Download or read book Multivariate Linear and Non Linear Causality Tests written by Zhidong Bai and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The traditional linear Granger test has been widely used to examine the linear causality among several time series in bivariate settings as well as multivariate settings. Hiemstra and Jones (1994) develop a nonlinear Granger causality test in a bivariate setting to investigate the nonlinear causality between stock prices and trading volume. In this paper, we first discuss linear causality tests in multivariate settings and thereafter develop a non-linear causality test in multivariate settings.
Download or read book Testing of Linear and Nonlinear Granger Causality in the Stock Price Volume Relation written by Paramsothy Silvapulle and published by . This book was released on 1997 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Linear and Nonlinear Granger Causality written by Abhay Abhyankar and published by . This book was released on 1998 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Several studies have observed a lead-lag relationship between stock index futures and the cash market returns relying largely on the traditional linear tests for Granger causality. Recent research however suggests evidence of nonlinearities in futures and cash market returns. In this study, matched five minute returns from the S amp; P 500 and the FT-SE 100 index futures and cash markets are examined for the presence of both linear and nonlinear causality. Tests for nonlinear Granger causality are based on a methodology recently developed by Baek and Brock. The results of the linear causality tests are similar to those reported in the previous literature. However, the nonlinear Granger causality tests suggest strong evidence of a bi-directional nonlinear causation. The results emphasize the utility of the Baek-Brock test in exploring dynamic asset pricing relationships and point toward a possible misspecification of the forward pricing model.
Download or read book Economic Growth and Financial Development written by Muhammad Shahbaz and published by Springer Nature. This book was released on 2021-09-21 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book looks into the relationship between financial development, economic growth, and the possibility of a potential capital flight in the transmission process. It also examines the important role that financial institutions, financial markets, and country-level institutional factors play in economic growth and their impact on capital flight in emerging economies. By presenting new theoretical insights and empirical country studies as well as econometric approaches, the authors focus on the relationship between financial development and economic growth with capital flight in the era of financial crisis. Therefore, this book is a must-read for researchers, scholars, and policy-makers, interested in a better understanding of economic growth and financial development of emerging economies alike.
Download or read book Panel Non Linear Causality Test written by Zhidong Bai and published by . This book was released on 2015 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we develop a new nonlinear Granger causality test to take into consideration different lags of variables and different values of the bound for panel data.
Download or read book Linear and Nonlinear Granger Causality written by A. H. Abhyankar and published by . This book was released on 1994 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Handbook of Time Series Analysis written by Björn Schelter and published by John Wiley & Sons. This book was released on 2006-12-13 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook provides an up-to-date survey of current research topics and applications of time series analysis methods written by leading experts in their fields. It covers recent developments in univariate as well as bivariate and multivariate time series analysis techniques ranging from physics' to life sciences' applications. Each chapter comprises both methodological aspects and applications to real world complex systems, such as the human brain or Earth's climate. Covering an exceptionally broad spectrum of topics, beginners, experts and practitioners who seek to understand the latest developments will profit from this handbook.
Download or read book Cointegration and Detectable Linear and Nonlinear Causality written by An-Sing Chen and published by . This book was released on 2003 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper applies linear and nonlinear Granger causality tests to examine the dynamic relation between London Metal Exchange (LME) cash prices and three possible predictors. The analysis uses matched quarterly inventory, UK Treasury bill interest rates, futures prices and cash prices for the commodity lead traded on the LME. We also examine the effects of cointegration on both linear and nonlinear Granger causality tests. When cointegration is not modeled, we find evidence of both linear and nonlinear causality between cash prices and analyzed predictor variables. However, after controlling for cointegration, we no longer find evidence of significant nonlinear causality. Our results contribute to the empirical literature on commodity price forecasting by highlighting the relationship between cointegration and detectable linear and nonlinear causality. We also illustrate the importance of interest rate and inventory as well as futures price in forecasting cash prices. Our failure to detect significant nonlinearity after controlling for cointegration may also go some way to explaining the reason for the disappointing forecasting performances of many nonlinear models in the general finance literature. It may be that the variables are correct, but the functional form is overly complex and a standard VAR or VECM may often apply.
Download or read book Nonlinear Granger Causal Paths Dependence Measures and Canonical Correlations written by Hrishikesh D. Vinod and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A popular F test of Granger-causality relies on normally distributed errors of ordinary least squares (OLS) linear regressions. There is a long-standing need for a user-friendly algorithm replacing the OLS by kernel regressions, and the F test by a bootstrap. This paper introduces a version (1.1.6) of the R package 'generalCorr' which offers (bootGcRsq) to satisfy the need. Granger causality requires the 'cause' to occur at a time before the 'effect' occurs, ruling out instantaneous causality. The R command (causeSummary) for assessing instantaneous causality possibly in cross-sectional data is now enhanced by (causeSummBlk). The command (gmcmtx0) for the non-symmetric matrix of generalized correlation coefficients R* is enhanced by (gmcmtxBlk). The asymmetric R* leads to two new concepts, (i) measures of dependence implemented by the command (depMeas), and (ii) generalized canonical correlations which explicitly incorporate pairwise non-linear dependence between linear combinations of variables. The latter needs a new Lagrangian maximization implemented by the command (canonRho). We illustrate its application using joint production of wool and mutton by capital and labor.
Download or read book Cointegration Causality and Forecasting written by Halbert White and published by Oxford University Press, USA. This book was released on 1999 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: A collection of essays in honour of Clive Granger. The chapters are by some of the world's leading econometricians, all of whom have collaborated with and/or studied with both) Clive Granger. Central themes of Granger's work are reflected in the book with attention to tests for unit roots and cointegration, tests of misspecification, forecasting models and forecast evaluation, non-linear and non-parametric econometric techniques, and overall, a careful blend of practical empirical work and strong theory. The book shows the scope of Granger's research and the range of the profession that has been influenced by his work.
