nep-rmg New Economics Papers
on Risk Management
Issue of 2012‒11‒03
sixteen papers chosen by
Stan Miles
Thompson Rivers University

  1. Using Shapley’s asymmetric power index to measure banks’ contributions to systemic risk By Garratt, Rodney; Webber, Lewis; Willison, Matthew
  2. Optimal portfolios with minimum capital requirements. By Santos, André A. P.; Nogales, F. Javier; Ruiz, Esther; Dijk, Dick van
  3. Vigilant Measures of Risk and the Demand for Contingent Claims By Mario Ghossoub
  4. Enhanced Decision Support in Credit Scoring Using Bayesian Binary Quantile Regression By V. L. MIGUÉIS; D. F. BENOIT; D. VAN DEN POEL
  5. Ranking Systemically Important Financial Institutions By Mardi Dungey; Mattéo Luciani; David Veredas
  6. CVA, Wrong Way Risk, Hedging and Bermudan Swaption By Boukhobza, Ali; Maetz, Jerome
  7. Estimating bank loans loss given default by generalized additive models By Raffaella Calabrese
  8. A Macroprudential Framework for Monitoring and Examining Financial Soundness By Albert, Jose Ramon G.; Schou-Zibell, Lotte; Song, Lei Lei
  9. Estimating Conditional Average Treatment Effects By Jason Abrevaya; Yu-Chin Hsu; Robert P. Lieli
  10. Systemic Importance Index for financial institutions: A Principal Component Analysis approach By Carlos León; Andrés Murcia
  11. Realized mixed-frequency factor models for vast dimensional covariance estimation By Bannouh, K.; Martens, M.P.E.; Oomen, R.C.A.; Dijk, D.J.C. van
  12. The Volatility-Return Relationship: Insights from Linear and Non-Linear Quantile Regressions By D.E. Allen; Abhay K Singh; R. Powell; Michael McAleer; James Taylor; Lyn Thomas
  13. Stability analysis of financial contagion due to overlapping portfolios By Fabio Caccioli; Munik Shrestha; Cristopher Moore; J. Doyne Farmer
  14. The Index of the Financial Safety (IFS) of South Africa and Bayesian Estimates for IFS Vector-Autoregressive Model By Matkovskyy, Roman
  15. Estimation of Multivariate Stochastic Volatility Models: A Comparative Monte Carlo Study By Mustafa Hakan Eratalay
  16. Assessing the Resilience of ASEAN Banking Systems: the Case of the Philippines By Albert, Jose Ramon G.; Ng, Thiam Hee

  1. By: Garratt, Rodney (University of California, Santa Barbara); Webber, Lewis (Bank of England); Willison, Matthew (Bank of England)
    Abstract: An individual bank can put the whole banking system at risk if its losses in response to shocks push losses for the system as a whole above a critical threshold. We determine the contribution of banks to this systemic risk using a generalisation of the Shapley value; a concept originating in co-operative game theory. An important feature of this approach is that the order in which banks fail in response to a shock depends on the composition of the banks’ asset portfolios and capital buffers. We show how these factors affect banks’ contributions to systemic risk, and the extent to which these contributions depend on the level of the critical threshold.
    Keywords: Shapley value; systemic risk; bank regulation
    JEL: C71 G01 G21 G28
    Date: 2012–10–26
  2. By: Santos, André A. P.; Nogales, F. Javier; Ruiz, Esther; Dijk, Dick van
    Abstract: We propose a novel approach to active risk management based on the recent Basel II regulations to obtain optimal portfolios with minimum capital requirements. In order to avoid regulatory penalties due to an excessive number of Value-at-Risk (VaR) violations, capital requirements are minimized subject to a given number of violations over the previous trading year. Capital requirements are based on the recent Basel II amendments to account for the ‘stressed’ VaR, that is, the downside risk of the portfolio under extreme adverse market conditions. An empirical application for two portfolios involving different types of assets and alternative stress scenarios demonstrates that the proposed approach delivers an improved balance between capital requirement levels and the number of VaR exceedances. Furthermore, the riskadjusted performance of the proposed approach is superior to that of minimum-VaR and minimumstressed VaR portfolios.
