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We analyze the construction of multivariate forecasting densities based on conditional models for each variable, given the other variables; a joint predictive density is obtained by iteratively simulating from the conditional models. This idea has been pursued in the context of missing data...
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The paper develops a tail risk forecasting model that incorporates the wealth of economic and financial information available to risk managers. The approach can be viewed as a regularized extension of the two-stage GARCH-EVT model of McNeil and Frey (2000) where we permit a time-varying...
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forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the assumptions of jumping … prices and leverage effects for volatility. Findings suggest that GARJI model provides more accurate VaR measures for the S … accurate risk measures even if jump contribution is provided. More sophisticated models might address this issue, improving VaR …
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"This paper describes a Bayesian specification procedure used to generate a vector autoregressive model for forecasting macroeconomic variables. The specification search is over parameters of a prior. This quasi-Bayesian approach is viewed as a flexible tool for constructing a filter which...
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