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BEGIN:VEVENT
DTSTAMP:20261019T011022Z
LAST-MODIFIED:20121116T203436Z
DTSTART:20120620T164500Z
DTEND:20120620T181500Z
UID:event934@bu.edu
URL:http://physics.bu.edu/internal/events/show/934
SUMMARY:Quantifying and Modeling Cross-Correlations in Multiple Time Series
DESCRIPTION:Featuring Duan Wang\n\nPart of the Preliminary Oral Exam.\n\nEx
	amining Committee: H.E. Stanley\, William Klein\, Robert Carey\, Boris Podo
	bnik\nAbstract and link of paper: http://pre.aps.org/abstract/PRE/v83/i4/e0
	46121\nWe propose a modified time lag random matrix theory in order to stud
	y time-lag cross correlations in multiple time series. We apply the method 
	to 48 world indices\, one for each of 48 different countries. We find long-
	range power-law cross correlations in the absolute values of returns that q
	uantify risk\, and find that they decay much more slowly than cross correla
	tions between the returns. The magnitude of the cross correlations constitu
	tes “bad news” for international investment managers who may be
	lieve that risk is reduced by diversifying across countries. We find that w
	hen a market shock is transmitted around the world\, the risk decays very s
	lowly. We explain these time-lag cross correlations by introducing a global
	 factor model (GFM) in which all index returns fluctuate in response to a s
	ingle global factor. For each pair of individual time series of returns\, t
	he cross correlations between returns (or magnitudes) can be modeled with t
	he autocorrelations of the global factor returns (or magnitudes). We estima
	te the global factor using principal component analysis\, which minimizes t
	he variance of the residuals after removing the global trend. Using random 
	matrix theory\, a significant fraction of the world index cross correlation
	s can be explained by the global factor\, which supports the utility of the
	 GFM. We demonstrate applications of the GFM in forecasting risks at the wo
	rld level\, and in finding uncorrelated individual indices. We find ten ind
	ices that are practically uncorrelated with the global factor and with the 
	remainder of the world indices\, which is relevant information for world ma
	nagers in reducing their portfolio risk. Finally\, we argue that this gener
	al method can be applied to a wide range of phenomena in which time series 
	are measured\, ranging from seismology and physiology to atmospheric geophy
	sics.
LOCATION:SCI 352\, 590 Commonwealth Avenue\, 02215
STATUS:CONFIRMED
CLASS:PUBLIC
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