    Keywords: Convex optimization; Multivariate GARCH; Out-of-sample evaluation; Stress testing;
    JEL: G11 G32
    Date: 2012
  3. By: Mario Ghossoub
    Abstract: I examine a class of utility maximization problems with a not necessarily law-invariant utility, and with a not necessarily law-invariant risk measure constraint. The objective function is an integral of some function U with respect to some probability measure P, and the constraint set contains some risk measure constraint which is not necessarily P-law-invariant. This introduces some heterogeneity in the perception of uncertainty. The primitive U is a function of some given underlying random variable X and of a contingent claim Y on X. Many problems in economic theory and financial theory can be formulated in this manner, when a heterogeneity in the perception of uncertainty is introduced. Under a consistency requirement on the risk measure that will be called Vigilance, supermodularity of the primitive U is sufficient for the existence of optimal continent claims, and for these optimal claims to be comonotonic with the underlying random variable X. Vigilance is satisfied by a large class of risk measures, including all distortion risk measures. An explicit characterization of an optimal contingent claim is also provided in the case where the risk measure is a convex distortion risk measure.
    Keywords: Contingent Claims, Heterogeneous Beliefs, Choquet Integral, Vigilance, Monotone Likelihood Ratio JEL Classification Numbers: C02, D81, D89, G11
    Date: 2012–10–22
    Abstract: Fierce competition as well as the recent financial crisis in financial and banking industries made credit scoring gain importance. An accurate estimation of credit risk helps organizations to decide whether or not to grant credit to potential customers. Many classification methods have been suggested to handle this problem in the literature. This paper proposes a model for evaluating credit risk based on binary quantile regression, using Bayesian estimation. This paper points out the distinct advantages of the latter approach: that is (i) the method provides accurate predictions of which customers may default in the future, (ii) the approach provides detailed insight into the effects of the explanatory variables on the probability of default, and (iii) the methodology is ideally suited to build a segmentation scheme of the customers in terms of risk of default and the corresponding uncertainty about the prediction. An often studied dataset from a German bank is used to show the applicability of the method proposed. The results demonstrate that the methodology can be an important tool for credit companies that want to take the credit risk of their customer fully into account.
    Keywords: Credit Scoring, Quantile regression, Classification, Bayesian estimation, Markov Chain Monte Carlo
    Date: 2012–08
  5. By: Mardi Dungey; Mattéo Luciani; David Veredas
    Abstract: We propose a simple network–based methodology for ranking systemically importantfinancial institutions. We view the risks of firms –including both the financial sectorand the real economy– as a network with nodes representing the volatility shocks. Themetric for the connections of the nodes is the correlation between these shocks. Dailydynamic centrality measures allow us to rank firms in terms of risk connectedness and firmcharacteristics. We present a general systemic risk index for the financial sector. Resultsfrom applying this approach to all firms in the S&P500 for 2003–2011 are twofold. First,Bank of America, JP Morgan and Wells Fargo are consistently in the top 10 throughoutthe sample. Citigroup and Lehman Brothers also were consistently in the top 10 up tolate 2008. At the end of the sample, insurance firms emerge as systemic. Second, thesystemic risk in the financial sector built–up from early 2005, peaked in September 2008,and greatly reduced after the introduction of TARP and the rescue of AIG. Anxiety aboutEuropean debt markets saw the systemic risk begin to rise again from April 2010. Wefurther decompose these results to find that the systemic risk of insurance and deposit–taking institutions differs importantly, the latter experienced a decline from late 2007, inline with the burst of the housing price bubble, while the former continued to climb upto the rescue of AIG
    Keywords: systemic risk; ranking; financial institutions; Lehman
    JEL: G10 G18 G20 G32 G38
    Date: 2012–10
  6. By: Boukhobza, Ali; Maetz, Jerome
    Abstract: “Roughly two-thirds of credit counterparty losses were due to credit valuation adjustment losses and only one-third were due to actual defaults” according to the Basel Committee on Banking Supervision, highlighting the importance of counterparty credit risk management to the derivatives contracts. Today, managing counterparty credit risk has become an integrated part of many derivative trading desks’ day-to-day activities and the need of accurate pricing, efficient hedging strategies and practical proxies has become critical. As a result, banks have sharpened their CVA pricing and modeling infrastructure and most have a dedicated trading desk dynamically hedging their CVA. However, if pricing techniques have become standard over the past few years, the expected positive exposure (EPE) modeling is usually not taking into account the embedded correlation between the counterparty and underlying market movements. This correlation known as wrong way risk can substantially affect the price and the related hedging strategy and is the main focus of this article.
