scholarly journals “Go Wild for a While!”: A New Test for Forecast Evaluation in Nested Models

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2254
Author(s):  
Pablo Pincheira ◽  
Nicolás Hardy ◽  
Felipe Muñoz
Keyword(s):  
Predictive Ability ◽  
Random Variable ◽  
Simple Modification ◽  
Nested Models ◽  
Asymptotically Normal ◽  
Estimation Uncertainty ◽  
Normal Test ◽  
Out Of Sample ◽  
Size Distortions ◽  
The Impact

In this paper, we present a new asymptotically normal test for out-of-sample evaluation in nested models. Our approach is a simple modification of a traditional encompassing test that is commonly known as Clark and West test (CW). The key point of our strategy is to introduce an independent random variable that prevents the traditional CW test from becoming degenerate under the null hypothesis of equal predictive ability. Using the approach developed by West (1996), we show that in our test, the impact of parameter estimation uncertainty vanishes asymptotically. Using a variety of Monte Carlo simulations in iterated multi-step-ahead forecasts, we evaluated our test and CW in terms of size and power. These simulations reveal that our approach is reasonably well-sized, even at long horizons when CW may present severe size distortions. In terms of power, results were mixed but CW has an edge over our approach. Finally, we illustrate the use of our test with an empirical application in the context of the commodity currencies literature.

scholarly journals Can Machine Learning-Based Portfolios Outperform Traditional Risk-Based Portfolios? The Need to Account for Covariance Misspecification

Risks ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 74 ◽  
Author(s):  
Prayut Jain ◽  
Shashi Jain
Keyword(s):  
Machine Learning ◽  
Predictive Ability ◽  
Forecast Model ◽  
Minimum Variance ◽  
Portfolio Performance ◽  
Risk Parity ◽  
Out Of Sample ◽  
Weighted Portfolio ◽  
The Impact ◽  
Allocation Methods

The Hierarchical risk parity (HRP) approach of portfolio allocation, introduced by Lopez de Prado (2016), applies graph theory and machine learning to build a diversified portfolio. Like the traditional risk-based allocation methods, HRP is also a function of the estimate of the covariance matrix, however, it does not require its invertibility. In this paper, we first study the impact of covariance misspecification on the performance of the different allocation methods. Next, we study under an appropriate covariance forecast model whether the machine learning based HRP outperforms the traditional risk-based portfolios. For our analysis, we use the test for superior predictive ability on out-of-sample portfolio performance, to determine whether the observed excess performance is significant or if it occurred by chance. We find that when the covariance estimates are crude, inverse volatility weighted portfolios are more robust, followed by the machine learning-based portfolios. Minimum variance and maximum diversification are most sensitive to covariance misspecification. HRP follows the middle ground; it is less sensitive to covariance misspecification when compared with minimum variance or maximum diversification portfolio, while it is not as robust as the inverse volatility weighed portfolio. We also study the impact of the different rebalancing horizon and how the portfolios compare against a market-capitalization weighted portfolio.

scholarly journals The impact of inclusive business on ethical values & internal control quality: an accounting perspective

2016 ◽  
Vol 5 (4) ◽  
pp. 106-113 ◽  
Author(s):  
Tamer El Nashar
Keyword(s):  
Probability Distribution ◽  
Internal Control ◽  
Random Variable ◽  
Ethical Values ◽  
Discrete Probability ◽  
Control Quality ◽  
Inclusive Business ◽  
Potential Impact ◽  
Business Approach ◽  
The Impact

The objective of this paper is to examine the impact of inclusive business on the internal ethical values and the internal control quality while conceiving the accounting perspective. I construct the hypothesis for this paper based on the potential impact on the organizations’ awareness to be directed to the inclusive business approach that will significantly impact the culture of the organizations then the ethical values and the internal control quality. I use the approach of the expected value and variance of random variable test in order to analyze the potential impact of inclusive business. I support the examination by discrete probability distribution and continuous probability distribution. I find a probability of 85.5% to have a significant potential impact of the inclusive business by 100% score on internal ethical values and internal control quality. And to help contribute to sustainability growth, reduce poverty and improve organizational culture and learning.

