Abstract:

Fifty years ago, Professor Edward Altman created the now well established, highly praised by academics and practitioners and much tested Altman Z-Score model for predicting corporate distress and bankruptcy. Over the years, Altman, and others, have developed models for specific industries and in several different countries to enlarge the scope and usage of credit scoring models. Indeed, just about every major bank in the world has a version of the Z-Score model to assess the probability of default and loss given default to conform with requirements of Basel II and III. In his Keynote address, Dr. Altman will explore how his models, including second and third generation models called Z'' for emerging market firms and non-manufacturer industrials (1995), Z-Metrics for global corporates (2009) and his latest model (2016) for Italian mini-bond issuers have been applied to numerous important practical applications and even to assessing sovereign default risk.

One possible application is related to answering the question as to how, if any, has the credit risk profile of high-yield bond issuers changed between issuers just before the great financial crisis of 2008/2009 and today's non-investment grade firms This will be used to assess current conditions and the outlook for global credit markets; another topic that Professor Altman will explore. In his discussion, we will analyze how a scoring model can be transformed into a probability of default assessment.

Abstract:

The business model of investment banks was born together with the quantitative framework for managing derivatives: both were based on hedging with a limited role for capital. Post-crisis RWA regulations started to shake this, and now recent regulations are giving further blows. We will see how Leverage ratio and FRTB penalize hedging further and lead to a new approach to valuation and risk management where P-based KVA plays a fundamental role. This squeezes the profitability of banks, that are looking for solutions in the world of Fintech, from Machine Learning for FRTB to Blockchain Technology. We will see a case study on how blockchain and smart contracts could compress XVAs.

Abstract:

To measure the major risks experienced by financial institutions, for instance Market, Credit and Operational, regarding the risk measures, the distributions used to model them and the level of confidence, the regulation either offers a limited choice or demands the implementation of a particular approach. In this paper, we review, highlight and illustrate the paradoxes and issues observed when implementing an approach over another, the inconsistencies between the methodologies suggested and the problems related to their interpretation. Starting with a discussion on the intrinsic properties of two classical risk measures: the Value-at-Risk and the Expected Shortfall, at each step we illustrate our proposal through an example based on real data using estimates of the risk measures themselves. Thus, this exercise provides practitioners and supervisors with some recommendations to assess, manage and control risks in a financial institution relying on alternative approaches, for instance, spectral, spectrum, distortion and Spatial risk measures.

Abstract:

While scenario analysis and stress testing have been an explicit part of risk management methodologies and systems for over two decades, the typical scenario and stress testing tools are still generally quite static and largely subjective. In this talk, we discuss advanced approaches to create meaningful stress scenarios for risk management and regulatory stress testing, which effectively combine economic forecasts and "expert" views with portfolio simulation methods.

Regulatory and expert scenarios are typically described in terms of a small number of key economic variables or factors. However, when applied to a portfolio, they are incomplete - they generally do not describe what occurs to all relevant market risk and credit risk factors that affect a portfolio. We need to understand how these risk factors behave, conditional on the outcome of the economic factors, and the map this to portfolio losses. We introduce a new approach called Least Squares Stress Testing (LSST). The key insight is that the conditional expectation, and more generally the full conditional distribution of all the factors, and of the portfolio P&L, can be estimated directly from a pre-computed simulation using Least Squares Regression. LSST is a simulation-based conditional scenario generation method that offers many advantages over more traditional analytical methods. Simulation techniques are simple, flexible, and provide very transparent results, which are auditable and easy to explain. LSST can be applied to both market and credit risk stress testing with a large number of risk factors, which can follow completely general stochastic processes, with fat-tails, non-parametric and general codependence structures, autocorrelation, etc. LSST further produces explicit risk factor P&L contributions. From a methodology perspective, we also discuss some of the assumptions the LSST approach, statistical tests to check when these assumptions fail, and remedies that can be applied.

We illustrate the application of the methodology through a couple of real-life examples for market risk and credit risk.

Abstract:

This talk reviews how Policy Uncertainty represents a measurable risk in for economic activity and financial markets. We focus on uncertainty about what policy choices decision-makers will take (for example, how lack of specificity regarding tax policy affect management investment decisions and therefore stock prices) as well as about how particular policy decisions will affect the economy (for example, the extent to which trade sanctions would affect how certain manufacturers can sell their products, and in turn impact the labor force allocation as well as market valuations). We review the available measures of policy uncertainty as well its channels for affecting economic activity and financial markets.

Abstract:

This article analyses the behavior of sovereign debt holders during distress scenarios. We examine the response of non-resident (NRH) debt holders during the last financial crisis. Our database consists of a comprehensive panel of sovereign debt holdings for Spanish Treasury auctions in the primary market from 1997 to 2016. First, we characterize the relationship between Resident Holders (RH) and NRH. The main fi nding suggests a negative time varying conditional correlation between NRH and RH holders. However, this inverse relationship is relaxed during the last financial crises, specially it turns positive for the series of short-term debt. Second, we focus on the crisis period to examine the consequences of the unconventional monetary policies conducted by the ECB on the demand of NRH.

Empirical results provide evidence of the importance of the ECB's intervention in reducing the sovereign demand of NRH.

Abstract:

La reciente crisis financiera ha puesto de manifiesto que existen las enormes relaciones entre los diferentes sistemas o modelos de gestión existentes en la banca. Dos de esos sistemas, aparentemente mundos separados hasta 2008, son el sistema de planificación estratégica y el de gestión de riesgos. Sin embargo, al profundizar en sus bases y metodologías podemos comprobar como la estrategia corporativa y la propensión al riesgo han caminado de la mano. Y ser conscientes de esos puntos de unión, conectar el riesgo y la estrategia corporativa, es esencial, especialmente bajo el nuevo modelo de supervisión que comienza por la revisión y evaluación del modelo de negocio y la estrategia corporativa.