    Keywords: CVA, Credit Valuation Adjustment, WWR, Wrong Way Risk, Hedging, Swap, Bermudan Swaption, EPE, Expected Positive Exposure
    JEL: A10
    Date: 2012–08
  7. By: Raffaella Calabrese (University of Milano-Bicocca)
    Abstract: With the implementation of the Basel II accord, the development of accurate loss given default models is becoming increasingly important. The main objective of this paper is to propose a new model to estimate Loss Given Default (LGD) for bank loans by applying generalized additive models. Our proposal allows to represent the high concentration of LGDs at the boundaries. The model is useful in uncovering nonlinear covariate effects and in estimating the mean and the variance of LGDs. The suggested model is applied to a comprehensive survey on loan recovery process of Italian banks. To model LGD in downturn conditions, we include macroeconomic variables in the model. Out-of-time validation shows that our model outperforms popular models like Tobit, decision tree and linear regression models for different time horizons.
    Keywords: downturn LGD, generalized additive model, Basel II
    Date: 2012–10–22
  8. By: Albert, Jose Ramon G.; Schou-Zibell, Lotte; Song, Lei Lei
    Abstract: This paper describes concepts and tools behind macroprudential monitoring and the growing importance of macroprudential tools for assessing the stability of financial systems. This paper also employs a macroprudential approach in examining financial soundness and identifying its determinants. Using data from selected developing economies in Asia, South America, and Europe as well as selected economies from the developed world, panel regressions are estimated to quantify the impacts of the major influences on key financial soundness indicators, including capital adequacy, asset quality, and earnings and profitability.
    Keywords: early warning system, banking regulation, macroprudential, banks, banking crises, banking supervision, stress testing
    Date: 2012
  9. By: Jason Abrevaya; Yu-Chin Hsu; Robert P. Lieli
    Abstract: This paper studies the effect of hedge-fund trading on idiosyncratic risk. We hypothesize that while hedge-fund activity would often reduce idiosyncratic risk, high initial levels of idiosyncratic risk might be further amplified due to fund loss limits. Panel-regression analyses provide supporting evidence for this hypothesis. The results are robust to sample selection and are further corroborated by a natural experiment using the Lehman bankruptcy as an exogenous adverse shock to hedge-fund trading. Hedge-fund capital also explains the increased idiosyncratic volatility of high-idiosyncratic-volatility stocks as well as the decreased idiosyncratic volatility of low-idiosyncratic-volatility stocks over the past few decade.
    Date: 2012–07–20
  10. By: Carlos León; Andrés Murcia
    Abstract: As a result of the most recent global financial crisis literature has embraced size, connectedness and substitutability as key indicators for financial institutions’ systemic importance. Despite the intuitiveness of these concepts, identifying systemic important institutions remain a non-trivial task that implies two primary challenges. First, designing metrics for connectedness and substitutability may require, as acknowledged by literature, non-standard data sources and techniques. Second, choosing a methodology capable of aggregating the metrics designed for the three aforementioned concepts into a systemic importance index may be intricate. The herein paper addresses the second challenge. The chosen approach is to apply Principal Components Analysis to the metrics designed by León and Machado (2011) for assessing size, connectedness and substitutability, where those metrics rely on a combination of balance sheet data and the application of network theory to large-value payment system’s information. Results (i) demonstrate that the three concepts and their metrics are explanatory and non-redundant for differentiating financial institutions’ relative systemic importance; (ii) allow for constructing a PCA-based Systemic Importance Index, a valuable tool for financial authorities’ policy and decision-making; and (iii) confirm the importance of the too-connected-to-fail criteria and the presence of non-banking firms among the most systemically important financial institutions in the Colombian case.
