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Michael Mcaleer – One of the best experts on this subject based on the ideXlab platform.

  • Volatility spillovers between energy and Agricultural Markets in theory and practice
    , 2018
    Co-Authors: Chia-lin Chang, Michael Mcaleer

    Abstract:

    textabstractEnergy and Agricultural commodities and Markets have been examined extensively, albeit separately, for a number of years. In the energy literature, the returns, volatility and volatility spillovers (namely, the delayed effect of a returns shock in one asset on the subsequent volatility or covolatility in another asset), among alternative energy commodities, such as oil, gasoline and ethanol across different Markets, have been analysed using a variety of univariate and multivariate models, estimation techniques, data sets, and time frequencies. A similar comment applies to the separate theoretical and empirical analysis of a wide range of Agricultural commodities and Markets. Given the recent interest and emphasis in bio-fuels and green energy, especially bio-ethanol, which is derived from a range of Agricultural products, it is not surprising that there is a topical and developing literature on the spillovers between energy and Agricultural Markets. Modelling and testing spillovers between the energy and Agricultural Markets has typically been based on estimating multivariate conditional volatility models, specifically the BEKK and DCC models. The purpose of the paper is to evaluate the theory and practice in testing for volatility spillovers between energy and Agricultural Markets using the multivariate BEKK and DCC models, and to make recommendations as to how such spillovers might be tested using valid statistical techniques. Three new definitions of volatility and covolatility spillovers are given, and the different models used in empirical applications are evaluated in terms of the new definitions and statistical criteria.

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  • volatility spillovers between energy and Agricultural Markets a critical appraisal of theory and practice
    Energies, 2018
    Co-Authors: Chia-lin Chang, Michael Mcaleer

    Abstract:

    Energy and Agricultural commodities and Markets have been examined extensively, albeit separately, for a number of years. In the energy literature, the returns, volatility and volatility spillovers (namely, the delayed effect of a returns shock in one asset on the subsequent volatility or covolatility in another asset), among alternative energy commodities, such as oil, gasoline and ethanol across different Markets, have been analysed using a variety of univariate and multivariate models, estimation techniques, data sets, and time frequencies. A similar comment applies to the separate theoretical and empirical analysis of a wide range of Agricultural commodities and Markets. Given the recent interest and emphasis in bio-fuels and green energy, especially bio-ethanol, which is derived from a range of Agricultural products, it is not surprising that there is a topical and developing literature on the spillovers between energy and Agricultural Markets. Modelling and testing spillovers between the energy and Agricultural Markets has typically been based on estimating multivariate conditional volatility models, specifically the Baba, Engle, Kraft, and Kroner (BEKK) and dynamic conditional correlation (DCC) models. A serious technical deficiency is that the Quasi-Maximum Likelihood Estimates (QMLE) of a Full BEKK matrix, which is typically estimated in examining volatility spillover effects, has no asymptotic properties, except by assumption, so that no valid statistical test of volatility spillovers is possible. Some papers in the literature have used the DCC model to test for volatility spillovers. However, it is well known in the financial econometrics literature that the DCC model has no regularity conditions, and that the QMLE of the parameters of DCC has no asymptotic properties, so that there is no valid statistical testing of volatility spillovers. The purpose of the paper is to evaluate the theory and practice in testing for volatility spillovers between energy and Agricultural Markets using the multivariate Full BEKK and DCC models, and to make recommendations as to how such spillovers might be tested using valid statistical techniques. Three new definitions of volatility and covolatility spillovers are given, and the different models used in empirical applications are evaluated in terms of the new definitions and statistical criteria.

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  • volatility spillovers between energy and Agricultural Markets a critical appraisal of theory and practice
    Econometric Institute Research Papers, 2015
    Co-Authors: Chia-lin Chang, Michael Mcaleer

    Abstract:

