Modeling financial time series with the skew slash distribution
Financial returns often present moderate skewness and high kurtosis. As a consequence, it is natural to look for a model that is exible enough to capture these characteristics. The proposal is to undertake inference for a generalized autoregressive conditional heteroskedastic (GARCH) model, where the innovations are assumed to follow a skew slash distribution. Both classical and Bayesian inference are carried out. Simulations and a real data example illustrate the performance of the proposed methodology.
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157, Federal Reserve Bank of Minneapolis.
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