A new bootstrap test for multiple assets joint risk testing

2017 ◽  
Vol 19 (4) ◽  
pp. 1-22 ◽  
Author(s):  
David Ardia ◽  
Lukasz Gatarek ◽  
Lennart Hoogerheide
Keyword(s):  
Bootstrap Test ◽  
Multiple Assets

scholarly journals A Bootstrap Method to Test Granger-Causality in the Frequency Domain

2021 ◽  
Author(s):  
Matteo Farnè ◽  
Angela Montanari
Keyword(s):  
Stochastic Process ◽  
Frequency Domain ◽  
Granger Causality ◽  
Bootstrap Method ◽  
Money Stock ◽  
Bootstrap Test ◽  
Low Frequencies ◽  
Economic Output ◽  
Stationary Bootstrap ◽  
The Relationship

AbstractWe propose a bootstrap test for unconditional and conditional Granger-causality spectra in the frequency domain. Our test aims to detect if the causality at a particular frequency is systematically different from zero. In particular, we consider a stochastic process derived applying independently the stationary bootstrap to the original series. At each frequency, we test the sample causality against the distribution of the median causality across frequencies estimated for that process. Via our procedure, we infer about the relationship between money stock and GDP in the Euro Area during the period 1999–2017. We point out that the money stock aggregate M1 had a significant impact on economic output at all frequencies, while the opposite relationship is significant only at low frequencies.

scholarly journals A hierarchical clustering method for random intervals based on a similarity measure

2021 ◽  
Author(s):  
Ana Belén Ramos-Guajardo
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Real Life ◽  
Stopping Criterion ◽  
Clustering Method ◽  
Bootstrap Test ◽  
Empirical Performance ◽  
Random Intervals ◽  
Expected Values

AbstractA new clustering method for random intervals that are measured in the same units over the same group of individuals is provided. It takes into account the similarity degree between the expected values of the random intervals that can be analyzed by means of a two-sample similarity bootstrap test. Thus, the expectations of each pair of random intervals are compared through that test and a p-value matrix is finally obtained. The suggested clustering algorithm considers such a matrix where each p-value can be seen at the same time as a kind of similarity between the random intervals. The algorithm is iterative and includes an objective stopping criterion that leads to statistically similar clusters that are different from each other. Some simulations to show the empirical performance of the proposal are developed and the approach is applied to two real-life situations.

scholarly journals Integration and Modularity of Quantitative Trait Locus Effects on Geometric Shape in the Mouse Mandible

Genetics ◽  
2004 ◽  
Vol 166 (4) ◽  
pp. 1909-1921
Author(s):  
Christian Peter Klingenberg ◽  
Larry J Leamy ◽  
James M Cheverud
Keyword(s):  
Quantitative Trait ◽  
Parametric Bootstrap ◽  
Interval Mapping ◽  
Procrustes Analysis ◽  
Bootstrap Test ◽  
Mandible Shape ◽  
Trait Locus ◽  
F2 Generation ◽  
Analysis Interval ◽  
Qtl Effects

Abstract The mouse mandible has long served as a model system for complex morphological structures. Here we use new methodology based on geometric morphometrics to test the hypothesis that the mandible consists of two main modules, the alveolar region and the ascending ramus, and that this modularity is reflected in the effects of quantitative trait loci (QTL). The shape of each mandible was analyzed by the positions of 16 morphological landmarks and these data were analyzed using Procrustes analysis. Interval mapping in the F2 generation from intercrosses of the LG/J and SM/J strains revealed 33 QTL affecting mandible shape. The QTL effects corresponded to a variety of shape changes, but ordination or a parametric bootstrap test of clustering did not reveal any distinct groups of QTL that would affect primarily one module or the other. The correlations of landmark positions between the two modules tended to be lower than the correlations between arbitrary subsets of landmarks, indicating that the modules were relatively independent of each other and confirming the hypothesized location of the boundary between them. While these results are in agreement with the hypothesis of modularity, they also underscore that modularity is a question of the relative degrees to which QTL contribute to different traits, rather than a question of discrete sets of QTL contributing to discrete sets of traits.

