573 research outputs found
Nonparametric and Semiparametric Volatility Models: Specification, Estimation, and Testing
No full text(Luc Bauwens, Christian Hafner, and Sebastien Laurent, editors) (forthcoming) - expected publication date April</p
Proceedings of the 1st International Workshop on Advanced Analytics and Learning on Temporal Data
No full textSeveral recent contributions in econometrics and statistics .deal with the dynamic modelling of conditional covariance matrices. To guarantee the positive definiteness of the estimated
covariance matrices and to obtain parsimonious models, most of the models proposed
use scalar parameterizations that involve a small number of parameters, but
have the drawback to impose constraints that may strongly restrict the flexibility of
the dynamics of the conditional covariance or correlation process. Using the properties
of the Hadamard exponential functions, we develop parsimonious but flexible
models, which provide positive definite covariance matrices with different and time
varying coefficients for each element of the covariance matrix. Their properties are
verified with an empirical exercise, using realized covariance daily data for 29 assets
A dynamic component model for forecasting high-dimensional realized covariance matrices
No full textThe Multiplicative MIDAS Realized DCC (MMReDCC) model of Bauwens et al. decomposes the dynamics of the realized covariance matrix of returns into short-run transitory and long-run secular components where the latter reflects the effect of the continuously changing economic conditions. The model allows to obtain positive-definite forecasts of the realized covariance matrices but, due to the high number of parameters involved, estimation becomes unfeasible for large cross-sectional dimensions. Our contribution in this paper is twofold. First, in order to obtain a computationally feasible estimation procedure, we propose an algorithm that relies on the maximization of an iteratively re-computed moment-based profile likelihood function. We assess the finite sample properties of the proposed algorithm via a simulation study. Second, we propose a bootstrap procedure for generating multi-step ahead forecasts from the MMReDCC model. In an empirical application on realized covariance matrices for fifty equities, we find that the MMReDCC not only statistically outperforms the selected benchmarks in-sample, but also improves the out-of-sample ability to generate accurate multi-step ahead forecasts of the realized covariances
Computationally efficient inference procedures for vast dimensional realized covariance models
No full textThis paper illustrates some computationally efficient estimation procedures for the estimation of vast dimensional realized covariance models. In particular, we derive a Composite Maximum Likelihood (CML) estimator for the parameters of a Conditionally Autoregressive Wishart (CAW) model incorporating scalar system matrices and covariance targeting. The finite sample statistical properties of this estimator are investigated by means of a Monte Carlo simulation study in which the data generating process is assumed to be given by a scalar CAW model. The performance of the CML estimator is satisfactory in all the settings considered although a relevant finding of our study is that the efficiency of the CML estimator is critically dependent on the implementation settings chosen by modeller and, more specifically, on the dimension of the marginal log-likelihoods used to build the composite likelihood functions
Modeling Realized Covariance Matrices: A Class of Hadamard Exponential Models
No full textTime series of realized covariance matrices can be modeled 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. Two empirical studies reveal that the extended models have superior forecasting performances than their simpler versions and benchmark models
Nonlinearities and regimes in conditional correlations with different dynamics
No full textNew parameterizations of the dynamic conditional correlation (DCC) model and of the regime-switching dynamic correlation (RSDC) model are introduced, such that these models provide a specific dynamics for each correlation. They imply a nonlinear autoregressive form of dependence on lagged correlations and are based on properties of the Hadamard exponential matrix. The new models are applied to a data set of twenty stock market indices and a data set of the thirty Dow Jones components, comparing them to the classical DCC and RSDC models. The empirical results show that the new models improve their classical versions in terms of several criteria
Modeling the Dependence of Conditional Correlations on Market Volatility
No full text<p>Several models have been developed to capture the dynamics of the conditional correlations between time series of financial returns and several studies have shown that the market volatility is a major determinant of the correlations. We extend some models to include explicitly the dependence of the correlations on the market volatility. The models differ by the way—linear or nonlinear, direct or indirect—in which the volatility influences the correlations. Using a wide set of models with two measures of market volatility on two datasets, we find that for some models, the empirical results support to some extent the statistical significance and the economic significance of the volatility effect on the correlations, but the presence of the volatility effect does not improve the forecasting performance of the extended models. Supplementary materials for this article are available online.</p
Forecasting comparison of long term component dynamic models for realized covariance matrices
No full textNovel model specifications that include a time-varying long-run component in the dy-
namics of realized covariance matrices are proposed. The modeling framework allows the
secular component to enter the model either additively or as a multiplicative factor, and
to be specified parametrically, using a MIDAS filter, or non-parametrically. Estimation
is performed by maximizing a Wishart quasi-likelihood function. The one-step ahead
forecasting performance is assessed by means of three approaches: model confidence sets,
minimum variance portfolios and Value-at-Risk. The results illustrate that the proposed
models outperform benchmarks incorporating a constant long-run component, both in
and out-of-sample
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