000146904 001__ 146904
000146904 005__ 20190619003258.0
000146904 0247_ $$2doi$$a10.5075/epfl-thesis-4688
000146904 02470 $$2urn$$aurn:nbn:ch:bel-epfl-thesis4688-9
000146904 02471 $$2nebis$$a5996752
000146904 037__ $$aTHESIS
000146904 041__ $$aeng
000146904 088__ $$a4688
000146904 245__ $$aRobust Multivariate and Nonlinear Time Series Models
000146904 269__ $$a2010
000146904 260__ $$bEPFL$$c2010$$aLausanne
000146904 300__ $$a158
000146904 336__ $$aTheses
000146904 520__ $$aTime series modeling and analysis is central to most financial and econometric data modeling. With increased globalization in trade, commerce and finance, national variables like gross domestic productivity (GDP) and unemployment rate, market variables like indices and stock prices and global variables like commodity prices are more tightly coupled than ever before. This translates to the use of multivariate or vector time series models and algorithms in analyzing and understanding the relationships that these variables share with each other. Autocorrelation is one of the fundamental aspects of time series modeling. However, traditional linear models, that arise from a strong observed autocorrelation in many financial and econometric time series data, are at times unable to capture the rather nonlinear relationship that characterizes many time series data. This necessitates the study of nonlinear models in analyzing such time series. The class of bilinear models is one of the simplest nonlinear models. These models are able to capture temporary erratic fluctuations that are common in many financial returns series and thus, are of tremendous interest in financial time series analysis. Another aspect of time series analysis is homoscedasticity versus heteroscedasticity. Many time series data, even after differencing, exhibit heteroscedasticity. Thus, it becomes important to incorporate this feature in the associated models. The class of conditional heteroscedastic autoregressive (ARCH) models and its variants form the primary backbone of conditional heteroscedastic time series models. Robustness is a highly underrated feature of most time series applications and models that are presently in use in the industry. With an ever increasing amount of information available for modeling, it is not uncommon for the data to have some aberrations within itself in terms of level shifts and the occasional large fluctuations. Conventional methods like the maximum likelihood and least squares are well known to be highly sensitive to such contaminations. Hence, it becomes important to use robust methods, especially in this age with high amounts of computing power readily available, to take into account such aberrations. While robustness and time series modeling have been vastly researched individually in the past, application of robust methods to estimate time series models is still quite open. The central goal of this thesis is the study of robust parameter estimation of some simple vector and nonlinear time series models. More precisely, we will briefly study some prominent linear and nonlinear models in the time series literature and apply the robust S-estimator in estimating parameters of some simple models like the vector autoregressive (VAR) model, the (0, 0, 1, 1) bilinear model and a simple conditional heteroscedastic bilinear model. In each case, we will look at the important aspect of stationarity of the model and analyze the asymptotic behavior of the S-estimator.
000146904 6531_ $$avector models
000146904 6531_ $$amultivariate time series
000146904 6531_ $$arobust estimation
000146904 6531_ $$aoutlier propagation
000146904 6531_ $$astationarity
000146904 6531_ $$avector autoregression
000146904 6531_ $$abilinear series
000146904 6531_ $$aconditional heteroscedasticity
000146904 6531_ $$aS-estimator
000146904 6531_ $$aFast-S
000146904 6531_ $$amodèles vectoriels
000146904 6531_ $$aséries multivariées
000146904 6531_ $$aestimation robuste
000146904 6531_ $$aoutlier propagation
000146904 6531_ $$astationnarité
000146904 6531_ $$aautorégression vectorielle
000146904 6531_ $$aséries bilinéaires
000146904 6531_ $$ahétéroscédasticité conditionnelle
000146904 6531_ $$aS-estimateur
000146904 6531_ $$aFast-S
000146904 700__ $$0243487$$g148959$$aRamakrishnan, Ravi
000146904 720_2 $$aMorgenthaler, Stephan$$edir.$$g105911$$0241889
000146904 8564_ $$uhttps://infoscience.epfl.ch/record/146904/files/EPFL_TH4688.pdf$$zTexte intégral / Full text$$s1136038$$yTexte intégral / Full text
000146904 909C0 $$xU10127$$0252209$$pSTAP
000146904 909CO $$pthesis-public$$pDOI$$pSB$$ooai:infoscience.tind.io:146904$$qGLOBAL_SET$$pthesis$$pthesis-bn2018$$qDOI2
000146904 918__ $$dEDMA$$cIMA$$aSB
000146904 919__ $$aSTAP
000146904 920__ $$b2010
000146904 970__ $$a4688/THESES
000146904 973__ $$sPUBLISHED$$aEPFL
000146904 980__ $$aTHESIS