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Pearson, Neil D. Deng, Qian 2015-09-28T16:03:16Z 2015-09-28T16:03:16Z 10000-01-01 2008 2008 The third chapter estimates the conditional variance of daily stock returns using an extended GARCH model with event-related dummy variables to capture the predictable components of volatility change, such as earnings announcements, macroeconomic announcements, day-of-the-week effects, etc. We examine the out-of-sample forecasting ability and find this model provides a better performance compared to the usual GARCH(1,1) volatility model. In addition, we find that the dependence on the random components increases after we include the predictable components. This implies that modeling volatilities using only past returns without other predictable variables could underestimate the persistence levels of volatilities and thus bias the volatility forecasts, especially those over long horizons. Made available in DSpace on 2015-09-28T16:03:16Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3314760.pdf: 1506573 bytes, checksum: 1108ebc3321cb6d8dc44791f69224741 (MD5) Previous issue date: 2008 Embargo set by: Seth Robbins for item 88721 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 100 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008. http://hdl.handle.net/2142/87440 (MiAaPQ)AAI3314760 eng Economics, Finance Three Essays on Empirical Asset Pricing text Finance Finance University of Illinois at Urbana-Champaign Dissertation Ph.D.