    Keywords: Systemic Importance, Systemic Risk, Principal Components Analysis, Too-connected-to-fail, Too-big-to-fail. Classification JEL: D85, C63, E58, G28
    Date: 2012–10
  11. By: Bannouh, K.; Martens, M.P.E.; Oomen, R.C.A.; Dijk, D.J.C. van
    Abstract: We introduce a Mixed-Frequency Factor Model (MFFM) to estimate vast dimensional covari- ance matrices of asset returns. The MFFM uses high-frequency (intraday) data to estimate factor (co)variances and idiosyncratic risk and low-frequency (daily) data to estimate the factor loadings. We propose the use of highly liquid assets such as exchange traded funds (ETFs) as factors. Prices for these contracts are observed essentially free of microstructure noise at high frequencies, allowing us to obtain precise estimates of the factor covariances. The factor loadings instead are estimated from daily data to avoid biases due to market microstructure effects such as the relative illiquidity of individual stocks and non-synchronicity between the returns on factors and stocks. Our theoretical, simulation and empirical results illustrate that the performance of the MFFM is excellent, both compared to conventional factor models based solely on low-frequency data and to popular realized covariance estimators based on high-frequency data.
    Keywords: dimensional covariance estimation;mixed-frequency factor models
    Date: 2012–10–23
  12. By: D.E. Allen (School of Accounting Finance and Economics Edith Cowan University Joondalup Drive Joondalup Western Australia 6027); Abhay K Singh (School of Accouting Finance & Economics, Edith Cowan University, Australia); R. Powell (School of Accounting Finance and Economics Edith Cowan University Joondalup Drive Joondalup Western Australia 6027); Michael McAleer (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam.); James Taylor (Said Business School, University of Oxford, Oxford); Lyn Thomas (Southampton Management School, University of Southampton, Southampton)
    Abstract: This paper examines the asymmetric relationship between price and implied volatility and the associated extreme quantile dependence using a linear and non- linear quantile regression approach. Our goal is to demonstrate that the relationship between the volatility and market return, as quantied by Ordinary Least Square (OLS) regression, is not uniform across the distribution of the volatility-price re- turn pairs using quantile regressions. We examine the bivariate relationships of six volatility-return pairs, namely: CBOE VIX and S&P 500, FTSE 100 Volatility and FTSE 100, NASDAQ 100 Volatility (VXN) and NASDAQ, DAX Volatility (VDAX) and DAX 30, CAC Volatility (VCAC) and CAC 40, and STOXX Volatility (VS- TOXX) and STOXX. The assumption of a normal distribution in the return series is not appropriate when the distribution is skewed, and hence OLS may not capture a complete picture of the relationship. Quantile regression, on the other hand, can be set up with various loss functions, both parametric and non-parametric (linear case) and can be evaluated with skewed marginal-based copulas (for the non-linear case), which is helpful in evaluating the non-normal and non-linear nature of the relationship between price and volatility. In the empirical analysis we compare the results from linear quantile regression (LQR) and copula based non-linear quantile regression known as copula quantile regression (CQR). The discussion of the properties of the volatility series and empirical ndings in this paper have signicance for portfolio optimization, hedging strategies, trading strategies and risk management, in general.
    Keywords: Return Volatility relationship, quantile regression, copula, copula quantile regression, volatility index, tail dependence.
