The Experts below are selected from a list of 52857 Experts worldwide ranked by ideXlab platform

Christian Gollier - One of the best experts on this subject based on the ideXlab platform.

  • assets Returns Volatility and investment horizon the french case
    Research Papers in Economics, 2008
    Co-Authors: Frederique Bec, Christian Gollier
    Abstract:

    This paper explores French assets Returns predictability within a VAR setup. Using quarterly data from 1970Q4 to 2006Q4, it turns out that bonds, equities and bills Returns are actually predictable. This feature implies that the investment horizon does indeed matter in the asset allocation. The VAR parameters estimates are then used to compute real Returns conditional Volatility across investment horizons. The results reveal the same kind of horizon effect as the one found in recent empirical studies using quarterly U.S. data. More specifically, the annualized standard deviation of French stocks Returns goes down from 22% for a 1-year horizon to only 2.8% for a 25-year investment horizon. They suggest that long-horizon investors overstate the share of bonds in their portfolio choice when neglecting the horizon effect on risk of asset Returns predictability.

  • assets Returns Volatility and investment horizon the french case
    Social Science Research Network, 2006
    Co-Authors: Frederique Bec, Christian Gollier
    Abstract:

    This paper explores French assets Returns predictability within a VAR setup. Using quarterly data from 1970Q4 to 2006Q4, it turns out that bonds, equities and bills Returns are actually predictable. This feature implies that the investment horizon does indeed matter in the asset allocation. The VAR parameters estimates are then used to compute real Returns conditional Volatility across investment horizons. The results reveal the same kind of horizon effect as the one found in recent empirical studies using quarterly U.S data. More specifically, the annualized standard deviation of French stocks Returns goes down from 2,8% for a 25 year investment horizon. They suggest that long-horizon investors overstate the share of bonds in their portfolio choice when neglecting the horizon effect on risk of asset Returns predictability.

Avanidhar Subrahmanyam - One of the best experts on this subject based on the ideXlab platform.

  • an empirical analysis of stock and bond market liquidity
    Review of Financial Studies, 2005
    Co-Authors: Tarun Chordia, Asani Sarkar, Avanidhar Subrahmanyam
    Abstract:

    This article explores cross-market liquidity dynamics by estimating a vector autoregressive model for liquidity (bid-ask spread and depth, Returns, Volatility, and order flow in the stock and Treasury bond markets). Innovations to stock and bond market liquidity and Volatility are significantly correlated, implying that common factors drive liquidity and Volatility in these markets. Volatility shocks are informative in predicting shifts in liquidity. During crisis periods, monetary expansions are associated with increased liquidity. Moreover, money flows to government bond funds forecast bond market liquidity. The results establish a link between "macro" liquidity, or money flows, and "micro" or transactions liquidity. Copyright 2005, Oxford University Press.

  • an empirical analysis of stock and bond market liquidity
    Review of Financial Studies, 2005
    Co-Authors: Tarun Chordia, Asani Sarkar, Avanidhar Subrahmanyam
    Abstract:

    This article explores cross-market liquidity dynamics by estimating a vector autoregressive model for liquidity (bid-ask spread and depth, Returns, Volatility, and order flow in the stock and Treasury bond markets). Innovations to stock and bond market liquidity and Volatility are significantly correlated, implying that common factors drive liquidity and Volatility in these markets. Volatility shocks are informative in predicting shifts in liquidity. During crisis periods, monetary expansions are associated with increased liquidity. Moreover, money flows to government bond funds forecast bond market liquidity. The results establish a link between "macro" liquidity, or money flows, and "micro" or transactions liquidity. A number of important theorems in finance rely on the ability of investors to trade any amount of a security without affecting the price. However, there exist several frictions,1 such as trading costs, short sale restrictions, and circuit breakers, that impact price formation. The influence of market imperfections on security pricing has long been recognized. Liquidity, in particular, has attracted a lot of attention from traders, regulators,

  • an empirical analysis of stock and bond market liquidity
    Staff Reports, 2003
    Co-Authors: Tarun Chordia, Asani Sarkar, Avanidhar Subrahmanyam
    Abstract:

