Dependence modelling and estimation is a key issue in the assessment of portfolio risk. When measuring extreme risk in terms of the Value-at-Risk, the multivariate normal model with linear correlation as its natural dependence measure is by no means an ideal model. We suggest a large class of...

Model-based small area predictors are derived under the assumption that data files are complete. In application to real data, files may contain missing values. We introduce a variant of the bivariate Fay-Herriot model that takes into account for missing values in one component of the target...

Dependence modelling and estimation is a key issue in the assessment of portfolio risk. When measuring extreme risk in terms of the Value-at-Risk, the multivariate normal model with linear correlation as its natural dependence measure is by no means an ideal model. We suggest a large class of...

Many global shocks, including the renegotiation of NAFTA, the United States-China trade war, the Brexit, and the COVID-19 pandemic, may have recently influenced the inflation spillover in the G7 countries. The current literature overlooks the influence of these important events on the inflation...

The field of computational finance is evolving ever faster. This book collects a number of novel contributions on the use of computational methods and techniques for modelling financial asset prices, returns, and volatility, and on the use of numerical methods for pricing, hedging, and risk...

The bivariate Fay–Herriot model is an area-level linear mixed model that can be used for estimating the domain means of two correlated target variables. Under this model, the dependent variables are direct estimators calculated from survey data and the auxiliary variables are true domain means...