A NONPARAMETRIC TEST OF SIGNIFICANT VARIABLES IN GRADIENTS

2020 ◽  
pp. 1-45
Author(s):  
Feng Yao ◽  
Taining Wang
Keyword(s):  
Null Distribution ◽  
Monte Carlo Study ◽  
Nonparametric Test ◽  
Hedonic Price ◽  
Finite Sample ◽  
Test Statistic ◽  
Bootstrap Test ◽  
Local Polynomial Estimation ◽  
Polynomial Estimation ◽  
Mean Function

We propose a nonparametric test of significant variables in the partial derivative of a regression mean function. The derivative is estimated by local polynomial estimation and the test statistic is constructed through a variation-based measure of the derivative in the direction of variables of interest. We establish the asymptotic null distribution of the test statistic and demonstrate that it is consistent. Motivated by the null distribution, we propose a wild bootstrap test, and show that it exhibits the same null distribution, whether the null is valid or not. We perform a Monte Carlo study to demonstrate its encouraging finite sample performance. An empirical application is conducted showing how the test can be applied to infer certain aspects of regression structures in a hedonic price model.

scholarly journals Government Debt Management: The Long and the Short of It

2018 ◽  
Vol 86 (6) ◽  
pp. 2554-2604 ◽  
Author(s):  
Elisa Faraglia ◽  
Albert Marcet ◽  
Rigas Oikonomou ◽  
Andrew Scott
Keyword(s):  
Transaction Costs ◽  
Incomplete Markets ◽  
Dominant Role ◽  
Global Solutions ◽  
Debt Management ◽  
Computational Method ◽  
Complete Markets ◽  
Market Models ◽  
Multiple Assets ◽  
Over Time

Abstract Standard optimal Debt Management (DM) models prescribe a dominant role for long bonds and advocate against issuing short bonds. They require very large positions in order to complete markets and assume each period that governments repurchase all outstanding bonds and reissue (r/r) new ones. These features of DM are inconsistent with U.S. data. We introduce incomplete markets via small transaction costs which serves to make optimal DM more closely resemble the data : r/r are negligible, short bond issuance substantial and persistent and short and long bonds positively co-vary. Intuitively, long bonds help smooth taxes over states and short bonds over time. Solving incomplete market models with multiple assets is challenging so a further contribution of this article is introducing a novel computational method to find global solutions.

scholarly journals MARKETING ON POLITICS AND PUBLIC LEADERSHIP

2018 ◽  
Vol 20 (1) ◽  
pp. 53
Author(s):  
Purwanto Purwanto ◽  
Eddy Madiono Sutanto ◽  
Asmara Indahingwati
Keyword(s):  
Significant Influence ◽  
Indirect Effect ◽  
Random Sampling ◽  
Sampling Method ◽  
Individual Performance ◽  
Political Marketing ◽  
Political Will ◽  
Bootstrap Test ◽  
Public Leadership ◽  
The People

This study examines the influence of political marketing and political will on public leadership and individual performance. The sample used is the people in Surabaya as many as 265 with a random sampling method. Amos 21 is used to test the indirect effect by using bootstrap test. The result of the research shows that political marketing and political will have a significant influence to the success of gaining public leadership, political marketing and marketing will have no significant effect on performance, and public le­a­dership has a significant effect on performance. For the indirect influence, political marketing and political will on individual performance are mediated by public leadership.

scholarly journals Relation-Aware Transformer for Portfolio Policy Learning

2020 ◽  
Author(s):  
Ke Xu ◽  
Yifan Zhang ◽  
Deheng Ye ◽  
Peilin Zhao ◽  
Mingkui Tan
Keyword(s):  
Portfolio Selection ◽  
State Of The Art ◽  
Asset Price ◽  
Sequential Patterns ◽  
Portfolio Decisions ◽  
Price Series ◽  
Crypto Currency ◽  
Key Issues ◽  
Multiple Assets ◽  
Portfolio Policy

Portfolio selection is an important yet challenging task in AI for FinTech. One of the key issues is how to represent the non-stationary price series of assets in a portfolio, which is important for portfolio decisions. The existing methods, however, fall short of capturing: 1) the complicated sequential patterns for asset price series and 2) the price correlations among multiple assets. In this paper, under a deep reinforcement learning paradigm for portfolio selection, we propose a novel Relation-aware Transformer (RAT) to handle these aspects. Specifically, being equipped with our newly developed attention modules, RAT is structurally innovated to capture both sequential patterns and asset correlations for portfolio selection. Based on the extracted sequential features, RAT is able to make profitable portfolio decisions regarding each asset via a newly devised leverage operation. Extensive experiments on real-world crypto-currency and stock datasets verify the state-of-the-art performance of RAT.

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