    This paper explores liquidity movements in stock and Treasury bond markets over a period of more than 1800 trading days. Cross-market dynamics in liquidity are documented by estimating a vector autoregressive model for liquidity (that is, bid-ask spreads and depth), Returns, Volatility, and order flow in the stock and bond markets. We find that a shock to quoted spreads in one market affects the spreads in both markets, and that return Volatility is an important driver of liquidity. Innovations to stock and bond market liquidity and Volatility prove to be significantly correlated, suggesting that common factors drive liquidity and Volatility in both markets. Monetary expansion increases equity market liquidity during periods of financial crises, and unexpected increases (decreases) in the federal funds rate lead to decreases (increases) in liquidity and increases (decreases) in stock and bond Volatility. Finally, we find that flows to the stock and government bond sectors play an important role in forecasting stock and bond liquidity. The results establish a link between "macro" liquidity, or money flows, and "micro" or transactions liquidity.

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

  • 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.

  • 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.

Tarun Chordia - One of the best experts on this subject based on the ideXlab platform.

  • an empirical analysis of stock and bond market liquidity
    Review of Financial Studies, 2005
    Co-Authors: Tarun Chordia, Asani Sarkar, Avanidhar Subrahmanyam
    Abstract:

    This article explores cross-market liquidity dynamics by estimating a vector autoregressive model for liquidity (bid-ask spread and depth, Returns, Volatility, and order flow in the stock and Treasury bond markets). Innovations to stock and bond market liquidity and Volatility are significantly correlated, implying that common factors drive liquidity and Volatility in these markets. Volatility shocks are informative in predicting shifts in liquidity. During crisis periods, monetary expansions are associated with increased liquidity. Moreover, money flows to government bond funds forecast bond market liquidity. The results establish a link between "macro" liquidity, or money flows, and "micro" or transactions liquidity. Copyright 2005, Oxford University Press.

  • an empirical analysis of stock and bond market liquidity
    Review of Financial Studies, 2005
    Co-Authors: Tarun Chordia, Asani Sarkar, Avanidhar Subrahmanyam
    Abstract:

    This article explores cross-market liquidity dynamics by estimating a vector autoregressive model for liquidity (bid-ask spread and depth, Returns, Volatility, and order flow in the stock and Treasury bond markets). Innovations to stock and bond market liquidity and Volatility are significantly correlated, implying that common factors drive liquidity and Volatility in these markets. Volatility shocks are informative in predicting shifts in liquidity. During crisis periods, monetary expansions are associated with increased liquidity. Moreover, money flows to government bond funds forecast bond market liquidity. The results establish a link between "macro" liquidity, or money flows, and "micro" or transactions liquidity. A number of important theorems in finance rely on the ability of investors to trade any amount of a security without affecting the price. However, there exist several frictions,1 such as trading costs, short sale restrictions, and circuit breakers, that impact price formation. The influence of market imperfections on security pricing has long been recognized. Liquidity, in particular, has attracted a lot of attention from traders, regulators,

  • an empirical analysis of stock and bond market liquidity
    Staff Reports, 2003
    Co-Authors: Tarun Chordia, Asani Sarkar, Avanidhar Subrahmanyam
    Abstract:

    This paper explores liquidity movements in stock and Treasury bond markets over a period of more than 1800 trading days. Cross-market dynamics in liquidity are documented by estimating a vector autoregressive model for liquidity (that is, bid-ask spreads and depth), Returns, Volatility, and order flow in the stock and bond markets. We find that a shock to quoted spreads in one market affects the spreads in both markets, and that return Volatility is an important driver of liquidity. Innovations to stock and bond market liquidity and Volatility prove to be significantly correlated, suggesting that common factors drive liquidity and Volatility in both markets. Monetary expansion increases equity market liquidity during periods of financial crises, and unexpected increases (decreases) in the federal funds rate lead to decreases (increases) in liquidity and increases (decreases) in stock and bond Volatility. Finally, we find that flows to the stock and government bond sectors play an important role in forecasting stock and bond liquidity. The results establish a link between "macro" liquidity, or money flows, and "micro" or transactions liquidity.

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

  • 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.

  • 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.