Results 21 to 30 of about 111,969 (307)
Dependence Measuring from Conditional Variances
Dependence Modeling, 2015 Abstract A conditional variance is an indicator of the level of independence between two random variables. We exploit this intuitive relationship and define a measure v which is almost a measure of mutual complete dependence. Unsurprisingly, the measure attains its minimum value for many pairs of non-independent ran- dom variables ...
Kamnitui Noppadon +2 more
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Structural Break Tests Robust to Regression Misspecification
Econometrics, 2018 Structural break tests for regression models are sensitive to model misspecification. We show—analytically and through simulations—that the sup Wald test for breaks in the conditional mean and variance of a time series process exhibits severe
Alaa Abi Morshed +2 more
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Duality in Mean-Variance Frontiers with Conditioning Information [PDF]
SSRN Electronic Journal, 2007 Portfolio and stochastic discount factor (SDF) frontiers are usually regarded as dual objects, and researchers sometimes use one to answer questions about the other. However, the introduction of conditioning information and active portfolio strategies alters this relationship.
Peñaranda, Francisco, Sentana, Enrique
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Modeling and Recognition of Driving Fatigue State Based on R-R Intervals of ECG Data
IEEE Access, 2019 Driving fatigue is an important contributing factor to traffic crashes. Developing a system that monitors the driver's fatigue level in real time and produces alarm signals when necessary, is important for the prevention of accidents. In the past decades,
Linhong Wang, Jingwei Li, Yunhao Wang
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An analysis of conditional mean-variance portfolio performance using hierarchical clustering
Journal of Finance and Data Science, 2023 This paper studies portfolio optimization through improvements of ex-ante conditional covariance estimates. We use the cross-section of stock returns over a 52-year sample to analyze trading performance by implementing the machine learning algorithm of ...
Stephen R. Owen
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Modelling the persistence of conditional variances [PDF]
Econometric Reviews, 1986 This paper will discuss the current research in building models of conditional variances using the Autoregressive Conditional Heteroskedastic (ARCH) and Generalized ARCH (GARCH) formulations. The discussion will be motivated by a simple asset pricing theory which is particularly appropriate for examining futures contracts with risk averse agents. A new
Robert F. Engle, Tim Bollerslev
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Risk Reduction of Portfolio based on Generalized Autoregressive Conditional Heteroscedasticity Model in Tehran Stock Exchange [PDF]
تحقیقات مالی, 2012 Return maximization or risk minimization is goal in portfolio optimization based on mean variance theory. The structure of correlation matrices and individual variance of each asset are two main factors in optimization with risk minimization object. It’s
Gholamreza Eslami Bidgoli +1 more
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The Financial Risk Measurement EVaR Based on DTARCH Models
Entropy, 2023 The value at risk based on expectile (EVaR) is a very useful method to measure financial risk, especially in measuring extreme financial risk. The double-threshold autoregressive conditional heteroscedastic (DTARCH) model is a valuable tool in assessing ...
Xiaoqian Liu +3 more
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Early warnings of regime shift when the ecosystem structure is unknown. [PDF]
PLoS ONE, 2012 Abrupt changes in dynamics of an ecosystem can sometimes be detected using monitoring data. Using nonparametric methods that assume minimal knowledge of the underlying structure, we compute separate estimates of the drift (deterministic) and diffusion ...
William A Brock, Stephen R Carpenter
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On Variance Conditions for Markov Chain CLTs
Electronic Communications in Probability, 2007 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Haggstrom, Olle, Rosenthal, Jeffrey
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