scispace - formally typeset
Search or ask a question
Book ChapterDOI

Understanding the Interrelationship Between Commodity and Stock Indices Daily Movement Using ACE and Recurrence Analysis

TL;DR: In this article, the authors analyzed the complex dynamics of the daily variation of two indices of stock and commodity exchange respectively of India and the US market and found that the dynamics of Indian stock and commodities exchanges have a lagged correlation while those of US market have a lead correlation and a weaker correlation.
Abstract: The relationship between the temporal evolution of the commodity market and the stock market has long term implications for policy makers, and particularly in the case of emerging markets, the economy as a whole. We analyze the complex dynamics of the daily variation of two indices of stock and commodity exchange respectively of India. To understand whether there is any difference between emerging markets and developed markets in terms of a dynamic correlation between the two market indices, we also examine the complex dynamics of stock and commodity indices of the US market. We compare the daily variation of the commodity and stock prices in the two countries separately. For this purpose we have considered commodity India along with Dow Jones Industrial Average (DJIA) and Dow Jones-AIG Commodity (DJ-AIGCI) indices for stock and commodities, USA, from June 2005 to August 2008. To analyse the dynamics of the time variation of the indices we use a set of analytical methods based on recurrence plots. Our studies show that the dynamics of the Indian stock and commodity exchanges have a lagged correlation while those of US market have a lead correlation and a weaker correlation.
Citations
More filters
01 Jan 2010
TL;DR: The results show that synchronization of growth rates were higher among the euro area member states during the second half of the 1980s and from 1997 to roughly 2002, suggesting that apart from specific times when European integration initiatives were being implemented, globalization was likely the dominant factor behind international business cycle synchronization.
Abstract: Synchronization of growth rates are an important feature of international business cycles, particularly in relation to regional integration projects such as the single currency in Europe. Synchronization of growth rates clearly enhances the effectiveness of European Central Bank monetary policy, ensuring that policy changes are attuned to the dynamics of growth and business cycles in the majority of member states. In this paper a dissimilarity metric is constructed by measuring the topological differences between the GDP growth patterns in recurrence plots for individual countries. The results show that synchronization of growth rates were higher among the Euro area member states during the second half of the 1980s and from 1997 to roughly 2002. Apart from these two time periods, Euro area member states do not appear to be more synchronized than a group of major international countries, signifying that globalization was the major cause of international business cycle synchronization.

22 citations

01 Jan 2016
TL;DR: The nonlinear dynamics chaos and econometrics is universally compatible with any devices to read and is available in the digital library an online access to it is set as public so you can download it instantly.
Abstract: Thank you very much for downloading nonlinear dynamics chaos and econometrics. Maybe you have knowledge that, people have search numerous times for their chosen books like this nonlinear dynamics chaos and econometrics, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they cope with some infectious virus inside their desktop computer. nonlinear dynamics chaos and econometrics is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library hosts in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the nonlinear dynamics chaos and econometrics is universally compatible with any devices to read.

19 citations

Journal ArticleDOI
01 Aug 2018-Chaos
TL;DR: Experimental results show that the generalized recurrence network approach yields superior performance in the visualization of recurrence patterns in spatial data and in the extraction of salient features to characterize recurrence dynamics in spatial systems.
Abstract: Nonlinear dynamical systems exhibit complex recurrence behaviors. Recurrence plot is widely used to graphically represent the patterns of recurrence dynamics and further facilitates the quantification of recurrence patterns, namely, recurrence quantification analysis. However, traditional recurrence methods tend to be limited in their ability to handle spatial data due to high dimensionality and geometric characteristics. Prior efforts have been made to generalize the recurrence plot to a four-dimensional space for spatial data analysis, but this framework can only provide graphical visualization of recurrence patterns in the projected reduced-dimension space (i.e., two- or three- dimensions). In this paper, we propose a new weighted recurrence network approach for spatial data analysis. A weighted network model is introduced to represent the recurrence patterns in spatial data, which account for both pixel intensities and spatial distance simultaneously. Note that each network node represents a location in the high-dimensional spatial data. Network edges and weights preserve complex spatial structures and recurrence patterns. Network representation is shown to be an effective means to provide a complete picture of recurrence patterns in the spatial data. Furthermore, we leverage network statistics to characterize and quantify recurrence properties and features in the spatial data. Experimental results in both simulation and real-world case studies show that the generalized recurrence network approach yields superior performance in the visualization of recurrence patterns in spatial data and in the extraction of salient features to characterize recurrence dynamics in spatial systems.

13 citations

Book ChapterDOI
01 Jan 2016
TL;DR: In this paper, a matrix completion based approach is proposed to restore the corrupted cross-recurrence plot (CRP) prior to the estimation of the time-synchronization relationship.
Abstract: The success of a trading strategy can be significantly enhanced by tracking accurately the implied volatility changes, which refers to the amount of uncertainty or risk about the degree of changes in a market index. This fosters the need for accurate estimation of the time-synchronization profile between a given market index and its associated volatility index. In this chapter, we advance existing solutions, which are based widely on the typical correlation, for identifying this temporal interdependence. To this end, cross-recurrence plot (CRP) analysis is exploited for extracting the underlying dynamics of a given market and volatility indexes pair, along with their time-synchronization profile. However, CRPs of degraded quality, for instance due to missing information, may yield a completely erroneous estimation of this profile. To overcome this drawback, a restoration stage based on the concept of matrix completion is applied on a corrupted CRP prior to the estimation of the time-synchronization relationship. A performance evaluation on the S&P 500 index and its associated VIX volatility index reveals the superior capability of our proposed approach in restoring accurately their CRP and subsequently estimating a temporal relation between the two indexes even when \(80\,\%\) of CRP values are missing.

