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Now showing items 1 - 15 of 15

  • Dynamic Dependence in Corporate Credit: Peter Christo¤ersen, Kris Jacobs, Xisong Jin and Hughes Langlois

    Andrew Patton  

    A very interesting paper:1 A large and relatively novel data set2 A new dynamic copula model, the largest to date3 Interesting contrasts between the dependence between CDS spreadsand dependence between corresponding equity returns
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  • Investment Funds' Vulnerabilities: A Tail-risk Dynamic CIMDO Approach

    Xisong Jin   Francisco Nadal De Simone  

    This study applies to investment funds a novel framework which combines marginal probabilities of distress estimated from a structural credit risk model with the consistent information multivariate density optimization (CIMDO) methodology of Segoviano, and the generalized dynamic factor model (GDFM). The framework models investment funds' distress dependence explicitly and captures the time-varying non-linearities and feedback effects typical of financial markets. It measures investment funds systemic credit risk in three forms: (1) credit risk common to all funds within each of the seven categories the Eurosystem reports to the ECB; (2) credit risk in each category of investment fund conditional on distress on another category of investment fund and; (3) the buildup of investment funds' vulnerabilities over time which may unravel disorderly. In addition, the estimates of the common components of the investment funds' distress measures contain early warning features, and the identification of their drivers is useful for macroprudential policy. As a result, this framework contributes to making macroprudential policy operational
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  • BANKING SYSTEMIC VULNERABILITIES: A TAIL-RISK DYNAMIC CIMDO APPROACH

    Xisong Jin   Francisco Nadal De Simone  

    This study proposes a novel framework which combines marginal probabilities of default estimated from a structural credit risk model with the consistent information multivariate density optimization (CIMDO) methodology of Segoviano, and the generalized dynamic factor model (GDFM) supplemented by a dynamic t-copula. The framework models banks' default dependence explicitly and captures the time-varying non-linearities and feedback effects typical of financial markets. It measures banking systemic credit risk in three forms: (1) credit risk common to all banks; (2) credit risk in the banking system conditional on distress on a specific bank or combinations of banks and; (3) the buildup of banking system vulnerabilities over time which may unravel disorderly. In addition, the estimates of the common components of the banking sector short-term and conditional forward default measures contain early warning features, and the identification of their drivers is useful for macroprudential policy. Finally, the framework produces robust out- of-sample forecasts of the banking systemic credit risk measures. This paper advances the agenda of making macroprudential policy operational.
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  • Does the GARCH Structural Credit Risk Model Make a Difference?

    Xisong Jin   Thorsten Lehnert   Francisco Nadal de Simone  

    In this study, we empirically investigate and evaluate various approaches to structurally assess credit risk using a panel of European banking groups. We consider not only the standard approaches in the literature, but also include models that allow the asset volatility to be stochastic and models that allow for short- and long-term components of default risk. Models are evaluated by comparing their ability to correctly and timely identify changes in risk indicators. Surprisingly, we find that the GARCH structural credit risk model, despite its more sophisticated modeling approach, typically underperforms more basic models. Importantly for macro-prudential policy, the combined Merton/GARCH-MIDAS model performs best and reflects important market events earlier than the other approaches.
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  • A Framework for Tracking Changes in the Intensity of Investment Funds' Systemic Risk

    Xisong Jin   Francisco Nadal De Simone  

    This study applies to investment funds a novel framework which combines marginal probabilities of distress estimated from a structural credit risk model with the consistent information multivariate density optimization (CIMDO) methodology and the generalized dynamic factor model (GDFM). The framework models investment funds' distress dependence explicitly and captures the time-varying non-linearities and feedback effects typical of financial markets. It measures investment funds systemic credit risk in three forms: (1) credit risk common to all funds within each of the seven categories the Eurosystem reports to the ECB; (2) credit risk in each category of investment fund conditional on distress on another category of investment fund and; (3) the buildup of investment funds' vulnerabilities over time which may unravel disorderly. In addition, the estimates of the common components of the investment funds' distress measures contain early warning features, and the identification of their drivers is useful for macroprudential policy. The ranking of drivers of those common components in terms of importance differs from the ranking of the drivers of the common components of marginal measures of distress. This framework contributes to making macroprudential policy operational.
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  • Large Portfolio Risk Management and Optimal Portfolio Allocation with Dynamic Copulas

