In the context of the multivariate Normal regression model, a mean squared error of prediction is developed for making the choice of subset of explanatory variables for predicting the response variable in future samples

We provide evidence suggesting that the assumption on the probability distribution for return innovations is more influential for Value at Risk (VaR) performance than the conditional volatility specification. We also show that some recently proposed asymmetric probability distributions and the...

The study of dependence between random variables is the core of theoretical and applied statistics. Static and dynamic copula models are useful for describing the dependence structure, which is fully encrypted in the copula probability density function. However, these models are not always able...

Binary events are involved in many economic decision problems. In recent years, considerable progress has been made in diverse disciplines in developing models for forecasting binary outcomes. We distinguish between two types of forecasts for binary events that are generally obtained as the...

This study investigates the role of probability distribution in forecasting volatility and Value-at-Risk (VaR). We use the Realized GARCH model and high-frequency data from the cryptocurrency market and show that the role of probability distribution varies across different situations. A skewed-t...

This study provides empirical evidence for the efficacy of deriving firms' earnings forecasts from predictions of the complete, conditional probability density function (pdf). Relative to cross-sectional earnings forecasts based on OLS regressions, improvements of accuracy, bias and measures for...

Generalized additive models for location, scale and shape define a flexible, semi-parametric class of regression models for analyzing insurance data in which the exponential family assumption for the response is relaxed. This approach allows the actuary to include risk factors not only in the...