scholarly journals The predictive power of the business and bank sentiment of firms: A high-dimensional Granger Causality approach

2016 ◽  
Vol 254 (1) ◽  
pp. 138-147 ◽  
Author(s):  
Ines Wilms ◽  
Sarah Gelper ◽  
Christophe Croux
Keyword(s):  
Granger Causality ◽  
Predictive Power ◽  
High Dimensional

scholarly journals Stock prices and economic activity nexus in OECD countries: new evidence from an asymmetric panel Granger causality test in the frequency domain

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Veli Yilanci ◽  
Onder Ozgur ◽  
Muhammed Sehid Gorus
Keyword(s):  
Frequency Domain ◽  
Granger Causality ◽  
Stock Prices ◽  
Economic Activity ◽  
Stock Price ◽  
Predictive Power ◽  
Oecd Countries ◽  
Causality Test ◽  
Asymmetric Shocks ◽  
Panel Granger Causality

AbstractThis study investigates the stock price–economic activity nexus in 12 member countries of the Organization for Economic Cooperation and Development (OECD) by employing monthly data over the period 1981:1–2018:3. For this purpose, the study uses Granger causality in the frequency domain in the panel setting by decomposing the symmetric and asymmetric fluctuations. This methodology determines whether the predictive power of interested variables is concentrated on quickly, moderately, or slowly fluctuating components. Our findings show that the stock prices have predictive power for future long-term economic activity in the panel setting. However, economic activity has more reliable information for stock prices for negative components. Additionally, empirical findings for asymmetric shocks are not fully consistent with those of symmetric ones. Besides, the country-specific results provide different causal linkages across members and frequencies. These findings may provide valuable information for policymakers to design proper and effective policies in OECD countries regarding the stock market and economic activity nexus.

scholarly journals Non-linear Causality Between Stock Returns And Inflation Uncertainty: Evidence From The US And The UK

2012 ◽  
Vol 12 (1) ◽  
pp. 63
Author(s):  
Esin Cakan
Keyword(s):  
Stock Returns ◽  
Granger Causality ◽  
Predictive Power ◽  
Inflation Uncertainty ◽  
The Us ◽  
Non Linear ◽  
Causality Tests ◽  
Granger Causality Tests ◽  
The Uk ◽  
Dynamic Relationships

This study analyzes the dynamic relationships between inflation uncertainty and stock returns by employing the linear and non-linear Granger causality tests for the US and the UK. Using GARCH model to generate a measure of inflation uncertainty, it does not have a predictive power for stock returns, as predicted by Friedman, and it does not support the opportunistic central bank hypothesis suggested by Cukierman-Meltzer. However, the findings from non-linear Granger causality put forth that there is a bi-directional non-linear predictive power between these variables. Stock market is used as a hedge against inflation uncertainty.

scholarly journals Yule–Walker Equations Using a Gini Covariance Matrix for the High-Dimensional Heavy-Tailed PVAR Model

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 614
Author(s):  
Jin Zou ◽  
Dong Han
Keyword(s):  
Granger Causality ◽  
Real Data ◽  
Vital Role ◽  
High Dimensional ◽  
Asymptotic Results ◽  
Vector Autoregressive ◽  
Matrix Estimation ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed ◽  
Gini Covariance

Gini covariance plays a vital role in analyzing the relationship between random variables with heavy-tailed distributions. In this papaer, with the existence of a finite second moment, we establish the Gini–Yule–Walker equation to estimate the transition matrix of high-dimensional periodic vector autoregressive (PVAR) processes, the asymptotic results of estimators have been established. We apply this method to study the Granger causality of the heavy-tailed PVAR process, and the results show that the robust transfer matrix estimation induces sign consistency in the value of Granger causality. Effectiveness of the proposed method is verified by both synthetic and real data.

A high-dimensional classification approach based on class-dependent feature subspace

2017 ◽  
Vol 117 (10) ◽  
pp. 2325-2339
Author(s):  
Fuzan Chen ◽  
Harris Wu ◽  
Runliang Dou ◽  
Minqiang Li
Keyword(s):  
Predictive Power ◽  
High Dimensional Data ◽  
Classification Model ◽  
High Dimensional ◽  
Svm Classifier ◽  
Data Sets ◽  
Content Type ◽  
Classification Approach ◽  
Dimensional Classification ◽  
Feature Subspace

Purpose The purpose of this paper is to build a compact and accurate classifier for high-dimensional classification. Design/methodology/approach A classification approach based on class-dependent feature subspace (CFS) is proposed. CFS is a class-dependent integration of a support vector machine (SVM) classifier and associated discriminative features. For each class, our genetic algorithm (GA)-based approach evolves the best subset of discriminative features and SVM classifier simultaneously. To guarantee convergence and efficiency, the authors customize the GA in terms of encoding strategy, fitness evaluation, and genetic operators. Findings Experimental studies demonstrated that the proposed CFS-based approach is superior to other state-of-the-art classification algorithms on UCI data sets in terms of both concise interpretation and predictive power for high-dimensional data. Research limitations/implications UCI data sets rather than real industrial data are used to evaluate the proposed approach. In addition, only single-label classification is addressed in the study. Practical implications The proposed method not only constructs an accurate classification model but also obtains a compact combination of discriminative features. It is helpful for business makers to get a concise understanding of the high-dimensional data. Originality/value The authors propose a compact and effective classification approach for high-dimensional data. Instead of the same feature subset for all the classes, the proposed CFS-based approach obtains the optimal subset of discriminative feature and SVM classifier for each class. The proposed approach enhances both interpretability and predictive power for high-dimensional data.

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