    Xisong Jin   Thorsten Lehnert  

    Previous research focuses on the importance of modeling the multivariate distribution for optimal portfolio allocation and active risk management. However, available dynamic models are not easily applied for high-dimensional problems due to the curse of dimensionality. In this paper, we extend the framework of the Dynamic Conditional Correlation/Equicorrelation and an extreme value approach into a series of Dynamic Conditional Elliptical Copulas. We investigate risk measures like Value at Risk (VaR) and Expected Shortfall (ES) for passive portfolios and dynamic optimal portfolios through Mean-Variance and ES criteria for a sample of US stocks over a period of 10 years. Our results suggest that (1) Modeling the marginal distribution is important for the dynamic high dimensional multivariate models. (2) Neglecting the dynamic dependence in the copula causes over-aggressive risk management. (3) TheDCC/DECO Gaussian copula and t-copula work very well for both VaR and ES. (4)Grouped t-copula and t-copula with dynamic degrees of freedom further match the fat tail. (5) Correctly modeling dependence structure makes an improvement in portfolio optimization against the tail risk. (6) Models driven by multivariate t innovations with exogenously given degrees of freedom provide a. exible and applicable alternative for optimal portfolio risk management.
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  • The Conditional Dynamic Dependence between Herding and Return: Evidence from US Equity Market?

    Xisong Jin   Ya Tang  

    The literature has focused on how herding behavior a¤ects security prices. Surprisingly, the dynamic correlation between herding and return has not been examined. This paper provides a comprehensive empirical analysis of the conditional dynamic dependence between stock return and herding behavior. By using the statistical herding measure developed by Lakonishok et al (1992) and the ?exible dynamic correlation and equicorrelation techniques (Engle, 2002, Engle and Kelly, 2008) in the framework of Gaussian Copula, we... nd that (1) the level of herding is high in volatile downside markets and moderate in periods of low volatility; (2) the signi... cantly positive herding correlation implies a systematic risk factor in herding behavior; (3) herding behavior is less contagious in a stable market; (4) market herding level is positively correlated with market return; (5) herding behavior drives return co-movement in the housing bubble period. In addition, we... nd that the... rm size e¤ect plays an important role in understanding the above dynamic dependences.
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  • Large Portfolio Risk Management with Dynamic Copulas

    Xisong Jin  

    Modeling the dynamic high-dimensional multivariate distribution is very useful for active risk management and optimal portfolio allocation; however, available dynamic models are not easily applied for high-dimensional problems due to the curse of dimensionality. In the light of the recent development of multivariate GARCH techniques for a large number of underlying securities, I extend the framework of the Dynamic Conditional Correlation/Equicorrelation (DCC/DECO) (Engle, 2002 and Engle and Kelly, 2008) and an extreme value approach (Mc- Neil and Frey, 2000) into a series of Dynamic Conditional Elliptical Copulas. By constructing portfolios of 89 stocks from CDX-listed... rms between 1995 and 2005, I examine Value at Risk (VaR) and Expected Shortfall (ES) by Monte Carlo simulation for passive portfolios and dynamic optimal portfolios through Mean-Variance and ES criteria. I... nd: (1) Modeling the marginal distribution is important for the dynamic high-dimensional multivariate models. (2) Neglecting the dynamic dependence in the copula causes over-aggressive risk management. (3) The DCC/DECO Gaussian copula and t-copula work very well for both VaR and ES. The DCC copula is necessary for value-weighted portfolios. For equally-weighted portfolios, the DECO copula performs about as well as the DCC copula. (4) Grouped t-copula and t-copula with dynamic degrees of freedom further match the fat tail. (5) Correctly modeling dependence structure makes an improvement in portfolio optimization. The optimal portfolio by ES does a good job against the tail risk in both DECO and DCC copulas. (6) Since portfolio optimiza- tion induces statistical error maximization, the assumption of multivariate t innovations with exogenously given degrees of freedom provides a ?exible and applicable method for optimal portfolio risk management.
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  • The Conditional Dynamic Dependences between Herding and Return: Evidences from US Equity Market?

