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AbstractDynamic conditional beta is an approach to estimating regressions with time varying parameters. The conditional covariance matrices of the exogenous and dependent variable for each time period are used to formulate the dynamic beta. Joint estimation of the covariance matrices and other...
AbstractExtracting and forecasting the volatility of financial markets is an important empirical problem. The article provides a time series characterization of the volatility components arising when the volatility process is fractionally integrated, through a generalization of the...
AbstractA new class of quantile regression-based tests for fractional integration at individual and joint quantiles of a time series, thereby generalizing unit-root testing in this context, are introduced. The asymptotic null distributions of these tests are standard and free of nuisance...
AbstractThis article develops a new test to evaluate value-at-risk (VaR) forecasts. VaR is a standard risk measure widely utilized by financial institutions and regulators, yet estimating VaR is a challenging problem, and popular VaR forecast relies on unrealistic assumptions. Hence, assessing...
AbstractWe investigate, within flexible semiparametric and parametric frameworks, the shape of the news impact curve (NIC) for the conditional skewness of stock returns, that is, how past returns affect present skewness. We find that returns may impact skewness in ways that sharply differ from...
AbstractWe propose importance sampling algorithms based on fast band matrix routines for estimating the observed-data likelihoods for a variety of stochastic volatility models. This is motivated by the problem of computing the deviance information criterion (DIC)—a popular Bayesian model...
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