    Energy and Agricultural commodities and Markets have been examined extensively, albeit separately, for a number of years. In the energy literature, the returns, volatility and volatility spillovers (namely, the delayed effect of a returns shock in one asset on the subsequent volatility or covolatility in another asset), among alternative energy commodities, such as oil, gasoline and ethanol across different Markets, have been analysed using a variety of univariate and multivariate models, estimation techniques, data sets, and time frequencies. A similar comment applies to the separate theoretical and empirical analysis of a wide range of Agricultural commodities and Markets. Given the recent interest and emphasis in bio-fuels and green energy, especially bio-ethanol, which is derived from a range of Agricultural products, it is not surprising that there is a topical and developing literature on the spillovers between energy and Agricultural Markets. Modelling and testing spillovers between the energy and Agricultural Markets has typically been based on estimating multivariate conditional volatility models, specifically the BEKK and DCC models. A serious technical deficiency is that the Quasi-Maximum Likelihood Estimates (QMLE) of a full BEKK matrix, which is typically estimated in examining volatility spillover effects, has no asymptotic properties, except by assumption, so that no statistical test of volatility spillovers is possible. Some papers in the literature have used the DCC model to test for volatility spillovers. However, it is well known in the financial econometrics literature that the DCC model has no regularity conditions, and that the QMLE of the parameters of DCC has no asymptotic properties, so that there is no valid statistical testing of volatility spillovers. The purpose of the paper is to evaluate the theory and practice in testing for volatility spillovers between energy and Agricultural Markets using the multivariate BEKK and DCC models, and to make recommendations as to how such spillovers might be tested using valid statistical techniques. Three new definitions of volatility and covolatility spillovers are given, and the different models used in empirical applications are evaluated in terms of the new definitions and statistical criteria.

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Giovanni Federico – One of the best experts on this subject based on the ideXlab platform.

  • The cost of railroad regulation: the disintegration of American Agricultural Markets in the interwar period
    The Economic History Review, 2013
    Co-Authors: Giovanni Federico, Paul Sharp

    Abstract:

    Article first published online: 16 APR 2013.This article investigates the costs of transport regulation using the example of Agricultural Markets in the US. Using a large database of prices by state of Agricultural commodities, we find that dispersion fell for many commodities until the First World War. We demonstrate that this reflected changes in transport costs which in turn in the long run depended on productivity growth in railroads. The year 1920 marked a change in this relationship, however, and between the First and Second World Wars we find considerable disintegration of Agricultural Markets, ultimately as a consequence of the 1920 Transportation Act. We argue that this benefited railroad companies in the 1920s and workers in the 1930s, and we put forward an estimate of the welfare losses for the consumers of railroad services (that is, Agricultural producers and final consumers)

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  • The cost of railroad regulation: the disintegration of American Agricultural Markets in the interwar period
    The Economic History Review, 2013
    Co-Authors: Giovanni Federico, Paul Richard Sharp

    Abstract:

    We investigate the costs of transportation regulation using the example of Agricultural Markets in the United States. Using a large database of prices by state of Agricultural commodities, we find that the coefficient of variation (as a measure of market integration between states) falls for many commodities until the First World War. We demonstrate that this reflected changes in transportation costs which in turn in the long run depended on productivity growth in railroads. 1920 marked a change in this relationship, however, and between the First and Second World Wars we find considerable disintegration of Agricultural Markets, ultimately as a consequence of the 1920 Transportation Act. We argue that this benefited railroad companies in the 1920s and workers in the 1930s, and we put forward an estimate of the welfare losses for the consumers of railroad services (i.e. Agricultural producers and final consumers).

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Chia-lin Chang – One of the best experts on this subject based on the ideXlab platform.

  • Volatility spillovers between energy and Agricultural Markets in theory and practice
    , 2018
    Co-Authors: Chia-lin Chang, Michael Mcaleer

    Abstract:

    textabstractEnergy and Agricultural commodities and Markets have been examined extensively, albeit separately, for a number of years. In the energy literature, the returns, volatility and volatility spillovers (namely, the delayed effect of a returns shock in one asset on the subsequent volatility or covolatility in another asset), among alternative energy commodities, such as oil, gasoline and ethanol across different Markets, have been analysed using a variety of univariate and multivariate models, estimation techniques, data sets, and time frequencies. A similar comment applies to the separate theoretical and empirical analysis of a wide range of Agricultural commodities and Markets. Given the recent interest and emphasis in bio-fuels and green energy, especially bio-ethanol, which is derived from a range of Agricultural products, it is not surprising that there is a topical and developing literature on the spillovers between energy and Agricultural Markets. Modelling and testing spillovers between the energy and Agricultural Markets has typically been based on estimating multivariate conditional volatility models, specifically the BEKK and DCC models. The purpose of the paper is to evaluate the theory and practice in testing for volatility spillovers between energy and Agricultural Markets using the multivariate BEKK and DCC models, and to make recommendations as to how such spillovers might be tested using valid statistical techniques. Three new definitions of volatility and covolatility spillovers are given, and the different models used in empirical applications are evaluated in terms of the new definitions and statistical criteria.

