scholarly journals A simple method for amateur investors to analyze covered call options

2020 ◽  
Vol 8 (1) ◽  
pp. 227-235
Author(s):  
Greg Samsa
Keyword(s):  
Gaussian Distribution ◽  
Simple Method ◽  
Call Options ◽  
Market Participants ◽  
Margin Of Safety ◽  
Covered Call ◽  
Truncated Gaussian ◽  
Basic Version

We describe a simple method which amateur investors can use to analyze covered calls.  The most basic version is based on the formula for the expectation of a truncated Gaussian distribution, and it can be generalized to accommodate other assumptions.  This approach might be especially considered during a time of market overvaluation, such as the present.  During such times, investors should shift their preferences toward writing deep-in-the-money covered calls, which provide a greater margin of safety while monetizing the (probably optimistic) expectations of other market participants regarding future returns.

EFFECT OF THE STATISTICAL VOLATILITY IN THE WRITING OF COVERED CALL OPTIONS STRATEGY

2013 ◽  
Vol 13 (4) ◽  
pp. 5-16
Author(s):  
P�mella Teixeira ◽  
Tabajara Junior ◽  
Rossimar Oliveira ◽  
Fabiano Lima
Keyword(s):  
Call Options ◽  
Covered Call

scholarly journals Positively skewed data: revisiting the box-cox power transformation.

2010 ◽  
Vol 3 (1) ◽  
pp. 68-77 ◽  
Author(s):  
Jake Olivier ◽  
Melissa M. Norberg
Keyword(s):  
Gaussian Distribution ◽  
Reaction Times ◽  
Power Transformation ◽  
Skewed Data ◽  
Statistical Methodology ◽  
Simple Method ◽  
Normal Distributions ◽  
Normal Probability ◽  
Power Transformations ◽  
Normal Probability Distribution

Although the normal probability distribution is the cornerstone of applying statistical methodology; data do not always meet the necessary normal distribution assumptions. In these cases, researchers often transform non-normal data to a distribution that is approximately normal. Power transformations constitute a family of transformations, which include logarithmic and fractional exponent transforms. The Box-Cox method offers a simple method for choosing the most appropriate power transformation. Another option for data that is positively skewed, often used when measuring reaction times, is the Ex-Gaussian distribution which is a combination of the exponential and normal distributions. In this paper, the Box-Cox power transformation and Ex-Gaussian distribution will be discussed and compared in the context of positively skewed data. This discussion will demonstrate that the Box-Cox power transformation is simpler to apply and easier to interpret than the Ex-Gaussian distribution.

The impact of market drops is different for investors before and after retirement

2020 ◽  
Vol 8 (6) ◽  
pp. 196-201
Author(s):  
Greg Samsa
Keyword(s):  
Expected Returns ◽  
Call Options ◽  
Market Return ◽  
Before And After ◽  
Accumulation Phase ◽  
Covered Call ◽  
The Impact ◽  
Financial Press

As applied to investing for and during retirement, the popular financial press has promulgated two memes about the impact of market drops: (1) for those investing for retirement market drops aren’t problematic; and (2) for those in retirement market drops are.    We use simulation to illustrate the logic behind these memes, to demonstrate that they are mostly but not entirely true, and finally to restate them more precisely.  Although sequence of returns risk is not present during the accumulation phase as an investor plans for retirement, it can have a significant (and perhaps underestimated) impact during retirement.  This, however, can place the retiree in a predicament – namely, settle for lower returns and lower distributions during retirement or gamble on stocks.  However, it does not necessarily imply that retirees must abandon the expected returns associated with stocks, because of the ability to write deep-in-the-money covered call options, which harvest the expected market return (but no more than this) with limited variability.

scholarly journals GEOMETRY AND STATISTICS OF COSMIC MICROWAVE POLARIZATION

1999 ◽  
Vol 08 (02) ◽  
pp. 189-212 ◽  
Author(s):  
A. D. DOLGOV ◽  
A. G. DOROSHKEVICH ◽  
D. I. NOVIKOV ◽  
I. D. NOVIKOV
Keyword(s):  
Vector Field ◽  
Gaussian Distribution ◽  
Statistical Properties ◽  
Statistical Distribution ◽  
Singular Points ◽  
Simple Method ◽  
Minkowski Functionals ◽  
Cmb Polarization ◽  
Tensor Perturbations

Geometrical and statistical properties of polarization of CMB are analyzed. Singular points of the vector field which describes CMB polarization are found and classified. Statistical distribution of the singularities is studied. A possible signature of tensor perturbations in CMB polarization is discussed. For a further analysis of CMB statistics, Minkowski functionals are used, which present a technically simple method to search for deviations from a Gaussian distribution.