    Xisong Jin   Ya Tang  

    The literature has been interested in how herding behavior a¤ects security prices. Sur- prisingly, the dynamic correlation between return and herding has not been examined. This paper provides a comprehensive empirical analysis of conditional dynamic dependences among herding level, herding contagions, herding volatility, stock return, return comovement, and return volatility. This is conducted by joining the statistical herding measure developed by Lakonishok et al. (1992) and the extended ?exible dynamic correlation and equicorrelation techniques (Engle (2002), Engle and Kelly (2008)) in the framework of Gaussian copula. We …nd that (1) while the level of herding is considerable in a volatile downside market, it is moderate in a unwavering market; (2) herding behavior is more contagious in a less stable market; (3) market herding level is positively and signi…cantly correlated with market return, with an upward trend in a bull market and downward trend in a bear market; (4) contagions of herding and return comovement are negatively correlated. In addition, we …nd that the size e¤ect is important in understanding the above dynamic dependences.
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  • MARKET- AND BOOK-BASED MODELS OF PROBABILITY OF DEFAULT FOR DEVELOPING MACROPRUDENTIAL POLICY TOOLS

    XISONG JIN   FRANCISCO NADAL DE SIMONE  

    The recent financial crisis raised awareness of the need for a framework for conducting macroprudential policy. Identifying as early as possible and addressing the buildup of endogenous imbalances, exogenous shocks, and contagion from financial markets, market infrastructures, and financial institutions are key elements of a sound macroprudential framework. This paper contributes to this literature by estimating several models of default probability, two of which relax two key assumptions of the Merton model: the assumption of constant asset volatility and the assumption of a single debt maturity. The study uses market and banks' balance sheet data. It finds that systemic risk in Luxembourg banks, while mildly correlated with that of European banking groups, did not increase as dramatically as it did for the European banking groups during the heights of the financial crisis. In addition, it finds that systemic risk has declined during the second half of 2010, both for the banking groups as well as for the Luxembourg banks. Finally, this study illustrates how models of default probability can be used for event-study purposes, for simulation exercises, and for ranking default probabilities during a period of distress according to banks' business lines. As such, this study is a stepping stone toward developing an operational framework to produce quantitative judgments on systemic risk and financial stability in Luxembourg.
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  • An Early-warning and Dynamic Forecasting Framework of Default Probabilities for the Macroprudential Policy Indicators Arsenal

    Xisong Jin   Francisco Nadal De Simone  

    The estimation of banks' marginal probabilities of default using structural credit risk models can be enriched incorporating macro-financial variables readily available to economic agents. By combining Delianedis and Geske's model with a Generalized Dynamic Factor Model into a dynamic t-copula as a mechanism for obtaining banks' dependence, this paper develops a framework that generates an early warning indicator and robust out-of-sample forecasts of banks' probabilities of default. The database comprises both a set of Luxembourg banks and the European banking groups to which they belong. The main results of this study are, first, that the common component of the forward probability of banks' defaulting on their long-term debt, conditional on not defaulting on their short-term debt, contains a significant early warning feature of interest for an operational macroprudential framework driven by economic activity, credit and interbank activity. Second, incorporating the common and the idiosyncratic components of macro-financial variables improves the analytical features and the out-of-sample forecasting performance of the framework proposed.
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  • Correlation dynamics and international diversification benefits

