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Modeling and Forecasting Realized Volatility

by Torben G. Andersen, Tim Bollerslev, Francis X. Diebold, Paul Labys , 2002
"... this paper is built. First, although raw returns are clearly leptokurtic, returns standardized by realized volatilities are approximately Gaussian. Second, although the distributions of realized volatilities are clearly right-skewed, the distributions of the logarithms of realized volatilities are a ..."
Abstract - Cited by 544 (53 self) - Add to MetaCart
: a fractionally-integrated Gaussian vector autoregression (VAR) . Importantly, our approach explicitly permits measurement errors in the realized volatilities. Comparing the resulting volatility forecasts to those obtained from currently popular daily volatility models and more complicated high

Efficient tests for an autoregression unit root

by Graham Elliott, Thomas J. Rothenberg, James H. Stock - ECONOMETRICA , 1996
"... ..."
Abstract - Cited by 648 (4 self) - Add to MetaCart
Abstract not found

Finite state Markov-chain approximations to univariate and vector autoregressions

by George Tauchen - Economics Letters , 1986
"... The paper develops a procedure for finding a discrete-valued Markov chain whose sample paths approximate well those of a vector autoregression. The procedure has applications in those areas of economics, finance, and econometrics where approximate solutions to integral equations are required. 1. ..."
Abstract - Cited by 472 (0 self) - Add to MetaCart
The paper develops a procedure for finding a discrete-valued Markov chain whose sample paths approximate well those of a vector autoregression. The procedure has applications in those areas of economics, finance, and econometrics where approximate solutions to integral equations are required. 1.

Statistical Analysis of Cointegrated Vectors

by Soren Johansen - Journal of Economic Dynamics and Control , 1988
"... We consider a nonstationary vector autoregressive process which is integrated of order 1, and generated by i.i.d. Gaussian errors. We then derive the maximum likelihood estimator of the space of cointegration vectors and the likelihood ratio test of the hypothesis that it has a given number of dimen ..."
Abstract - Cited by 2576 (12 self) - Add to MetaCart
We consider a nonstationary vector autoregressive process which is integrated of order 1, and generated by i.i.d. Gaussian errors. We then derive the maximum likelihood estimator of the space of cointegration vectors and the likelihood ratio test of the hypothesis that it has a given number

Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models

by Robert Engle - Journal of Business and Economic Statistics , 2002
"... Time varying correlations are often estimated with Multivariate Garch models that are linear in squares and cross products of the data. A new class of multivariate models called dynamic conditional correlation (DCC) models is proposed. These have the flexibility of univariate GARCH models coupled wi ..."
Abstract - Cited by 684 (17 self) - Add to MetaCart
Time varying correlations are often estimated with Multivariate Garch models that are linear in squares and cross products of the data. A new class of multivariate models called dynamic conditional correlation (DCC) models is proposed. These have the flexibility of univariate GARCH models coupled with parsimonious parametric models for the correlations. They are not linear but can often be estimated very simply with univariate or two step methods based on the likelihood function. It is shown that they perform well in a variety of situations and provide sensible empirical results.

A No-Arbitrage Vector Autoregression of Term Structure Dynamics with Macroeconomic and Latent Variables

by Andrew Ang, Monika Piazzesi , 2002
"... ..."
Abstract - Cited by 471 (24 self) - Add to MetaCart
Abstract not found

Image denoising using a scale mixture of Gaussians in the wavelet domain

by Javier Portilla, Vasily Strela, Martin J. Wainwright, Eero P. Simoncelli - IEEE TRANS IMAGE PROCESSING , 2003
"... We describe a method for removing noise from digital images, based on a statistical model of the coefficients of an overcomplete multiscale oriented basis. Neighborhoods of coefficients at adjacent positions and scales are modeled as the product of two independent random variables: a Gaussian vecto ..."
Abstract - Cited by 514 (17 self) - Add to MetaCart
We describe a method for removing noise from digital images, based on a statistical model of the coefficients of an overcomplete multiscale oriented basis. Neighborhoods of coefficients at adjacent positions and scales are modeled as the product of two independent random variables: a Gaussian

A Simple Estimator of Cointegrating Vectors in Higher Order Cointegrated Systems

by James H. Stock, Mark W. Watson - ECONOMETRICA , 1993
"... Efficient estimators of cointegrating vectors are presented for systems involving deterministic components and variables of differing, higher orders of integration. The estimators are computed using GLS or OLS, and Wald Statistics constructed from these estimators have asymptotic x2 distributions. T ..."
Abstract - Cited by 507 (3 self) - Add to MetaCart
Efficient estimators of cointegrating vectors are presented for systems involving deterministic components and variables of differing, higher orders of integration. The estimators are computed using GLS or OLS, and Wald Statistics constructed from these estimators have asymptotic x2 distributions

Knowledge-based Analysis of Microarray Gene Expression Data By Using Support Vector Machines

by Michael P. S. Brown, William Noble Grundy, David Lin, Nello Cristianini, Charles Walsh Sugnet, Terrence S. Furey, Manuel Ares, Jr., David Haussler , 2000
"... We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of ..."
Abstract - Cited by 514 (8 self) - Add to MetaCart
We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge

New Support Vector Algorithms

by Bernhard Schölkopf, Alex J. Smola, Robert C. Williamson, Peter L. Bartlett , 2000
"... this article with the regression case. To explain this, we will introduce a suitable definition of a margin that is maximized in both cases ..."
Abstract - Cited by 461 (42 self) - Add to MetaCart
this article with the regression case. To explain this, we will introduce a suitable definition of a margin that is maximized in both cases
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