3 citations


Cites background from "Understanding the Interrelationship..."

  • ...CRPs, in specific, have been already exploited in the financial industry to analyze convergence and synchronicity of business and growth cycles [24], to examine the interactive behavior between the hourly accepted weighted average price and the hourly required load in electricity markets [25], as well as for understanding the interrelation between commodity and stock indexes [26] or the coupling of the European banking and insurance sectors [27]....

    [...]

Journal ArticleDOI
TL;DR: The cross-recurrence plot analysis is used as a nonlinear method for quantifying the multidimensional coupling in the time domain of two time series and for determining their state of synchronization.
Abstract: Cross-correlation analysis is a powerful tool for understanding the mutual dynamics of time series. This study introduces a new method for predicting the future state of synchronization of the dynamics of two financial time series. To this end, we use the cross recurrence plot analysis as a nonlinear method for quantifying the multidimensional coupling in the time domain of two time series and for determining their state of synchronization. We adopt a deep learning framework for methodologically addressing the prediction of the synchronization state based on features extracted from dynamically sub-sampled cross recurrence plots. We provide extensive experiments on several stocks, major constituents of the S &P100 index, to empirically validate our approach. We find that the task of predicting the state of synchronization of two time series is in general rather difficult, but for certain pairs of stocks attainable with very satisfactory performance (84% F1-score, on average).
References
More filters
Book
01 Jan 1995
TL;DR: In this article, the authors present a comparison of Discriminant Analysis with Neural Networks Predicting Corporate Mergers Using Backpropagation Networks Self-Organizing Neural Networks: the Financial State of Spanish Companies.
Abstract: PART ONE: NEURAL NETWORKS: Introduction Design Considerations Methods for Optimal Network Design Data Modelling Considerations Testing Strategies and Metrics PART TWO: EQUITY APPLICATIONS: Modelling Stock Returns in the Framework of APT: A Comparative Study with Regression Models Testing the Efficient Markets Hypothesis with Gradient Descent Algorithms Neural Networks as an Alternative Market Model PART THREE: FOREIGN EXCHANGE APPLICATIONS: The Foreign Exchange Markets Nonlinear Modelling of the US$/DM Exchange Rate Managing Exchange Rate Trading Strategies Financial Market Applications of Learning from Hints Machine Learning for Foreign Exchange Trading Indicator Selection PART FOUR: BOND APPLICATIONS: Criteria for Performance in Gilt Futures Pricing Bond Rating with Neural Networks PART FIVE: MACRO-ECONOMIC FORECASTING APPLICATIONS: Bankruptcy Prediction: a Comparison of Discriminant Analysis with Neural Networks Predicting Corporate Mergers Using Backpropagation Networks Self-Organizing Neural Networks: the Financial State of Spanish Companies.

222 citations

Journal ArticleDOI
TL;DR: In this paper, consistent cointegration tests, and estimators of a basis of the space of cointegrating vectors, that do not used specification of the data-generating process, apart from some mild regularity conditions, or estimation of structural and/or nuisance parameters, are proposed.

214 citations

Journal ArticleDOI
TL;DR: In this article, a unit-root test procedure based on the nonlinear STAR framework was proposed to uncover evidence of nonlinear mean-reversion for most cases whereas the standard Dickey-Fuller test based on a linear model cannot.

189 citations

Journal ArticleDOI
TL;DR: A number of tests for non-linear dependence in time series are presented and implemented on a set of 10 daily sterling exchange rates covering the entire post Bretton-Woods era until the present day as mentioned in this paper.
Abstract: A number of tests for non-linear dependence in time series are presented and implemented on a set of 10 daily sterling exchange rates covering the entire post Bretton-Woods era until the present day Irrefutable evidence of non-linearity is shown in many of the series, but most of this dependence can apparently be explained by reference to the GARCH family of models It is suggested that the literature in this area has reached an impasse, with the presence of ARCH effects clearly demonstrated in a large number of papers, but with the tests for non-linearity which are currently available being unable to classify any additional non-linear structure

181 citations

Journal ArticleDOI
TL;DR: In this paper, the presence of nonlinear dependence and chaos in real-time returns on the U.K. FTSE-100 Index using a six-month sample of about 60,000 observations was tested.
Abstract: This paper tests for the presence of nonlinear dependence and chaos in real-time returns on the U.K. FTSE-100 Index using a six-month sample of about 60,000 observations. Since there is clear evidence of nonlinearity, the authors follow other researchers in this field by applying the same tests to the residuals from a GARCH process fitted to the data in order to find out whether or not the nonlinearity can be explained by this type of model. In the event, their results suggest that GARCH can explain some but not all of the observed nonlinear dependence. Copyright 1995 by Royal Economic Society.

150 citations