pricingmodels. Our results are also directly related to the popular mixture-of-distributions hypoth-esis and the role of the corresponding latent information arrival process. On applying oursequential test procedure to the thirty individual stocks in the Dow Jones Industrial Averageindex, the data suggest that it is important to allow for both time-varying diffusive volatility,jumps, and leverage effects in order to satisfactorily describe the daily stock price dynamics.At a broader level, the empirical results also illustrate how the realized variation measures andhigh-frequency sampling schemes may be used in eliciting important distributional features andasset pricing implications more generally.

KW - Return distributions, continuous-time models, mixture-of-distributions hypothesis, financial-time sampling, high-frequency data, volatility signature plots, realized volatilities, jumps, leverage and volatility feedback effects

M3 - Working paper

BT - Continuous-Time Models, Realized Volatilities, and Testable Distributional Implications for Daily Stock Returns

PB - Institut for Økonomi, Aarhus Universitet

CY - Aarhus

ER -