    JEL: C14 C58 G11
    Date: 2012–10
  13. By: Fabio Caccioli; Munik Shrestha; Cristopher Moore; J. Doyne Farmer
    Abstract: Common asset holdings are widely believed to have been the primary vector of contagion in the recent financial crisis. We develop a network approach to the amplification of financial contagion due to the combination of overlapping portfolios and leverage, and we show how it can be understood in terms of a generalized branching process. By studying a stylized model we estimate the circumstances under which systemic instabilities are likely to occur as a function of parameters such as leverage, market crowding, diversification, and market impact. Although diversification may be good for individual institutions, it can create dangerous systemic effects, and as a result financial contagion gets worse with too much diversification. Under our model there is a critical threshold for leverage; below it financial networks are always stable, and above it the unstable region grows as leverage increases. The financial system exhibits "robust yet fragile" behavior, with regions of the parameter space where contagion is rare but catastrophic whenever it occurs. Our model and methods of analysis can be calibrated to real data and provide simple yet powerful tools for macroprudential stress testing.
    Date: 2012–10
  14. By: Matkovskyy, Roman
    Abstract: This paper proposes an approach to explore the strength of the financial system of a country against the possibility of financial perturbations appearing based on the construction of the Index of Financial Safety (IFS) of a country. The Markov Chain Monte Carlo (MCMC) and Gibbs sampler technique is used to estimate a Bayesian Vector Autoregressive Model of the IFS of South Africa for the period 1990Q1-2011Q1 and to forecast its value over the period 2011Q2-2017Q1. It is shown that the IFS could capture the disturbances in the financial system and the BVAR models with the non-informative and Minnesota priors could predict the future dynamics of IFS with sufficient accuracy.
    Keywords: Financial safety; index of financial safety (IFS); Bayesian Vector Autoregressive (BVAR) model; MCMC; Gibbs sampler; South Africa
    JEL: C15 E47 C11 C01 G01
    Date: 2012–04
  15. By: Mustafa Hakan Eratalay
    Abstract: In this paper, we make two contributions to the MSV literature. First, we propose two new MSV models that account for leverage effects. Second, we compare the small sample performances of Quasi Maximum Likelihood (QML) and Monte Carlo Likelihood (MCL) methods through Monte Carlo studies for Constant Correlations MSV and Time Varying Correlations MSV and for the two MSV models with leverage we propose. We also provide the specific transformations necessary for the MCL estimation of the proposed MSV models with leverage. Our results confirm that the MCL estimator has better small sample performance compared to the QML estimator. In terms of parameter estimation, both estimators perform better when the series are highly correlated. In estimating the underlying volatilities and correlations, QML estimator’s performance comes closer to that of MCL estimator when the SV process has higher variance or when the correlations are time varying, while it is performing relatively worse in MSV models with leverage. Finally we include an empirical illustration by estimating an MSV model with leverage that we propose using a trivariate data from the major European stock markets.
    Keywords: Multivariate Stochastic Volatility, Estimation, Constant Correlations, Time Varying Correlations, Leverage
    JEL: C32
    Date: 2012–10–15
  16. By: Albert, Jose Ramon G.; Ng, Thiam Hee
    Abstract: Since the global financial crisis in 2008/09 there has been heightened concern about the resilience of banking systems in Southeast Asia. This paper proposes a methodology that uses a macroprudential perspective to assess the resilience of banking systems in member countries of the Association of Southeast Asian Nations. It then proceeds to apply this methodology to examine the resilience of the Philippine banking system. Data on financial soundness in the Philippine banking system are utilized in a vector autoregression model to study the dynamic relationships that exist among financial and macroeconomic indicators. Using impulse response functions, a simulation of financial ratios in the banking system is conducted by assuming unlikely but plausible stress scenarios to determine whether banking system credit and capital could withstand the impact of such circumstances. In the stress scenarios, the estimated impact of macroeconomic shocks on nonperforming loan and capital adequacy ratios is generally minimal. The results, however, do suggest that the Philippine banking system has some vulnerability to interest rate and stock market shocks. The results of such stress testing provide a better understanding of the level of preparedness required for managing risks in the financial system, especially in the wake of continuing global economic uncertainty.
    Keywords: banking system, Philippines, macroprudential, stress testing, panel VAR
    Date: 2012

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