    Peter Christoffersen   Vihang Errunza   Kris Jacobs   Xisong Jin  

    Abstract Forecasting the evolution of security co-movements is critical for asset pricing and portfolio allocation. Hence, we investigate patterns and trends in correlations over time using weekly returns for developed markets (DMs) and emerging markets (EMs) over the period 1973–2012. We show that it is possible to model co-movements for many countries simultaneously using BEKK, DCC, and DECO models. Empirically, we find that correlations have trended upward significantly for both DMs and EMs. Based on a time-varying measure of diversification benefits, we find that it is not possible to circumvent the increasing correlations in a long-only portfolio by adjusting the portfolio weights over time. However, we do find some evidence that adding EMs to a DM-only portfolio increases diversification benefits.
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  • Banking SyStemic VulneraBilitieS: a tail-riSk Dynamic cimDO apprOach

    cahier D’étuDeS   Xisong JiN   Francisco NAdAL de SimoNe   Xisong Jin   Francisco Nadal De Simone  

    This study proposes a novel framework which combines marginal probabilities of default estimated from a structural credit risk model with the consistent information multivariate density optimization (CIMDO) methodology of Segoviano, and the generalized dynamic factor model (GDFM) supplemented by a dynamic t-copula. The framework models banks’ default dependence explicitly and captures the time-varying non-linearities and feedback effects typical of financial markets. It measures banking systemic credit risk in three forms: (1) credit risk common to all banks; (2) credit risk in the banking system conditional on distress on a specific bank or combinations of banks and; (3) the buildup of banking system vulnerabilities over time which may unravel disorderly. In addition, the estimates of the common components of the banking sector short-term and conditional forward default measures contain early warning features, and the identification of their drivers is useful for macroprudential policy. Finally, the framework produces robust outof-sample forecasts of the banking systemic credit risk measures. This paper advances the agenda of making macroprudential policy operational. JEL Classification: C30, E44, G1
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  • A framework for tracking changes in the intensity of investment funds' systemic risk

    Xisong Jin   Francisco Nadal De Simone  

    Abstract This study applies to investment funds a novel framework which combines marginal probabilities of distress estimated from a structural credit risk model with the consistent information multivariate density optimization (CIMDO) methodology and the generalized dynamic factor model (GDFM). The framework models investment funds' distress dependence explicitly and captures the time-varying non-linearities and feedback effects typical of financial markets. It measures investment funds' systemic credit risk in three forms: (1) credit risk common to all funds within each of the seven categories the Eurosystem reports to the ECB; (2) credit risk in each category of investment fund conditional on distress on another category of investment fund and; (3) the buildup of investment funds' vulnerabilities over time which may unravel disorderly. In addition, the estimates of the common components of the investment funds' distress measures contain early warning features, and the identification of their drivers is useful for macroprudential policy. The ranking of drivers of those common components in terms of importance differs from the ranking of the drivers of the common components of marginal measures of distress. This framework contributes to the formulation of macroprudential policy. Highlights • First study about the systemic credit risk in all types of investment funds (IF) • Contains a number of methodological features not applied to research on IF so far • Allows the estimation of measures of systemic credit risk for IF • Provides a structural early-warning measure of systemic vulnerabilities' build-up • Contributes to making macroprudential policy operational
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  • Euro at risk: The impact of member countries' credit risk on the stability of the common currency

    Lamia Bekkour   Xisong Jin   Thorsten Lehnert   Fanou Rasmouki   Christian Wolff  

    Abstract In this paper, we propose a new indicator of Euro stability. We make use of this new indicator and empirically investigate the impact of changes in sovereign risk of Eurozone member countries on the stability of the Euro. The stability of the Euro is proxied by decomposing Dollar–Euro exchange rate options into the moments of the risk-neutral distribution. Our stability measure can nicely separate periods of Dollar instability (the subprime crisis period) and Euro instability (the sovereign debt crisis period). In particular, we document that only during the sovereign debt crisis, changes in the creditworthiness of member countries with vulnerable fiscal positions have a significant impact on the stability of the common currency. Interestingly, however, the market perceives Greece not to be ‘systemically relevant’. Highlights • First systematic study about the Eurozone debt crisis through the twin lenses of the CDS and the currency option markets • Documents the impact of the credit risk of Eurozone member countries on the stability of the Euro • Proposes a new indicator of currency stability • Contributes to making macroprudential policy operational
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