Is optimum always optimal? A revisit of the mean‐variance method under nonlinear measures of dependence and non‐normal liquidity constraints

2020 ◽  
Author(s):  
Mazin A.M. Al Janabi
Keyword(s):  
Liquidity Constraints ◽  
Variance Method ◽  
Measures Of Dependence ◽  
The Mean ◽  
Mean Variance

Are Fund Investors Smart Enough to Know About the Mean-Variance Relation?

2012 ◽  
Author(s):  
Fabian Irek ◽  
Thorsten Lehnert
Keyword(s):  
The Mean ◽  
Mean Variance

Comparison Between the Mean-Variance Optimal and the Mean-Quadratic-Variation Optimal Trading Strategies

2013 ◽  
Vol 20 (5) ◽  
pp. 415-449 ◽  
Author(s):  
S. T. Tse ◽  
P. A. Forsyth ◽  
J. S. Kennedy ◽  
H. Windcliff
Keyword(s):  
Quadratic Variation ◽  
Trading Strategies ◽  
Optimal Trading ◽  
The Mean ◽  
Mean Variance

The Use of the Pseudo-Voigt Function in the Variance Method of X-ray Line-Broadening Analysis

1997 ◽  
Vol 30 (4) ◽  
pp. 427-430 ◽  
Author(s):  
F. Sánchez-Bajo ◽  
F. L. Cumbrera
Keyword(s):  
Crystallite Size ◽  
Original Form ◽  
Line Broadening ◽  
X Ray Diffraction ◽  
Stabilized Zirconia ◽  
X Ray ◽  
Variance Method ◽  
The Mean ◽  
Voigt Function

A modified application of the variance method, using the pseudo-Voigt function as a good approximation to the X-ray diffraction profiles, is proposed in order to obtain microstructural quantities such as the mean crystallite size and root-mean-square (r.m.s.) strain. Whereas the variance method in its original form is applicable only to well separated reflections, this technique can be employed in the cases where there is line-profile overlap. Determination of the mean crystallite size and r.m.s. strain for several crystallographic directions in a nanocrystalline cubic sample of 9-YSZ (yttria-stabilized zirconia) has been performed by means of this procedure.

Reproduction of the Mean, Variance, and Variogram Model in Spectral Simulation

2004 ◽  
Vol 36 (4) ◽  
pp. 487-506 ◽  
Author(s):  
Tingting Yao
Keyword(s):  
Spectral Simulation ◽  
Variogram Model ◽  
The Mean ◽  
Mean Variance

scholarly journals Analysis of stimulus-related activity in rat auditory cortex using complex spectral coefficients

2013 ◽  
Vol 110 (3) ◽  
pp. 621-639 ◽  
Author(s):  
Bryan M. Krause ◽  
Matthew I. Banks
Keyword(s):  
Low Frequency ◽  
Phase Changes ◽  
Induced Response ◽  
Top Down ◽  
Time Frequency ◽  
State Dependent ◽  
Spectral Coefficients ◽  
The Mean ◽  
Sensory Responses ◽  
Mean Variance

The neural mechanisms of sensory responses recorded from the scalp or cortical surface remain controversial. Evoked vs. induced response components (i.e., changes in mean vs. variance) are associated with bottom-up vs. top-down processing, but trial-by-trial response variability can confound this interpretation. Phase reset of ongoing oscillations has also been postulated to contribute to sensory responses. In this article, we present evidence that responses under passive listening conditions are dominated by variable evoked response components. We measured the mean, variance, and phase of complex time-frequency coefficients of epidurally recorded responses to acoustic stimuli in rats. During the stimulus, changes in mean, variance, and phase tended to co-occur. After the stimulus, there was a small, low-frequency offset response in the mean and modest, prolonged desynchronization in the alpha band. Simulations showed that trial-by-trial variability in the mean can account for most of the variance and phase changes observed during the stimulus. This variability was state dependent, with smallest variability during periods of greatest arousal. Our data suggest that cortical responses to auditory stimuli reflect variable inputs to the cortical network. These analyses suggest that caution should be exercised when interpreting variance and phase changes in terms of top-down cortical processing.

The Mean-Variance Rule (M-V Rule)

2012 ◽  
pp. 63-116
Author(s):  
Haim Levy
Keyword(s):  
The Mean ◽  
Mean Variance
Sign in / Sign up

Export Citation Format

Share Document