scholarly journals On the Role of the Estimation Error in Prediction of Expected Shortfall

2012 ◽  
Author(s):  
Carl Lönnbark
Keyword(s):  
Estimation Error ◽  
Expected Shortfall

scholarly journals Contour Map of Estimation Error for Expected Shortfall

2015 ◽  
Author(s):  
Imre Kondor ◽  
Fabio Caccioli ◽  
Gabor Papp ◽  
Matteo Marsili
Keyword(s):  
Estimation Error ◽  
Expected Shortfall ◽  
Contour Map

scholarly journals The Quality of Accruals and Earnings: The Role of Accrual Estimation Errors

2002 ◽  
Vol 77 (s-1) ◽  
pp. 35-59 ◽  
Author(s):  
Patricia M. Dechow ◽  
Ilia D. Dichev
Keyword(s):  
Estimation Error ◽  
Cash Flows ◽  
Empirical Measure ◽  
Earnings Persistence ◽  
Working Capital ◽  
Estimation Errors ◽  
Accrual Quality ◽  
Flows Over Time

This paper suggests a new measure of one aspect of the quality of working capital accruals and earnings. One role of accruals is to shift or adjust the recognition of cash flows over time so that the adjusted numbers (earnings) better measure firm performance. However, accruals require assumptions and estimates of future cash flows. We argue that the quality of accruals and earnings is decreasing in the magnitude of estimation error in accruals. We derive an empirical measure of accrual quality as the residuals from firm-specific regressions of changes in working capital on past, present, and future operating cash flows. We document that observable firm characteristics can be used as instruments for accrual quality (e.g., volatility of accruals and volatility of earnings). Finally, we show that our measure of accrual quality is positively related to earnings persistence.

scholarly journals The Role of Buckling in the Estimation of Compressive Strength of Corrugated Cardboard Boxes

Materials ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 4578 ◽  
Author(s):  
Tomasz Garbowski ◽  
Tomasz Gajewski ◽  
Jakub Krzysztof Grabski
Keyword(s):  
Compressive Strength ◽  
Analytical Approach ◽  
Estimation Error ◽  
Full Range ◽  
Shear Stiffness ◽  
Width Ratio ◽  
Force Estimation ◽  
Corrugated Cardboard ◽  
Precise Prediction

This paper presents analytical methods for estimating the static top-to-bottom compressive strength of simple corrugated packaging, in which the torsional and shear stiffness of corrugated cardboard as well as the panel depth-to-width ratio are included. The methods are compared herein with a basic and more detailed buckling description with the successful McKee formula, which is over fifty years old but still widely used among packaging designers and quality control departments. Additionally, the assumptions and applied simplifications used in the literature are analyzed, and the limits of applicability of different versions of the selected methods are checked. Finally, all approaches are verified with the experiment results of various packaging designs made of corrugated cardboard. The results show that, for certain proportions of dimensions of simple flap boxes, simplified methods give an even two times larger estimation error than the analytical approach proposed in the paper. Furthermore, it is evidenced that including all flexural, torsional and shear stiffnesses in the buckling force estimation gives a very precise prediction of the box compressive strength for the full range of package dimensions.

scholarly journals The Role of the Centre of Projection in the Estimation of Slant from Texture of Planar Surfaces

Perception ◽  
10.1068/p3002 ◽  
2001 ◽  
Vol 30 (2) ◽  
pp. 185-193 ◽  
Author(s):  
Stefaan Tibau ◽  
Bert Willems ◽  
Erik Van Den Bergh ◽  
Johan Wagemans
Keyword(s):  
Estimation Error ◽  
Stimulus Display ◽  
Perspective Projection ◽  
Viewing Distance ◽  
Circular Aperture ◽  
Focal Distance ◽  
Square Grid ◽  
Specific Combination ◽  
Planar Surfaces

Displays were presented consisting of a perspective projection of a regular square grid, made up of vertical and horizontal equally spaced white lines, that was slanted in depth. The surface was viewed monocularly, through a circular aperture. A range of slants was shown (0°, 10°, 20°, 30°, 40°, 50°, or 60°) and the observers' task was to match the slant by means of a mouse-driven probe. The viewing distance (50, 75, or 100 cm) as well as the focal distance (25, 50, 75, 100, or 125 cm) were varied. We expected the estimation error to be smallest when the viewing distance and the focal distance coincided. This was not the case. Instead, subjects seemed to use the perspective deformation of the texture elements in the stimulus display to make a slant estimation, regardless of the specific combination of viewing distance and focal distance.