    Free Register to Access Article

  • volatility spillovers between energy and Agricultural Markets a critical appraisal of theory and practice
    Energies, 2018
    Co-Authors: Chia-lin Chang, Michael Mcaleer

    Abstract:

    Energy and Agricultural commodities and Markets have been examined extensively, albeit separately, for a number of years. In the energy literature, the returns, volatility and volatility spillovers (namely, the delayed effect of a returns shock in one asset on the subsequent volatility or covolatility in another asset), among alternative energy commodities, such as oil, gasoline and ethanol across different Markets, have been analysed using a variety of univariate and multivariate models, estimation techniques, data sets, and time frequencies. A similar comment applies to the separate theoretical and empirical analysis of a wide range of Agricultural commodities and Markets. Given the recent interest and emphasis in bio-fuels and green energy, especially bio-ethanol, which is derived from a range of Agricultural products, it is not surprising that there is a topical and developing literature on the spillovers between energy and Agricultural Markets. Modelling and testing spillovers between the energy and Agricultural Markets has typically been based on estimating multivariate conditional volatility models, specifically the Baba, Engle, Kraft, and Kroner (BEKK) and dynamic conditional correlation (DCC) models. A serious technical deficiency is that the Quasi-Maximum Likelihood Estimates (QMLE) of a Full BEKK matrix, which is typically estimated in examining volatility spillover effects, has no asymptotic properties, except by assumption, so that no valid statistical test of volatility spillovers is possible. Some papers in the literature have used the DCC model to test for volatility spillovers. However, it is well known in the financial econometrics literature that the DCC model has no regularity conditions, and that the QMLE of the parameters of DCC has no asymptotic properties, so that there is no valid statistical testing of volatility spillovers. The purpose of the paper is to evaluate the theory and practice in testing for volatility spillovers between energy and Agricultural Markets using the multivariate Full BEKK and DCC models, and to make recommendations as to how such spillovers might be tested using valid statistical techniques. Three new definitions of volatility and covolatility spillovers are given, and the different models used in empirical applications are evaluated in terms of the new definitions and statistical criteria.

    Free Register to Access Article

  • volatility spillovers between energy and Agricultural Markets a critical appraisal of theory and practice
    Econometric Institute Research Papers, 2015
    Co-Authors: Chia-lin Chang, Michael Mcaleer

    Abstract:

    Energy and Agricultural commodities and Markets have been examined extensively, albeit separately, for a number of years. In the energy literature, the returns, volatility and volatility spillovers (namely, the delayed effect of a returns shock in one asset on the subsequent volatility or covolatility in another asset), among alternative energy commodities, such as oil, gasoline and ethanol across different Markets, have been analysed using a variety of univariate and multivariate models, estimation techniques, data sets, and time frequencies. A similar comment applies to the separate theoretical and empirical analysis of a wide range of Agricultural commodities and Markets. Given the recent interest and emphasis in bio-fuels and green energy, especially bio-ethanol, which is derived from a range of Agricultural products, it is not surprising that there is a topical and developing literature on the spillovers between energy and Agricultural Markets. Modelling and testing spillovers between the energy and Agricultural Markets has typically been based on estimating multivariate conditional volatility models, specifically the BEKK and DCC models. A serious technical deficiency is that the Quasi-Maximum Likelihood Estimates (QMLE) of a full BEKK matrix, which is typically estimated in examining volatility spillover effects, has no asymptotic properties, except by assumption, so that no statistical test of volatility spillovers is possible. Some papers in the literature have used the DCC model to test for volatility spillovers. However, it is well known in the financial econometrics literature that the DCC model has no regularity conditions, and that the QMLE of the parameters of DCC has no asymptotic properties, so that there is no valid statistical testing of volatility spillovers. The purpose of the paper is to evaluate the theory and practice in testing for volatility spillovers between energy and Agricultural Markets using the multivariate BEKK and DCC models, and to make recommendations as to how such spillovers might be tested using valid statistical techniques. Three new definitions of volatility and covolatility spillovers are given, and the different models used in empirical applications are evaluated in terms of the new definitions and statistical criteria.

    Free Register to Access Article