Are combination forecasts of S&P 500 volatility statistically superior?

For the purposes of this study estimates of actual volatility were obtained
using the realized volatility (RV) methodology outlined in ABDL (2001, 2003).
RV estimates volatility by means of aggregating intra-day squared returns. It
should be noted that the daily trading period of the SVP500 is 6.5 hours and
that overnight returns were used as the first intra-day return in order to capture
the variation over the full calender day. ABDL (1999) suggest how to deal with
practical issues relating to intra-day seasonality and sampling frequency when
dealing with intra-day data. Based on this methodology, daily RV estimates
were constructed using 30 minute SVP500 index returns². It is widely acknowl-
edged (see e.g. Poon and Granger, 2003) that RV is a more accurate and less
noisy estimate of the unobservable volatility process than squared daily returns.
Patton (2006) suggests that this property of RV is beneficial when RV is used
a proxy for observed volatility when evaluating forecasts.

Figure 1 shows the VIX and daily SVP500 RV for the sample period consid-
ered. While the RV estimates exhibit a similar overall pattern when compared
to the VIX, RV reaches higher peaks than the VIX. This difference is mainly
due to the fact that the VIX represents an average volatility measure for a 22
trading day period as opposed to RV that is a measure of daily volatility.

3 Methodology

In this section the econometric models upon which forecasts are based will
be outlined, followed by how the competing forecasts will be combined. This
section concludes with a discussion of the technique utilised to discriminate
²Intraday S&P 500 index data were purchased from Tick Data, Inc.

<<   3   4   5   6   7   8   9   >>

More intriguing information