| By: |
John M Maheu;
Thomas H McCurdy |
| Abstract: |
Many finance questions require a full characterization of the distribution of
returns. We propose a bivariate model of returns and realized volatility (RV),
and explore which features of that time-series model contribute to superior
density forecasts over horizons of 1 to 60 days out of sample. This term
structure of density forecasts is used to investigate the importance of: the
intraday information embodied in the daily RV estimates; the functional form
for log(RV) dynamics; the timing of information availability; and the assumed
distributions of both return and log(RV) innovations. We find that a joint
model of returns and volatility that features two components for log(RV)
provides a good fit to S&P 500 and IBM data, and is a significant improvement
over an EGARCH model estimated from daily returns. |
| Keywords: |
RV, multiperiod, out-of-sample, term structure of density forecasts, observable SV |
| JEL: |
C1 C50 C32 G1 |
| Date: |
2008–08–06 |
| URL: |
http://d.repec.org/n?u=RePEc:tor:tecipa:tecipa-324&r=mst |