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forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation …
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systematic exploration and forecasting of sovereign default risks. Multivariate statistical and stochastic process …-based sovereign default risk forecasting has a 50-year developmental history. This article describes a continuous, non …
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can be used in risk measurement and forecasting. Value at risk (VaR) is a widely used measure of financial risk, which … series analysis conducted and led to the forecasting of the returns. It was noted that these methods could not be used in … relation of assets with each other. Furthermore, we also examined the environment as a whole, then applied forecasting models …
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quite successful in forecasting monthly changes in commodity prices, but that success diminished in the period following …
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forecasting the volatility of international stock markets. Furthermore, the results suggest that the most vulnerable stock markets …
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This paper proposes a new combined semiparametric estimator of the conditional variance that takes the product of a parametric estimator and a nonparametric estimator based on machine learning. A popular kernel-based machine learning algorithm, known as the kernel-regularized least squares...
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