dc.contributor.author	Ammermann, Peter A.	en
dc.contributor.committeechair	Patterson, Douglas M.	en
dc.contributor.committeemember	Ye, Keying	en
dc.contributor.committeemember	McGuirk, Anya M.	en
dc.contributor.committeemember	Billingsley, Randall S.	en
dc.contributor.committeemember	Chance, Donald M.	en
dc.contributor.department	Finance	en
dc.date.accessioned	2014-03-14T20:15:45Z	en
dc.date.adate	1999-09-02	en
dc.date.available	2014-03-14T20:15:45Z	en
dc.date.issued	1999-07-16	en
dc.date.rdate	2000-09-02	en
dc.date.sdate	1999-08-27	en
dc.description.abstract	Numerous studies have documented the existence of nonlinearity within various financial time series. But how important of a finding is this? This dissertation examines this issue from a number of perspectives. First, is the nonlinearity that has been found a statistical anomaly that is isolated to a few of the more widely known financial time series or is nonlinearity a statistical regularity inherent in such series? Second, even if nonlinearity is pervasive, does this finding have any practical relevance for finance practitioners or academics? Using the relatively financially isolated but nonetheless well-traded Taiwan Stock Exchange as a case study, it is found that virtually all of the stocks trading on this exchange exhibit nonlinearity. The pervasiveness of nonlinearity within this market, combined with earlier results from other markets, suggests that nonlinearity is an inherent aspect of financial time series. Furthermore, closer examination of the time-paths of various measures of this nonlinearity via both windowed testing and recursive testing and parameter estimation reveals an additional complication, the possibility of nonstationarity. The serial dependency structures, especially for the nonlinear dependencies, do not appear to be constant, but instead appear to exhibit a number of brief episodes of extremely strong dependencies, followed by longer stretches of relatively quiet behavior. On average, though, these nonlinearities appear with sufficient strength to be significant for the full sample. Continuing on to examine the relevance of such nonlinearities for empirical work in finance, a variety of conditionally heteroskedastic models were fit to the returns for a subsample Taiwanese stocks, the Taiwanese stock index, and stock indices for other stock markets, including New York, London, Tokyo, Hong Kong, and Singapore. In a majority of cases, such models appear to be successful at filtering out the extant nonlinearity from these series of returns; however, a variety of indicators suggest that these models are not statistically well-specified for these returns, calling into question the inferences obtained from these models. Furthermore, a comparison of the various conditionally heteroskedastic models with each other and with a dynamic linear regression model reveals that, for many of the data series, the inferences obtained from these models regarding the day-of-the-week effect and the extant autocorrelation within the data varied from model to model. This finding suggests the importance of adequately accounting for nonlinear serial dependencies (and of ensuring data stationarity) when studying financial time series, even when other empirical aspects of the data are the focus of attention.	en
dc.description.degree	Ph. D.	en
dc.identifier.other	etd-082799-103026	en
dc.identifier.sourceurl	http://scholar.lib.vt.edu/theses/available/etd-082799-103026/	en
dc.identifier.uri	http://hdl.handle.net/10919/28816	en
dc.publisher	Virginia Tech	en
dc.relation.haspart	Chap8.pdf	en
dc.relation.haspart	Chap7.pdf	en
dc.relation.haspart	Chap6.pdf	en
dc.relation.haspart	chap5.pdf	en
dc.relation.haspart	Chap4.pdf	en
dc.relation.haspart	Chap2.pdf	en
dc.relation.haspart	Chap3.pdf	en
dc.relation.haspart	Chap1.pdf	en
dc.rights	In Copyright	en
dc.rights.uri	http://rightsstatements.org/vocab/InC/1.0/	en
dc.subject	Nonlinearity	en
dc.subject	Taiwan Stock Exchange	en
dc.subject	STAR	en
dc.subject	GARCH	en
dc.subject	Bispectrum	en
dc.title	Nonlinearity and Overseas Capital Markets: Evidence from the Taiwan Stock Exchange	en
dc.type	Dissertation	en
thesis.degree.discipline	Finance	en
thesis.degree.grantor	Virginia Polytechnic Institute and State University	en
thesis.degree.level	doctoral	en
thesis.degree.name	Ph. D.	en

Nonlinearity and Overseas Capital Markets: Evidence from the Taiwan Stock Exchange

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