A New Approach to Backtesting and Risk Model Selection

2018 ◽  
Author(s):  
Jacopo Corbetta ◽  
Ilaria Peri
Keyword(s):  
Model Selection ◽  
Risk Model ◽  
New Approach

scholarly journals Detecting multiple generalized change-points by isolating single ones

Metrika ◽  
2021 ◽  
Author(s):  
Andreas Anastasiou ◽  
Piotr Fryzlewicz
Keyword(s):  
Model Selection ◽  
Linear Trend ◽  
Information Criterion ◽  
Change Points ◽  
Isolation Technique ◽  
New Approach ◽  
Piecewise Constant ◽  
Practical Performance ◽  
R Packages ◽  
The Times

AbstractWe introduce a new approach, called Isolate-Detect (ID), for the consistent estimation of the number and location of multiple generalized change-points in noisy data sequences. Examples of signal changes that ID can deal with are changes in the mean of a piecewise-constant signal and changes, continuous or not, in the linear trend. The number of change-points can increase with the sample size. Our method is based on an isolation technique, which prevents the consideration of intervals that contain more than one change-point. This isolation enhances ID’s accuracy as it allows for detection in the presence of frequent changes of possibly small magnitudes. In ID, model selection is carried out via thresholding, or an information criterion, or SDLL, or a hybrid involving the former two. The hybrid model selection leads to a general method with very good practical performance and minimal parameter choice. In the scenarios tested, ID is at least as accurate as the state-of-the-art methods; most of the times it outperforms them. ID is implemented in the R packages IDetect and breakfast, available from CRAN.

Model and Moment Selection in Factor Copula Models*

2019 ◽  
Author(s):  
Fang Duan ◽  
Hans Manner ◽  
Dominik Wied
Keyword(s):  
Model Selection ◽  
Value At Risk ◽  
Selection Procedure ◽  
Copula Models ◽  
New Approach ◽  
Moment Selection ◽  
Finite Samples ◽  
Generalized Methods Of Moments ◽  
Methods Of Moments ◽  
The Impact

Abstract This article develops a simultaneous model and moment selection procedure for factor copula models. Since the density of the factor copula is generally not known in closed form, widely used likelihood or moment-based model selection criteria cannot be directly applied on factor copulas. The new approach is inspired by the methods for generalized methods of moments proposed by Andrews (1999) and Andrews and Lu (2001). The consistency of the procedure is proved and Monte Carlo simulations show its good performance in finite samples in different scenarios of sample sizes and dimensions. The impact of the choice of moments in selected regions of the support on model selection and value-at-risk prediction is further examined by simulation and an application to a portfolio consisting of ten stocks in the Deutscher Aktienindex (DAX30) index.

A temporal approach to the Parisian risk model

2018 ◽  
Vol 55 (1) ◽  
pp. 302-317 ◽  
Author(s):  
Bin Li ◽  
Gordon E. Willmot ◽  
Jeff T. Y. Wong
Keyword(s):  
Risk Model ◽  
Levy Processes ◽  
New Approach ◽  
Approximation Approach ◽  
Parisian Ruin ◽  
Spatial Approximation ◽  
Underlying Processes ◽  
Spectrally Negative Lévy Processes ◽  
Ruin Problem ◽  
Observation Scheme

Abstract In this paper we propose a new approach to study the Parisian ruin problem for spectrally negative Lévy processes. Since our approach is based on a hybrid observation scheme switching between discrete and continuous observations, we call it a temporal approach as opposed to the spatial approximation approach in the literature. Our approach leads to a unified proof for the underlying processes with bounded or unbounded variation paths, and our result generalizes Loeffen et al. (2013).

scholarly journals A new approach in model selection for ordinal target variables

2021 ◽  
Author(s):  
Elena Ballante ◽  
Silvia Figini ◽  
Pierpaolo Uberti
Keyword(s):  
Model Selection ◽  
Predictive Models ◽  
Performance Indicator ◽  
Binary Classification ◽  
Simulated Data ◽  
Parametric Models ◽  
New Approach ◽  
Mathematical Properties ◽  
Performance Indexes ◽  
Novel Approach

AbstractMulti-class predictive models are generally evaluated averaging binary classification indicators without a distinction between nominal and ordinal dependent variables. This paper introduces a novel approach to assess performances of predictive models characterized by an ordinal target variable and a new index for model evaluation is proposed. The new index satisfies mathematical properties and it can be applied to the evaluation of parametric and non parametric models. In order to show how our performance indicator works, empirical evidences obtained on toy examples and simulated data are provided. On the basis of the results achieved, we underline that our approach can be a more suitable criterion for model selection than the performance indexes currently suggested in the literature.

scholarly journals Ontology Modularization with OAPT

2020 ◽  
Vol 9 (2-3) ◽  
pp. 53-83
Author(s):  
Alsayed Algergawy ◽  
Samira Babalou ◽  
Friederike Klan ◽  
Birgitta König-Ries
Keyword(s):  
Model Selection ◽  
Semantic Web ◽  
Selection Method ◽  
Theoretic Model ◽  
New Approach ◽  
Information Theoretic ◽  
Model Selection Method ◽  
Partitioning Algorithm

Abstract Ontologies are the backbone of the Semantic Web. As a result, the number of existing ontologies and the number of topics covered by them has increased considerably. With this, reusing these ontologies becomes preferable to constructing new ontologies from scratch. However, a user might be interested in a part and/or a set of parts of a given ontology, only. Therefore, ontology modularization, i.e., splitting up an ontology into smaller parts that can be independently used, becomes a necessity. In this paper, we introduce a new approach to partition ontology based on the seeding-based scheme, which is developed and implemented through the Ontology Analysis and Partitioning Tool (OAPT). This tool proceeds according to the following methodology: first, before a candidate ontology is partitioned, OAPT optionally analyzes the input ontology to determine, if this ontology is worth considering using a predefined set of criteria that quantify the semantic and structural richness of the ontology. After that, we apply the seeding-based partitioning algorithm to modularize it into a set of modules. To decide upon a suitable number of modules that will be generated by partitioning the ontology, we provide the user a recommendation based on an information theoretic model selection method. We demonstrate the effectiveness of the OAPT tool and validate the performance of the partitioning approach by conducting an extensive set of experiments. The results prove the quality and the efficiency of the proposed tool.

scholarly journals Bayesian Lasso Tobit regression

2019 ◽  
Vol 11 (2) ◽  
pp. 1-13 ◽  
Author(s):  
Haider Kadhim Abbas
Keyword(s):  
Model Selection ◽  
Variable Selection ◽  
Covariance Matrix ◽  
Gibbs Sampler ◽  
Real Data ◽  
New Technique ◽  
Tobit Regression ◽  
Bayesian Lasso ◽  
New Approach ◽  
Selection Property

In the present research, we have proposed a new approach for model selection in Tobit regression. The new technique uses Bayesian Lasso in Tobit regression (BLTR). It has many features that give optimum estimation and variable selection property. Specifically, we introduced a new hierarchal model. Then, a new Gibbs sampler is introduced.We also extend the new approach by adding the ridge parameter inside the variance covariance matrix to avoid the singularity in the case of multicollinearity or in case the number of predictors greater than the number of observations. A comparison was made with other previous techniques applying the simulation examples and real data. It is worth mentioning, that the obtained results were promising and encouraging, giving better results compared to the previous methods.

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