The Compatibility Assessment between Voltage Sags and Equipment Tolerance Based on Fuzzy-Random Method

2012 ◽  
Vol 588-589 ◽  
pp. 458-462
Author(s):  
Zhi Jian Yuan ◽  
Yan Li
Keyword(s):  
Engineering Application ◽  
Fuzzy Random Variable ◽  
Random Variable ◽  
Assessment Model ◽  
Voltage Sags ◽  
Equipment Failure ◽  
Failure Event ◽  
Cut Set ◽  
The Impact ◽  
Fuzzy Random

The impact of voltage sags on equipment is usually described by equipment failure probability.It is generally difficult to assess and predict the probability because of the uncertainty of both the nature of voltage sags and the VTL (VTL) of equipment. By defining the equipment failure event caused by voltage sags as a fuzzy-random event, a fuzzy-random assessment model incorporating those uncertainty is developed. The model is able to convert the probability problem of a fuzzy-random variable to that of a common random variable by using λ-cut set. It is thus valuable in theoretical analysis and engineering application. The validity of the developed model is verified by Monte Carlo stochastic simulation using personal computers (PCs)as test equipment.

scholarly journals Business Distress Prediction Using Bayesian Logistic Model for Indian Firms

Risks ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 113 ◽  
Author(s):  
Arvind Shrivastava ◽  
Kuldeep Kumar ◽  
Nitin Kumar
Keyword(s):  
Bayesian Modeling ◽  
Logistic Model ◽  
Predictive Ability ◽  
Emerging Economy ◽  
Predictive Capability ◽  
Corporate Sector ◽  
Distress Prediction ◽  
Out Of Sample ◽  
Distressed Firms ◽  
Corporate Distress

The objective of the study is to perform corporate distress prediction for an emerging economy, such as India, where bankruptcy details of firms are not available. Exhaustive panel dataset extracted from Capital IQ has been employed for the purpose. Foremost, the study contributes by devising novel framework to capture incipient signs of distress for Indian firms by employing a combination of firm specific parameters. The strategy not only enables enlarging the sample of distressed firms but also enables to obtain robust results. The analysis applies both standard Logistic and Bayesian modeling to predict distressed firms in Indian corporate sector. Thereby, a comparison of predictive ability of the two approaches has been carried out. Both in-sample and out of sample evaluation reveal a consistently better predictive capability employing Bayesian methodology. The study provides useful structure to indicate the early signals of failure in Indian corporate sector that is otherwise limited in literature.

scholarly journals 1 Assessing the predictive adequacy of simple and complex mathematical models

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 24-24
Author(s):  
Luis O Tedeschi
Keyword(s):  
Model Development ◽  
Predictive Ability ◽  
Random Variable ◽  
Coefficient Of Determination ◽  
Skewed Data ◽  
Future Performance ◽  
Evaluation Phase ◽  
The Difference ◽  
Predicted Values ◽  
Analytical Approaches

Abstract The establishment of credibility for a mathematical model’s (MM) predictive ability is an essential component for improving the MM because it stimulates the evolutionary thinking (i.e., the next generation of the model) of mental conceptualizations, assumptions, and boundaries of the MM. Its predictive adequacy is commonly assessed through its ability to precisely or accurately predict observed (real) values. The precision component measures how closely the model predicted values are of each other or whether a defined pattern of predictions exists. The accuracy component, on the other hand, measures how closely the average of the model predicted values are to the actual (true) average. Many statistics exist to determine precision and accuracy of MM such as mean bias, resistant coefficient of determination, coefficient of determination, modeling efficiency, concordance correlation coefficient (CCC), the mean square error of prediction, Kleijnen’s statistic (regression of the difference between predicted and observed on their sum), and Altman and Bland’s limits of agreement statistics among many more. However, for complex models that use skewed data or repeated data in which the data is not independent (e.g., multiple measurements on the same subject), simple statistics may not suffice. For instance, four methods to compute CCC exist (moment, variance components, U-statistics, and generalized estimating equations—GEE), but only the last two methods are resilient to lightly skewed data. Another type of complexity arises when meta-analytical approaches are used at the model development phase or the model evaluation phase. In general, meta-analytical approaches remove errors (i.e., variation) associated with random variables that are believed to be known. Under these circumstances, MM tends to overperform (i.e., they have greater predictive adequacy) and their future performance may be deceitful when trying to forecast at scenarios in which the random variable(s) is(are) indeterminable or unquantifiable.

