Time series of realized covariance matrices can be modelled in the conditional autoregressive Wis... more Time series of realized covariance matrices can be modelled in the conditional autoregressive Wishart model family via dynamic correlations or via dynamic covariances. Extended parameterizations of these models are proposed, which imply a specific and time-varying impact parameter of the lagged realized covariance (or correlation) on the next conditional covariance (or correlation) of each asset pair. The proposed extensions guarantee the positive definiteness of the conditional covariance or correlation matrix with simple parametric restrictions, while keeping the number of parameters fixed or linear with respect to the number of assets. An empirical study on twenty-nine assets reveals that the extended models have superior forecasting performances than their simpler versions
The traditional matching methods for the estimation of treatment parameters are often affected by... more The traditional matching methods for the estimation of treatment parameters are often affected by selectivity bias due to the endogenous joint influence of latent factors on the assignment to treatment and on the outcome, especially in a cross-sectional framework. In this study, we show that the influence of unobserved factors involves a cross-correlation between the endogenous components of propensity scores and causal effects. A correction for the effects of this correlation on matching results leads to a reduction of bias. A Monte Carlo experiment and an empirical application using the LaLonde's experimental data set support this finding.
ABSTRACT In this work we use a measure of predictability of a time series following a stationary ... more ABSTRACT In this work we use a measure of predictability of a time series following a stationary ARMA process to develop a test of equal predictability of two or more time series. The test is derived by a set of propositions which links the structure of the AR and MA coefficients to the predictability measure. A particular case of this general approach is constituted by time series having a Wold decomposition with weights having the same sign; in this framework the equal predictability is equivalent to parallelism among ARMA models and the null hypothesis of equal predictability is simply a set of linear restrictions. The ARMA representation of the GARCH models presents non-negative weights, so that this test can be extended to verify the equal predictability of squared time series following GARCH structures.
In this paper we discuss some deep implications of the recent paper by Bollerslev et al. (2016) (... more In this paper we discuss some deep implications of the recent paper by Bollerslev et al. (2016) (BPQ). In BPQ the volatility dynamics modeled as a HAR is augmented by a term involving quarticity in order to correct measurement errors in realized variance. We show that the model is observationally equivalent to another where a quadratic term in realized variance accounts for a faster mean reversion when volatility is high. We argue that heteroskedasticity (volatility of volatility) and a time-varying mean seem to play a role of higher order of importance than measurement errors. In fact, the quarticity/quadratic terms disappear within an AMEM, and more so when a Markov Switching dynamics is considered. Some simulation results show that when the DGP is a (MS-)AMEM, such terms turn out spuriously significant in a HAR. Forecast performance of the (MS-)AMEM is superior to the augmented HARs.
One of the main problems in modelling multivariate conditional covariance time series is the para... more One of the main problems in modelling multivariate conditional covariance time series is the parameterization of the correlation structure because, if no constraints are imposed, it implies a large number of unknown coefficients. The most popular models propose parsimonious representations, imposing similar correlation structures to all the series or to groups of time series, but the choice of these groups is quite subjective. In this paper we propose a statistical approach to detect groups of homogeneous time series in terms of correlation dynamics. The approach is based on a clustering algorithm, which uses the idea of distance between dynamic conditional correlations, and the classical Wald test to compare the coefficients of two groups of dynamic conditional correlations. The proposed approach is evaluated in terms of simulation experiments and applied to a set of financial time series.
Time series of realized covariance matrices can be modelled in the conditional autoregressive Wis... more Time series of realized covariance matrices can be modelled in the conditional autoregressive Wishart model family via dynamic correlations or via dynamic covariances. Extended parameterizations of these models are proposed, which imply a specific and time-varying impact parameter of the lagged realized covariance (or correlation) on the next conditional covariance (or correlation) of each asset pair. The proposed extensions guarantee the positive definiteness of the conditional covariance or correlation matrix with simple parametric restrictions, while keeping the number of parameters fixed or linear with respect to the number of assets. An empirical study on twenty-nine assets reveals that the extended models have superior forecasting performances than their simpler versions
The traditional matching methods for the estimation of treatment parameters are often affected by... more The traditional matching methods for the estimation of treatment parameters are often affected by selectivity bias due to the endogenous joint influence of latent factors on the assignment to treatment and on the outcome, especially in a cross-sectional framework. In this study, we show that the influence of unobserved factors involves a cross-correlation between the endogenous components of propensity scores and causal effects. A correction for the effects of this correlation on matching results leads to a reduction of bias. A Monte Carlo experiment and an empirical application using the LaLonde's experimental data set support this finding.
ABSTRACT In this work we use a measure of predictability of a time series following a stationary ... more ABSTRACT In this work we use a measure of predictability of a time series following a stationary ARMA process to develop a test of equal predictability of two or more time series. The test is derived by a set of propositions which links the structure of the AR and MA coefficients to the predictability measure. A particular case of this general approach is constituted by time series having a Wold decomposition with weights having the same sign; in this framework the equal predictability is equivalent to parallelism among ARMA models and the null hypothesis of equal predictability is simply a set of linear restrictions. The ARMA representation of the GARCH models presents non-negative weights, so that this test can be extended to verify the equal predictability of squared time series following GARCH structures.
In this paper we discuss some deep implications of the recent paper by Bollerslev et al. (2016) (... more In this paper we discuss some deep implications of the recent paper by Bollerslev et al. (2016) (BPQ). In BPQ the volatility dynamics modeled as a HAR is augmented by a term involving quarticity in order to correct measurement errors in realized variance. We show that the model is observationally equivalent to another where a quadratic term in realized variance accounts for a faster mean reversion when volatility is high. We argue that heteroskedasticity (volatility of volatility) and a time-varying mean seem to play a role of higher order of importance than measurement errors. In fact, the quarticity/quadratic terms disappear within an AMEM, and more so when a Markov Switching dynamics is considered. Some simulation results show that when the DGP is a (MS-)AMEM, such terms turn out spuriously significant in a HAR. Forecast performance of the (MS-)AMEM is superior to the augmented HARs.
One of the main problems in modelling multivariate conditional covariance time series is the para... more One of the main problems in modelling multivariate conditional covariance time series is the parameterization of the correlation structure because, if no constraints are imposed, it implies a large number of unknown coefficients. The most popular models propose parsimonious representations, imposing similar correlation structures to all the series or to groups of time series, but the choice of these groups is quite subjective. In this paper we propose a statistical approach to detect groups of homogeneous time series in terms of correlation dynamics. The approach is based on a clustering algorithm, which uses the idea of distance between dynamic conditional correlations, and the classical Wald test to compare the coefficients of two groups of dynamic conditional correlations. The proposed approach is evaluated in terms of simulation experiments and applied to a set of financial time series.
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Papers by Edoardo Otranto