Download or read book Dynamic Copula Methods in Finance written by Umberto Cherubini and published by John Wiley & Sons. This book was released on 2011-10-20 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: The latest tools and techniques for pricing and risk management This book introduces readers to the use of copula functions to represent the dynamics of financial assets and risk factors, integrated temporal and cross-section applications. The first part of the book will briefly introduce the standard the theory of copula functions, before examining the link between copulas and Markov processes. It will then introduce new techniques to design Markov processes that are suited to represent the dynamics of market risk factors and their co-movement, providing techniques to both estimate and simulate such dynamics. The second part of the book will show readers how to apply these methods to the evaluation of pricing of multivariate derivative contracts in the equity and credit markets. It will then move on to explore the applications of joint temporal and cross-section aggregation to the problem of risk integration.
Download or read book Frontiers in Computational and Systems Biology written by Jianfeng Feng and published by Springer Science & Business Media. This book was released on 2010-06-14 with total page 411 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biological and biomedical studies have entered a new era over the past two decades thanks to the wide use of mathematical models and computational approaches. A booming of computational biology, which sheerly was a theoretician’s fantasy twenty years ago, has become a reality. Obsession with computational biology and theoretical approaches is evidenced in articles hailing the arrival of what are va- ously called quantitative biology, bioinformatics, theoretical biology, and systems biology. New technologies and data resources in genetics, such as the International HapMap project, enable large-scale studies, such as genome-wide association st- ies, which could potentially identify most common genetic variants as well as rare variants of the human DNA that may alter individual’s susceptibility to disease and the response to medical treatment. Meanwhile the multi-electrode recording from behaving animals makes it feasible to control the animal mental activity, which could potentially lead to the development of useful brain–machine interfaces. - bracing the sheer volume of genetic, genomic, and other type of data, an essential approach is, ?rst of all, to avoid drowning the true signal in the data. It has been witnessed that theoretical approach to biology has emerged as a powerful and st- ulating research paradigm in biological studies, which in turn leads to a new - search paradigm in mathematics, physics, and computer science and moves forward with the interplays among experimental studies and outcomes, simulation studies, and theoretical investigations.
Download or read book An Examination of Linear and Nonlinear Causal Relationships Between Commodity Prices and U S Inflation written by Renuka Mahadevan and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This article uses both linear and nonlinear causality tests to examine the causal relationships between changes in commodity prices and U.S. inflation. Prior to the Great Moderation, there is evidence that changes in commodity price indices linearly lead inflation. The stability of the causal relationship appears to vary over time with a stronger bivariate link established before the Great Moderation. Further, there is evidence of significant nonlinear causality from raw industrials and metals indices to inflation with most of this detected nonlinear relationship being captured using the Baba, Engle, Kraft, and Kroner asymmetric generalized autoregressive conditional heteroskedasticity model. This implies that the observed nonlinear Granger causality is largely driven by unanticipated shocks and volatility spillovers in the run-up of commodity prices in late 2000.
Download or read book Nonlinearity and Endogeneity in Macro Asset Pricing written by Mr.Charles Frederick Kramer and published by International Monetary Fund. This book was released on 1995-03-01 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: We find nonlinear feedback between the stock market and certain macroeconomic factors. This evidence calls into question the adequacy of these factors as a basis for a linear pricing model. It also means that the interaction between the economy and the stock market is more complicated than given by the simple relationship in Chen, Roll and Ross (1986). It also suggests that the univariate evidence for nonlinear dynamics in the stock market may be due to the complicated relationship between the macroeconomy and the stock market.
Download or read book Finite Mixture Models written by Geoffrey McLachlan and published by John Wiley & Sons. This book was released on 2004-03-22 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: An up-to-date, comprehensive account of major issues in finitemixture modeling This volume provides an up-to-date account of the theory andapplications of modeling via finite mixture distributions. With anemphasis on the applications of mixture models in both mainstreamanalysis and other areas such as unsupervised pattern recognition,speech recognition, and medical imaging, the book describes theformulations of the finite mixture approach, details itsmethodology, discusses aspects of its implementation, andillustrates its application in many common statisticalcontexts. Major issues discussed in this book include identifiabilityproblems, actual fitting of finite mixtures through use of the EMalgorithm, properties of the maximum likelihood estimators soobtained, assessment of the number of components to be used in themixture, and the applicability of asymptotic theory in providing abasis for the solutions to some of these problems. The author alsoconsiders how the EM algorithm can be scaled to handle the fittingof mixture models to very large databases, as in data miningapplications. This comprehensive, practical guide: * Provides more than 800 references-40% published since 1995 * Includes an appendix listing available mixture software * Links statistical literature with machine learning and patternrecognition literature * Contains more than 100 helpful graphs, charts, and tables Finite Mixture Models is an important resource for both applied andtheoretical statisticians as well as for researchers in the manyareas in which finite mixture models can be used to analyze data.
Download or read book Nonlinear Dynamics Chaos and Instability written by William A. Brock and published by MIT Press. This book was released on 1991 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: Brock, Hsieh, and LeBaron show how the principles of chaos theory can be applied to such areas of economics and finance as the changing structure of stock returns and nonlinearity in foreign exchange.
Download or read book Elements of Causal Inference written by Jonas Peters and published by MIT Press. This book was released on 2017-11-29 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.
