Title: News - Good or Bad - and its Impact on Volatility Predictions over Multiple Horizons
Abstract: We examine whether the sign and magnitude of discretely sampled high frequency returns have impact on future volatility predictions. We first let the 'data speak', namely with minimal interference we capture the mapping between returns over short horizons and future volatility over longer horizons. Technically speaking, we introduce semi-parametric MIDAS regressions. Compared to the semi-parametric infinite ARCH estimation in Linton and Mammen (2005) we show that the asymptotic distribution of semi-parametric MIDAS regressions depends on the mixed data sampling scheme. Also novel is the parametric specification we consider to deal with for intra-daily/daily lags. In the empirical work we revisit the concept of news impact curves introduced by Engle and Ng (1993), in the current high frequency data environment of financial market time series. We find that moderately good (intra-daily) news reduces volatility (the next day), while both very good news (unusual high positive returns) and bad news (negative returns) increase volatility, with the latter having a more severe impact. The asymmetries we find have profound implications for current volatility prediction models that are based on in-sample asymptotic analysis developed over recent years.
Publication Year: 2008
Publication Date: 2008-01-01
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 18
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