scholarly journals EXPLOITING INFINITE VARIANCE THROUGH DUMMY VARIABLES IN NONSTATIONARY AUTOREGRESSIONS

2013 ◽  
Vol 29 (6) ◽  
pp. 1162-1195 ◽  
Author(s):  
Giuseppe Cavaliere ◽  
Iliyan Georgiev
Keyword(s):  
Unit Root ◽  
Asymptotic Properties ◽  
Ordinary Least Squares ◽  
Infinite Variance ◽  
Local Power ◽  
Test Statistic ◽  
Asymptotically Normal ◽  
Normal Test ◽  
Order Of Magnitude ◽  
Near Unit Root

We consider estimation and testing in finite-order autoregressive models with a (near) unit root and infinite-variance innovations. We study the asymptotic properties of estimators obtained by dummying out “large” innovations, i.e., those exceeding a given threshold. These estimators reflect the common practice of dealing with large residuals by including impulse dummies in the estimated regression. Iterative versions of the dummy-variable estimator are also discussed. We provide conditions on the preliminary parameter estimator and on the threshold that ensure that (i) the dummy-based estimator is consistent at higher rates than the ordinary least squares estimator, (ii) an asymptotically normal test statistic for the unit root hypothesis can be derived, and (iii) order of magnitude gains of local power are obtained.

scholarly journals Applying the Metafounders Approach for Genomic Evaluation in a Multibreed Beef Cattle Population

2020 ◽  
Vol 11 ◽  
Author(s):  
Vinícius Silva Junqueira ◽  
Paulo Sávio Lopes ◽  
Daniela Lourenco ◽  
Fabyano Fonseca e Silva ◽  
Fernando Flores Cardoso
Keyword(s):  
Beef Cattle ◽  
Variance Components ◽  
Genetic Relationships ◽  
A Priori ◽  
Predictive Ability ◽  
Single Step ◽  
Pedigree Information ◽  
Phenotypic Variance ◽  
Genomic Information ◽  
The Impact

Pedigree information is incomplete by nature and commonly not well-established because many of the genetic ties are not known a priori or can be wrong. The genomic era brought new opportunities to assess relationships between individuals. However, when pedigree and genomic information are used simultaneously, which is the case of single-step genomic BLUP (ssGBLUP), defining the genetic base is still a challenge. One alternative to overcome this challenge is to use metafounders, which are pseudo-individuals that describe the genetic relationship between the base population individuals. The purpose of this study was to evaluate the impact of metafounders on the estimation of breeding values for tick resistance under ssGBLUP for a multibreed population composed by Hereford, Braford, and Zebu animals. Three different scenarios were studied: pedigree-based model (BLUP), ssGBLUP, and ssGBLUP with metafounders (ssGBLUPm). In ssGBLUPm, a total of four different metafounders based on breed of origin (i.e., Hereford, Braford, Zebu, and unknown) were included for the animals with missing parents. The relationship coefficient between metafounders was in average 0.54 (ranging from 0.34 to 0.96) suggesting an overlap between ancestor populations. The estimates of metafounder relationships indicate that Hereford and Zebu breeds have a possible common ancestral relationship. Inbreeding coefficients calculated following the metafounder approach had less negative values, suggesting that ancestral populations were large enough and that gametes inherited from the historical population were not identical. Variance components were estimated based on ssGBLUPm, ssGBLUP, and BLUP, but the values from ssGBLUPm were scaled to provide a fair comparison with estimates from the other two models. In general, additive, residual, and phenotypic variance components in the Hereford population were smaller than in Braford across different models. The addition of genomic information increased heritability for Hereford, possibly because of improved genetic relationships. As expected, genomic models had greater predictive ability, with an additional gain for ssGBLUPm over ssGBLUP. The increase in predictive ability was greater for Herefords. Our results show the potential of using metafounders to increase accuracy of GEBV, and therefore, the rate of genetic gain in beef cattle populations with partial levels of missing pedigree information.

