New Economics Papers
on Risk Management
Issue of 2009‒01‒10
five papers chosen by

  1. Measuring financial risk : comparison of alternative procedures to estimate VaR and ES By Maria Rosa Nieto; Esther Ruiz
  2. Stress testing credit risk: a survey of authorities' approaches By Antonella Foglia
  3. Ein Analyseraster zur Bestimmung langfristiger Wechselkursrisiken von Unternehmen dargestellt am Beispiel der US-Dollar-Abwertung By Bleuel, Hans-H.
  4. An Empirical Model of Subprime Mortgage Default From 2000 to 2007 By Patrick Bajari; Chenghuan Sean Chu; Minjung Park
  5. A multivariate generalized independent factor GARCH model with an application to financial stock returns By Antonio Garcia-Ferrer; Ester Gonzalez-Prieto; Daniel Pena

  1. By: Maria Rosa Nieto; Esther Ruiz
    Abstract: We review several procedures for estimating and backtesting two of the most important measures of risk, the Value at Risk (VaR) and the Expected Shortfall (ES). The alternative estimators differ in the way the specify and estimate the conditional mean and variance and the conditional distribution of returns. The results are illustrated by estimating the VaR and ES of daily S&P500 returns.
    Keywords: Backtesting, Extreme value, GARCH models, Leverage effect
    Date: 2008–12
  2. By: Antonella Foglia (Banca d'Italia)
    Abstract: This paper reviews the quantitative methods used at selected central banks to stress testing credit risk, focusing in particular on the methods used to link macroeconomic drivers of stress with bank specific measures of credit risk (macro stress test). Stress testing credit risk is an essential element of the Basel II Framework; because of their financial stability perspective, central banks and supervisors are particularly interested in quantifying the macro-to-micro linkages and have developed a specific modeling expertise in this field. In assessing current macro stress testing practices, the paper highlights the more recent developments and a number of methodological challenges that may be useful for supervisors in their review process of the banks' stress test models as required by the Basel II Framework. It also contributes to the on-going macroprudential research efforts that aim to integrate macroeconomic oversight and prudential supervision, in the direction of early identification of key vulnerabilities and assessment of macro-financial linkages.
    Keywords: Macro stress testing, financial stability, macro-prudential analysis, credit risk,probability of default
    JEL: E32 E37 G21
    Date: 2008–12
  3. By: Bleuel, Hans-H. (Department of Economics of the Duesseldorf University of Applied Sciences)
    Abstract: The US-Dollar has depreciated noticeably since the beginning of the year 2006. This depreciation changes the competitiveness of nations and corporations. This paper briefly presents the related exchange rate risks. Subsequently, the operating exposure is discussed, as this is the relevant foreign exchange risk in the long-term. A related issue in corporate risk management is to identify and quantify exchange rate risks. In this context a short guideline proposes an applied 3-step analysis. This DIR-Analysis investigates: direct exposures, indirect exposures and enterprise responses to changed fx-rates.
    Keywords: ökonomisches Wechselkursrisiko, ökonomisches Währungsrisiko, Unternehmensplanung, Risikomanagement, Euro, operating exporsure, foreign exchange risk, corporate planning, hedging, simulation
    JEL: E52
    Date: 2008–03
  4. By: Patrick Bajari; Chenghuan Sean Chu; Minjung Park
    Abstract: The turmoil that started with increased defaults in the subprime mortgage market has generated instability in the financial system around the world. To better understand the root causes of this financial instability, we quantify the relative importance of various drivers behind subprime borrowers' decision to default. In our econometric model, we allow borrowers to default either because doing so increases their lifetime wealth or because of short-term budget constraints, treating the decision as the outcome of a bivariate probit model with partial observability. We estimate our model using detailed loan-level data from LoanPerformance and the Case-Shiller home price index. According to our results, one main driver of default is the nationwide decrease in home prices. The decline in home prices caused many borrowers' outstanding mortgage liability to exceed their home value, and for these borrowers default can increase their wealth. Another important driver is deteriorating loan quality: The increase of borrowers with poor credit and high payment to income ratios elevates default rates in the subprime market. We discuss policy implications of our results. Our findings point to flaws in the securitization process that led to the current wave of defaults. Also, we use our model to evaluate alternative policies aimed at reducing the rate of default.
    JEL: G18 G2 G33 R51
    Date: 2008–12
  5. By: Antonio Garcia-Ferrer; Ester Gonzalez-Prieto; Daniel Pena
    Abstract: We propose a new multivariate factor GARCH model, the GICA-GARCH model , where the data are assumed to be generated by a set of independent components (ICs). This model applies independent component analysis (ICA) to search the conditionally heteroskedastic latent factors. We will use two ICA approaches to estimate the ICs. The first one estimates the components maximizing their non-gaussianity, and the second one exploits the temporal structure of the data. After estimating the ICs, we fit an univariate GARCH model to the volatility of each IC. Thus, the GICA-GARCH reduces the complexity to estimate a multivariate GARCH model by transforming it into a small number of univariate volatility models. We report some simulation experiments to show the ability of ICA to discover leading factors in a multivariate vector of financial data. An empirical application to the Madrid stock market will be presented, where we compare the forecasting accuracy of the GICA-GARCH model versus the orthogonal GARCH one.
    Keywords: ICA, Multivariate GARCH, Factor models, Forecasting volatility
    Date: 2008–12

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