TITLE:
Evaluating Volatility Forecasts with Ultra-High-Frequency Data—Evidence from the Australian Equity Market
AUTHORS:
Kai Zhang, Lurion De Mello, Mehdi Sadeghi
KEYWORDS:
High-Frequency Volatility, Volatility Forecasting, GARCH, Volatility Forecast Evaluation
JOURNAL NAME:
Theoretical Economics Letters,
Vol.8 No.1,
January
4,
2018
ABSTRACT: Due to the unobserved nature of the true return
variation process, one of the most challenging problems in evaluation of
volatility forecasts is to find an accurate benchmark proxy for ex-post volatility. This paper uses the Australian equity market ultra-high-frequency
data to construct an unbiased ex-post volatility estimator and then use
it as a benchmark to evaluate various practical volatility forecasting
strategies (GARCH class model based). These forecasting strategies allow for
the skewed distribution of innovations and use various estimation windows in
addition to the standard GARCH volatility models. In out-of-sample tests, we
find that forecasting errors across all model specifications are systematically
reduced if using the unbiased ex-post volatility estimator compared with
those using the realized volatility based on sparsely sampled intra-day data.
In particular, we show that the three benchmark forecasting models outperform
most of the modified strategies with different distribution of returns and
estimation windows. Comparing the three standard GARCH class models, we find
that the asymmetric power ARCH (APARCH) model exhibits the best forecasting
power in both normal and financial turmoil periods, which indicates the ability
of APARCH model to capture the leptokurtic returns and stylized features of
volatility in the Australian stock market.