scholarly journals Establishment of a Financial Crisis Early Warning System for Domestic Listed Companies Based on Three Decision Tree Models

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Gang Wang ◽  
Keming Wang ◽  
Yingying Zhou ◽  
Xiaoyan Mo ◽  
Weilin Xiao
Keyword(s):  
Financial Crisis ◽  
Decision Tree ◽  
Early Warning ◽  
Financial Management ◽  
Early Warning System ◽  
Comprehensive Evaluation ◽  
Warning System ◽  
Predictive Ability ◽  
Tree Models ◽  
The Impact

The financial crisis is a realistic problem that the general enterprise must encounter in the process of financial management. Due to the impact of the COVID-19 and the Sino-US trade war, domestic companies with unsound financial conditions are at risk of shutdowns and bankruptcies. Therefore, it is urgently needed to study the financial warning of enterprises. In this study, three decision tree models are used to establish the financial crisis early warning system. These three decision tree models include C50, CART, and random forest decision trees. In addition, the ROC curve was used for comprehensive evaluation of the accuracy analysis of the model to confirm the predictive ability of each model. This result can provide reference for domestic financial departments and provide financial management basis for the investing public.

scholarly journals A simple modification of dialysate potassium: its impact on plasma potassium concentrations and the electrocardiogram

2019 ◽  
Author(s):  
Pierre Delanaye ◽  
François Krzesinski ◽  
Bernard E Dubois ◽  
Alexandre Delcour ◽  
Sébastien Robinet ◽  
...  
Keyword(s):  
Sudden Death ◽  
Qt Interval ◽  
Lower Risk ◽  
Plasma Potassium ◽  
Qt Dispersion ◽  
Dialysis Session ◽  
Simple Modification ◽  
Before And After ◽  
Electrocardiogram Ecg ◽  
The Impact

Abstract Background Sudden death is frequent in haemodialysis (HD) patients. Both hyperkalaemia and change of plasma potassium (K) concentrations induced by HD could explain this. The impact of increasing dialysate K by 1 mEq/L on plasma K concentrations and electrocardiogram (ECG) results before and after HD sessions was studied. Methods Patients with pre-dialysis K >5.5 mEq/L were excluded. ECG and K measurements were obtained before and after the first session of the week for 2 weeks. Then, K in the dialysate was increased (from 1 or 3 to 2 or 4 mEq/L, respectively). Blood and ECG measurements were repeated after 2 weeks of this change. Results Twenty-seven prevalent HD patients were included. As expected, a significant decrease in K concentrations was observed after the dialysis session, but this decrease was significantly lower after the switch to an increased dialysate K. The pre-dialysis K concentrations were not different after changing, but post-dialysis K concentrations were higher after switching (P < 0.0001), with a lower incidence of post-dialysis hypokalaemia. Regarding ECG, before switching, the QT interval (QT) dispersion increased during the session, whereas no difference was observed after switching. One week after switching, post-dialysis QT dispersion [38 (34–42) ms] was lower than post-dialysis QT dispersion 2 weeks and 1 week before switching [42 (38–57) ms, P = 0.0004; and 40 (35–50) ms, P = 0.0002]. Conclusions A simple increase of 1 mEq/L of K in the dialysate is associated with a lower risk of hypokalaemia and a lower QT dispersion after the dialysis session. Further study is needed to determine if such a strategy is associated with a lower risk of sudden death.

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