The interdependence, dynamics and riskiness of financial institutions are the key features frequently tackled in financial econometrics. We propose a Tail Event driven Network Quantile Regression (TENQR) model which addresses ...

This paper contributes to model the industry interconnecting structure in a network context. General predictive model (Rapach et al. 2016) is extended to quantile LASSO regression so as to incorporate tail risks in the ...

Analysis of monthly disaggregated data from 1978 to 2016 on US household in ation expectations reveals that exposure to news on in ation and monetary policy helps to explain in ation expectations. This remains true when ...

This paper proposes a test for missing at random (MAR). The MAR assumption is shown to be testable given instrumental variables which are independent of response given potential outcomes. A nonparametric testing procedure ...

QuantNet being an online GitHub based organization is an integrated environment consisting of different types of statistics-related documents and program codes called Quantlets. The QuantNet Style Guide and the yamldebugger ...

Cryptocurrencies have left the dark side of the finance universe and become an object of study for asset and portfolio management. Since they have a low liquidity compared to traditional assets, one needs to take into ...

The JEL classification system is a standard way of assigning key topics to economic articles in order to make them more easily retrievable in the bulk of nowadays massive literature. Usually the JEL (Journal of Economic ...

We analyze an estimated stochastic general equilibrium model that replicates key macroeconomic and financial stylized facts during the Great Moderation of 1983-2007. Our model predicts a sizeable and volatile nominal term ...

I entertain a generalization of the standard Bolzmann-Gibbs-Shannon measure of entropy in multiplier preferences of model uncertainty. Using this measure, I derive a generalized exponential certainty equivalent, which nests ...

While incomplete models are desirable due to their robustness to misspecification, they cannot be used to conduct full information exercises i.e. counterfactual experiments and predictions. Moreover, the performance of the ...