Pierpaolo Uberti

A statistical method to optimize the combination of internal and external data in operational risk measurement

According to the last proposals of the Basel Committee on Banking Supervision, banks are allowed to use the Advanced Measurement Approach (AMA) option for the computation of their capital charge covering operational risks. Among these methods, the Loss Distribution Approach (LDA) is the most sophisticated (see Frachot et al (2001) and Baud et al (2002)). It is widely recognized that calibration on internal data may not suffice for computing an accurate capital charge against operational risk. In other words, internal data should be supplemented with external data. The goal of this paper is to address issues regarding the optimal way to mix internal and external data with regards to frequency and severity. As a result rigorous statistical treatments are required to make internal and external data comparable and to ensure that merging both databases leads to unbiased estimates. We propose a rigorous way to tackle this issue through a statistically optimized methodology.

How to measure single-name credit risk concentrations

Credit risk concentration is one of the leading topics in modern finance, as the bank regulation has made increasing use of external and internal credit ratings. Concentration risk in credit portfolios comes into being through an uneven distribution of bank loans to individual borrowers (single-name concentration) or in a hierarchical dimension such as in industry and services sectors and geographical regions (sectorial concentration). To measure single-name concentration risk the literature proposes specific concentration indexes such as the Herfindahl-Hirschman index, the Gini index or more general approaches to calculate the appropriate economic capital needed to cover the risk arising from the potential default of large borrowers. However, in our opinion, the Gini index and the Herfindahl-Hirschman index can be improved taking into account methodological and theoretical issues which are explained in this paper. We propose a new index to measure single-name credit concentration risk and we prove the properties of our contribution. Furthermore, considering the guidelines of Basel II, we describe how our index works on real financial data. Finally, we compare our index with the common procedures proposed in the literature on the basis of simulated and real data.

A threshold based approach to merge data in financial risk management

According to the last proposals by the Basel Committee, banks are allowed to use statistical approaches for the computation of their capital charge covering financial risks such as credit risk, market risk and operational risk. It is widely recognized that internal loss data alone do not suffice to provide accurate capital charge in financial risk management, especially for high-severity and low-frequency events. Financial institutions typically use external loss data to augment the available evidence and, therefore, provide more accurate risk estimates. Rigorous statistical treatments are required to make internal and external data comparable and to ensure that merging the two databases leads to unbiased estimates. The goal of this paper is to propose a correct statistical treatment to make the external and internal data comparable and, therefore, mergeable. Such methodology augments internal losses with relevant, rather than redundant, external loss data.

Google Searches for Portfolio Management: A Risk and Return Analysis

Google search data has proven to be useful in portfolio management. The basic idea is that high search volumes are related to bad news and risk increase. This paper shows additional evidence about the use of Google search volumes in risk management, for the Standard & Poor Industrial index components, from 2004 to 2017. To overcome the (time-series and cross-section) limitations Google imposes on the data download, a re-normalization procedure is presented, to obtain a multivariate sample of volumes which preserve their relative magnitude. The results indicate that the volumes’ normalization and the starting portfolio are decisive for the portfolio performances. Correctly normalized Google search volumes yield poor results. This may lead to revise the interpretation of the search volume: it can be considered a risk indicator, but when used in a equally risk contribution portfolio, no evidence of the improvement of the risk-return performances is found.

Somatic Portfolio Theory: When Emotions Lead to Economic Efficiency

This paper offers a theoretical generalization of the mean-variance theory (MVT) by integrating the ’expected returns/risk’ rule with variables that measure emotions. We validate its accuracy using a psycho-physiological experiment with a sample of 645 individuals who were asked to take portfolio decisions in a laboratory setting. Results show that MVT frequently fails to describe investor behavior. We obtain evidence that individuals actually take efficient portfolio choices, but only when emotions are added to the equation.This paper shows that by merging theories of rational choice and evidence of emotions, the authentic human decision process can be described and predicted.

Concentration Measures in Risk Management

Following the increasing use of external and internal credit ratings made by the Bank regulation, credit risk concentration has become one of the leading topics in modern finance. In order to measure separately single-name and sectoral concentration risk, the literature proposes specific concentration indexes and models, which we review in this paper. Following the guideline proposed by Basel 2 on risk integration, we believe that standard approaches could be improved by studying a new measure of risk that integrates single-name and sectoral credit risk concentration in a coherent way. The main objective of this paper is to propose a novel index useful to measure credit risk concentration integrating single-name and sectoral components. From a theoretical point of view, our measure of risk shows interesting mathematical properties; empirical evidences are given on the basis of a data set. Finally, we have compared the results achieved following our proposal with respect to the common procedures proposed in the literature.

Model Assessment for Predictive Classification Models

In this article, we present a novel methodology to assess predictive models for a binary target. In our opinion, the main weakness of the criteria proposed in the literature is not to take the financial costs of a wrong decision into account. The objective of this article is to derive the optimal cut-off in predictive classification models and to improve model assessment on the basis of a general class of loss functions. We describe how our proposal performs in a real application on credit scoring.