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Statistical modeling techniques-and factor models in particular-are extensively used in practice, especially in the insurance and finance industry, where many risks have to be accounted for. In risk management applications, it might be important to analyze the situation when fixing the value of...
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We develop a distribution regression model under endogenous sample selection. This model is a semiparametric generalization of the Heckman selection model that accommodates much rich patterns of heterogeneity in the selection process and effect of the covariates. The model applies to continuous,...
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The aim of this work was to test how returns are distributed across multiple asset classes, markets and sampling …. Finally, we show how sampling and distribution of returns are strictly connected. This is of great importance as, for example …
Persistent link: https://www.econbiz.de/10012596311
It is well documented in the literature that the sample skewness and excess kurtosis can be severely biased in finite samples. In this paper, we derive analytical results for their finite-sample biases up to the second order. In general, the bias results depend on the cumulants (up to the sixth...
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proposed algorithm, which is based on tempered importance sampling, adapts the model-based density forecasts to target …
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Stochastic Volatility (SV), along with Mixed Data Sampling (MIDAS) regressions, which enable us to incorporate the impacts of …
Persistent link: https://www.econbiz.de/10014252427