| Abstract: |
The extraction of the jump component in dynamics of asset prices haw witnessed
a considerably growing body of literature. Of particular interest is the
decomposition of returns' quadratic variation between their continuous and
jump components. Recent contributions highlight the importance of this
component in forecasting volatility at different horizons. In this article, we
extend a methodology developed in Maheu and McCurdy (2011) to exploit the
information content of intraday data in forecasting the density of returns at
horizons up to sixty days. We follow Boudt et al. (2011) to detect intraday
returns that should be considered as jumps. The methodology is robust to
intra-week periodicity and further delivers estimates of signed jumps in
contrast to the rest of the literature where only the squared jump component
can be estimated. Then, we estimate a bivariate model of returns and
volatilities where the jump component is independently modeled using a jump
distribution that fits the stylized facts of the estimated jumps. Our
empirical results for S&P 500 futures, U.S. 10-year Treasury futures, USD/CAD
exchange rate and WTI crude oil futures highlight the importance of
considering the continuous/jump decomposition for density forecasting while
this is not the case for volatility point forecast. In particular, we show
that the model considering jumps apart from the continuous component
consistenly deliver better density forecasts for forecasting horizons ranging
from 1 to 30 days |