The problem of robust estimation and multivariate outlier detection of the term structure of default intensity is considered. Both the multivariate Vasicek and CIR models, embedding the Kalman filter algorithm in a forward search context, are used to estimate default intensity. The focus is not...

Model risk is investigated from a commercial banking viewpoint. We firstly analyze model misspecification. Then, the focus shifts towards model sensitivity. Finally, interactions among various models are scrutinized. Our overarching goal is to derive a distribution of indicators for summarizing...

In the last few years, copulas have been widely applied in many field of studies. Concentrating our attention on financial applications, we pursue the goal to detect multivariate atypical observations by extending to elliptical copulas the forward search originally introduced in linear and...

In this paper we tackle the problem of outlier detection in data envelopment analysis (DEA). We propose a procedure where we merge the super-efficiency DEA and the forward search. Since DEA provides efficiency scores which are not parameters to fit the model to the data, we introduce a distance,...

Stress testing has become an important topic in banking practice since the development of the risk management and the enforcement of international supervisory requirements. While, in the context of credit risk, the regulatory perspective is mainly focused on stressing risk parameters, we propose...

In the last few years, according to the evolution of financial markets and the enforcement of international supervisory requirements, an increasing interest has been devoted to risk integration. The original focus on individual risk estimation has been replaced by the growing prominence of...

Since the 2007-09 crisis, increasing attention has been devoted to capital adequacy and balance sheet integrity. Banks have been required to improve the quality of their own funds, strengthen their liquidity structure, and enforce their risk management processes. This paper serves the purpose of...

Cointegration analysis is particularly sensitive to outlying observations. Traditional robust approaches rely on parameter estimates based on weighting schemes to penalize aberrant units. This, in particular, is the idea underlying pseudo maximum likelihood (PML) robust estimators. Atypical...

We address the problem of seasonal adjustment of a nonlinear transformation of the original time series, such as the Box-Cox transformation of a time series measured on a ratio scale, or the Aranda-Ordaz transformation of proportions, which aims at enforcing two essential features: additivity...

We develop a $C_{p}$ statistic for the selection of regression models with stationary and nonstationary ARIMA error term. We derive the asymptotic theory of the maximum likelihood estimators and show they are consistent and asymptotically Gaussian. We also prove that the distribution of the sum...