Effects of Size, Orientation, and Form Tolerances on the Frequency Distributions of Clearance Between Two Planar Faces

2011 ◽  
Vol 11 (1) ◽  
Author(s):  
Gaurav Ameta ◽  
Joseph K. Davidson ◽  
Jami J. Shah
Keyword(s):  
Mathematical Model ◽  
Uniform Distribution ◽  
Frequency Distribution ◽  
Gaussian Distribution ◽  
Planar Surface ◽  
Tolerance Zone ◽  
Frequency Distributions ◽  
Geometric Tolerances ◽  
Paper Form ◽  
Truncated Gaussian

A new mathematical model for representing the geometric variations of a planar surface is extended to include probabilistic representations for a 1D dimension of interest, which can be determined from multidimensional variations of the planar surface on a part. The model is compatible with the ASME/ANSI/ISO Standards for geometric tolerances. Central to the new model is a Tolerance-Map® (T-Map®) (Patent No. 6963824), a hypothetical volume of points that models the 3D variations in location and orientation of a feature, which can arise from tolerances on size, position, orientation, and form. The 3D variations of a planar surface are decomposed into manufacturing bias, i.e., toward certain regions of a Tolerance-Map, and into geometric bias that can be computed from the geometry of T-Maps. The geometric bias arises from the shape of the feature, the tolerance-zone, and the control used on the mating envelope. Influence of manufacturing bias on the frequency distribution of 1D dimension of interest is demonstrated with two examples: the multidimensional truncated Gaussian distribution and the uniform distribution. In this paper, form and orientation variations are incorporated as subsets in order to model the coupling between size and form variations, as permitted by the ASME Standard when the amounts of these variations differ. Two distributions for flatness, i.e., the uniform distribution and the Gaussian distribution that has been truncated symmetrically to six standard deviations, are used as examples to illustrate the influence of form on the dimension of interest. The influence of orientation (parallelism and perpendicularity) refinement on the frequency distribution for the dimension of interest is demonstrated. Although rectangular faces are utilized in this paper to illustrate the method, the same techniques may be applied to any convex plane-segment that serves as a target face.

Writing Covered Call Options

1980 ◽  
Vol 7 (1) ◽  
pp. 74-79 ◽  
Author(s):  
James W. Yates ◽  
Robert W. Kopprasch
Keyword(s):  
Call Options ◽  
Covered Call

Structure images of Si, Ge and Mos2 crystals and some application to radiation damage studies

1978 ◽  
Vol 36 (1) ◽  
pp. 292-293 ◽  
Author(s):  
K. Izui ◽  
T. Nishida ◽  
S. Furuno ◽  
H. Otsu ◽  
S. Kuwabara
Keyword(s):  
Radiation Damage ◽  
Exposure Time ◽  
Gaussian Distribution ◽  
Intensity Distribution ◽  
Chromatic Aberration ◽  
Magnetic Lens

Recently we have observed the structure images of silicon in the (110), (111) and (100) projection respectively, and then examined the optimum defocus and thickness ranges for the formation of such images on the basis of calculations of image contrasts using the n-slice theory. The present paper reports the effects of a chromatic aberration and a slight misorientation on the images, and also presents some applications of structure images of Si, Ge and MoS2 to the radiation damage studies.(1) Effect of a chromatic aberration and slight misorientation: There is an inevitable fluctuation in the amount of defocus due to a chromatic aberration originating from the fluctuations both in the energies of electrons and in the magnetic lens current. The actual image is a results of superposition of those fluctuated images during the exposure time. Assuming the Gaussian distribution for defocus, Δf around the optimum defocus value Δf0, the intensity distribution, I(x,y) in the image formed by this fluctuation is given by

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