A model with high variance
On estimation of the noise variance in a high-dimensional signal detection model
... In this paper, we identify completely the aforementioned negative bias of the variance estimator ˆσ 2 . Next using this identification, a bias-corrected estimator is found and its asymptotic normality proved under ...
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High-dimensional regression with unknown variance
... variance matrix Σ. This setting is mainly motivated by applications in com- pressed sensing [ 28 ] and in Gaussian graphical modeling. Indeed, Meinshausen and B¨ uhlmann [ 56 ] have proved that it is possible to ...
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The Mean Variance Model Revisited with a Cash Account
... our model are lower than those of corresponding traditional efficient portfolios, regardless of the type of portfolio, and thereby they have higher Sharpe ...modified model ranges from 0.8993 to 0.9322. ...
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The Constant Elasticity of Variance Option Pricing Model
... of high volatility should correspond to high share price level, and that periods of low volatility should correspond to periods of low share price ...CEV model would ...
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An Empirical Temperature Variance Source Model in Heated Jets
... source model is achieved when we consider the mean static pressure as a ...at high Mach ...at high subsonic Mach numbers and a negative contribution at supersonic Mach numbers when the observer angle ...
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Variance Estimation for High Dimensional Varying Index Coefficient Models
... a high probability to a certain ...The model variance estimated by the RCV-SIS two-stage method is significantly better than the N-SIS simple two-stage ...the high-dimensional case, the ...
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The best estimation for high dimensional Markowitz mean variance optimization
... On the other hand, Jacobs, Levy, and Markowitz (2005) present fast algorithms for calculating MV efficient frontiers when the investor can sell securities short as well as buy them long, and when a factor and/or scenario ...
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The best estimation for high-dimensional Markowitz mean-variance optimization
... On the other hand, Jacobs, Levy, and Markowitz (2005) present fast algorithms for calculating MV e fficient frontiers when the investor can sell securities short as well as buy them long, and when a factor and /or scenario ...
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Evolutionary variance of gene network model via simulated annealing
... out high fitnesses of gene network models, and tracked the fitness to show how gene networks are ...a high fitness of gene networks, we applied simulated annealing ...the high fitness of the gene ...
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Mean-variance hedging and optimal investment in Heston's model with correlation
... very high one may observe a significant deviation of the market price C 0 from the representative agent price V 0 which does not give rise to excessively attrac- tive investment opportunities, beyond the ones that ...
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The mean-variance model from the inverse of the variance-covariance matrix
... minimum variance among all the portfolios with zero expected ...the variance V along the critical line is a quadratic ...the variance- covariance matrix is defined as ...
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On generalized variance of normal Poisson model and Poisson variance estimation under Gaussianity
... generalized variance have been discussed by several ...generalized variance is used as a criterion for an unbiased critical region to have the maximum Gaussian curvature ...generalized variance has ...
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Fitting Variance Components Model and Fixed Effects Model for One-Way Analysis of Variance to Complex Survey Data
... the model covariates the design variables that define the selection probabilities at the various ...the model covariates, the resulting model may be no longer of scientific ...the model is fitted ...
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High dimensional Global Minimum Variance Portfolio
... For the risks of the GMVP estimates, the perfor- mance of the spectral corrected GMV portfolio is not significant different from the other as y = 0.1 and is significantly better than the others. For all y s from 0.2 to ...
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Model independent hedging strategies for variance swaps
... the model-free bound is optimal, and can be ...the variance swap depending on the form of the kernel, but there could be other sub- and super-replicating strategies which do not take this ...tighter ...
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Variance targeting estimation of the BEKK X model
... Variance targeting estimation of the BEKK-X model. Thieu, Le Quyen[r] ...
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The Mean Variance Mixing GARCH (1,1) model
... The first two papers have the common feature of stating a skewness parameter as a function of the conditioning information set. One problem becomes to choose which function that captures the time dependence in the third ...
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Variance targeting estimation of the BEKK-X model
... of model: The Asymmetric Power ARCH(p, q)-X model with an unrestrictive number of the exogenous ...log-GARCH model with co- ...BEKK model augmented by exogenous variables (BEKK-X). In their ...
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Efficient pricing of barrier options with the variance gamma model
... Montréal, H3C 3J7, CANADA ABSTRACT We develop an efficient Monte Carlo algorithm for pricing barrier options with the variance gamma model (Madan, Carr, and Chang 1998). After gener- alizing the double-gamma ...
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Linear mixed model with fixed effects in the residual variance
... linear model approach offers new possibilities to fit generalized linear mod- els with random ...a model for the residual variance, fits mod- els where the random effect distribution is not neces- ...
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