Show simple item record

dc.contributor.authorLU, Yang K.
dc.contributor.authorPERRON, Pierre
dc.date.accessioned2011-04-19T12:48:33Z
dc.date.available2011-04-19T12:48:33Z
dc.date.issued2010
dc.identifier.citationJournal of Empirical Finance, 2010, 17, 1, 138-156
dc.identifier.issn0927-5398
dc.identifier.urihttp://hdl.handle.net/1814/16542
dc.description.abstractWe consider the estimation of a random level shift model for which the series of interest is the sum of a short-memory process and a jump or level shift component. For the latter component, we specify the commonly used simple mixture model such that the component is the cumulative sum of a process which is 0 with some probability (1-alpha) and is a random variable with probability U. Our estimation method transforms such a model into a linear state space with mixture of normal innovations, so that an extension of Kalman filter algorithm can be applied. We apply this random level shift model to the logarithm of daily absolute returns for the S&P 500, AMEX, Dow Jones and NASDAQ stock Market return indices. Our point estimates imply few level shifts for all series. But once these are taken into account, there is little evidence of serial correlation in the remaining noise and, hence, no evidence of long-memory. Once the estimated shifts are introduced to a standard GARCH model applied to the returns series, any evidence of GARCH effects disappears. We also produce rolling out-of-sample forecasts of squared returns. In most cases, our simple random level shift model clearly outperforms a standard GARCH(1,1) model and, in many cases, it also provides better forecasts than a fractionally integrated GARCH model. (C) 2009 Elsevier B.V. All rights reserved.
dc.language.isoen
dc.publisherElsevier Science Bv
dc.subjectStructural change
dc.subjectForecasting
dc.subjectGARCH models
dc.subjectLong-memory
dc.titleModeling and Forecasting Stock Return Volatility Using a Random Level Shift Model
dc.typeArticle
dc.identifier.doi10.1016/j.jempfin.2009.10.001
dc.neeo.contributorLU|Yang K.|aut|
dc.neeo.contributorPERRON|Pierre|aut|
dc.identifier.volume17
dc.identifier.startpage138
dc.identifier.endpage156
eui.subscribe.skiptrue
dc.identifier.issue1


Files associated with this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record