scholarly journals Portfolio optimization under Expected Shortfall: contour maps of estimation error

2017 ◽  
Vol 18 (8) ◽  
pp. 1295-1313 ◽  
Author(s):  
Fabio Caccioli ◽  
Imre Kondor ◽  
Gábor Papp
Keyword(s):  
Portfolio Optimization ◽  
Estimation Error ◽  
Expected Shortfall ◽  
Contour Maps

scholarly journals Optimizing Expected Shortfall under an ℓ1 Constraint—An Analytic Approach

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 523
Author(s):  
Gábor Papp ◽  
Imre Kondor ◽  
Fabio Caccioli
Keyword(s):  
Statistical Physics ◽  
Risk Measure ◽  
Estimation Error ◽  
Random Systems ◽  
Expected Shortfall ◽  
Critical Ratio ◽  
Out Of Sample ◽  
High Volatility ◽  
High Quantile ◽  
Average Loss

Expected Shortfall (ES), the average loss above a high quantile, is the current financial regulatory market risk measure. Its estimation and optimization are highly unstable against sample fluctuations and become impossible above a critical ratio r=N/T, where N is the number of different assets in the portfolio, and T is the length of the available time series. The critical ratio depends on the confidence level α, which means we have a line of critical points on the α−r plane. The large fluctuations in the estimation of ES can be attenuated by the application of regularizers. In this paper, we calculate ES analytically under an ℓ1 regularizer by the method of replicas borrowed from the statistical physics of random systems. The ban on short selling, i.e., a constraint rendering all the portfolio weights non-negative, is a special case of an asymmetric ℓ1 regularizer. Results are presented for the out-of-sample and the in-sample estimator of the regularized ES, the estimation error, the distribution of the optimal portfolio weights, and the density of the assets eliminated from the portfolio by the regularizer. It is shown that the no-short constraint acts as a high volatility cutoff, in the sense that it sets the weights of the high volatility elements to zero with higher probability than those of the low volatility items. This cutoff renormalizes the aspect ratio r=N/T, thereby extending the range of the feasibility of optimization. We find that there is a nontrivial mapping between the regularized and unregularized problems, corresponding to a renormalization of the order parameters.

scholarly journals Correcting the Underestimation of Capital Incomes in Inequality Indicators: with an Application to the UK, 1997–2016

2021 ◽  
Author(s):  
Tahnee Christelle Ooms
Keyword(s):  
Estimation Error ◽  
Household Survey ◽  
Capital Income ◽  
Policy Makers ◽  
The Family ◽  
The Rich ◽  
Adjusted Indicators ◽  
The Uk ◽  
Personal Incomes

AbstractThis paper proposes a methodological framework to better incorporate non-labour income into existing top adjusted indicators of economic inequality. Surveys are known to miss the rich, receiving disproportionate amounts of capital income. There has been a surge in top harmonisation methodologies, which complement survey-based estimates of inequality with information from the rich reported in tax administrative sources. These harmonisation methods are found to have a significant upward effect on inequality indicators. This analysis uses the Family Resources Survey (household survey) and the Survey of Personal Incomes (tax data) to explore the extent to which existing UK harmonisation methodology corrects for capital income. First, this analysis finds that the FRS has experienced a significant decline in capital income measurement over the past 20 years (1997–2016), taking reported levels of capital income in the SPI as benchmark. Second, the top harmonisation methodology is found to only partially correct for this decline. Third, in response, the paper proposes a multi-step capital income correction to allocate the remaining capital income missing from top adjusted inequality indicators. The adjustment accounts for both under-coverage and under-estimation error of capital income across the income distribution. Poor measurement of capital incomes in household surveys has long been acknowledged but attempts to correct for this have remained few. This paper highlights the need for decomposable top adjusted indicators of inequality to give a better picture of the role of capital incomes in driving inequality. Surveys are traditionally used to produce inequality indicators used by governments, statistical offices and policy makers. The policy implication is that income missing from indicators structurally falls out of inequality debates, which has arguably been the case for capital incomes.

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