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Abstract:
In this paper, we provide an overview of the subprime mortgage securitization process and the seven key informational frictions that arise. We discuss the ways that market participants work to minimize these frictions and speculate on how this process broke down. We continue with a complete picture of the subprime borrower and the subprime loan, discussing both predatory borrowing and predatory lending. We present the key structural features of a typical subprime securitization, document how rating agencies assign credit ratings to mortgage-backed securities, and outline how these agencies monitor the performance of mortgage pools over time. Throughout the paper, we draw upon the example of a mortgage pool securitized by New Century Financial during 2006.

subprime mortgage credit, securitization, rating agencies, principal agent, moral hazard

Abstract:
The New Basel Accord for bank capital regulation is designed to better align regulatory capital to the underlying risks by encouraging better and more systematic risk management practices, especially in the area of credit risk. We provide an overview of the objectives, analytical foundations and main features of the Accord and then open the door to some research questions provoked by the Accord. We see these questions falling into three groups: What is the impact of the proposal on the global banking system through possible changes in bank behavior; a set of issues around risk analytics such as model validation, correlations and portfolio aggregation, operational risk metrics and relevant summary statistics of a bank's risk profile; issues brought about by Pillar 2 (supervisory review) and Pillar 3 (public disclosure).

Bank capital regulation, risk management, credit risk, operational risk

Abstract:
The goal of integrated risk management in a financial institution is to measure and manage risk and capital across a range of diverse business activities. This requires an approach for aggregating risk types (market, credit, and operational) whose distributional shapes vary considerably. In this paper, we construct the joint risk distribution for a typical large, internationally active bank using the method of copulas. This technique allows us to incorporate realistic marginal distributions that capture some of the essential empirical features of these risks like skewness and fat-tails while allowing for a rich dependence structure. We explore the impact of business mix and inter-risk correlations on total risk, whether measured by value-at-risk or expected shortfall. We find that given a risk type, total risk is more sensitive to differences in business mix or risk weights than to differences in inter-risk correlations. There is a complex relationship between volatility and fat-tails in determining the total risk: depending on the setting, they either offset or reinforce each other. The choice of copula (normal versus Student-t), which determines the level of tail dependence, has a more modest effect on risk. We then compare the copula-based method with several conventional approaches to computing risk, each of which may be thought of as an approximation. One easily implemented approximation, which uses empirical correlations and quantile estimates, tracks the copula approach surprisingly well. In contrast, the additive approximation, which assumes no diversification benefit, typically overestimates risk by about 30-40%.

Market risk, credit risk, operational risk, risk diversification, copula

Abstract:
The goal of integrated risk management in a financial institution is to measure and manage risk and capital across a range of diverse business activities. This requires an approach for aggregating risk types (market, credit, and operational) whose distributional shapes vary considerably. In this paper, we construct the joint risk distribution for a typical large, internationally active bank using the method of copulas. This technique allows us to incorporate realistic marginal distributions that capture some of the essential empirical features of these risks like skewness and fat-tails while allowing for a rich dependence structure. We explore the impact of business mix and inter-risk correlations on total risk, whether measured by value-at-risk or expected shortfall. We find that given a risk type, total risk is more sensitive to differences in business mix or risk weights than to differences in inter-risk correlations. There is a complex relationship between volatility and fat-tails in determining the total risk: depending on the setting, they either offset or reinforce each other. The choice of copula (normal versus Student-t), which determines the level of tail dependence, has a more modest effect on risk. We then compare the copula-based method with several conventional approaches to computing risk, each of which may be thought of as an approximation. One easily implemented approximation, which uses empirical correlations and quantile estimates, tracks the copula approach surprisingly well. In contrast, the additive approximation, which assumes no diversification benefit, typically overestimates risk by about 30-40%.

Market risk, credit risk, operational risk, risk diversification, copula

Abstract:
In this paper we conduct a systematic comparison of confidence intervals around estimated probabilities of default (PD) using several analytical approaches from large sample theory as well as bootstrapped small-sample confidence intervals. We do so for two different PD estimation methods, cohort and duration (intensity), using 22 years of credit ratings data. We find that the bootstrapped intervals for the duration based estimates are surprisingly tight when compared to the more commonly used (asymptotic) Wald interval. We find that even with these relatively tight confidence intervals, it is impossible to distinguish notch-level PDs for investment grade ratings, e.g. a PD(AA-) from a PD(A+). However, once the speculative grade barrier is crossed, we are able to distinguish quite cleanly notch-level estimated default probabilities. Conditioning on the state of the business cycle helps: It is easier to distinguish adjacent PDs in recessions than in expansions.

Risk management, credit risk, bootstrap, confidence intervals

Abstract:
The turmoil in the capital markets in 1997 and 1998 has highlighted the need for systematic stress testing of banks' portfolios, including both their trading and lending books. We propose that underlying macroeconomic volatility is a key part of a useful conceptual framework for stress testing credit portfolios, and that credit migration matrices provide the specific linkages between underlying macroeconomic conditions and asset quality. Credit migration matrices, which characterize the expected changes in credit quality of obligors, are cardinal inputs to many applications, including portfolio risk assessment, modeling the term structure of credit risk premia, and pricing of credit derivatives. They are also an integral part of many of the credit portfolio models used by financial institutions. By separating the economy into two states or regimes, expansion and contraction, and conditioning the migration matrix on these states, we show that the loss distribution of credit portfolios can differ greatly, as can the concomitant level of economic capital to be assigned.

Credit risk, stress testing, ratings migration, credit portfolio management

Abstract:
The New Basel Accord will allow internationally active banking organizations to calculate their credit risk capital requirements using an internal ratings based (IRB) approach, subject to supervisory review. One of the modeling components is loss given default (LGD), the credit loss incurred if an obligor of the bank defaults. The flexibility to determine LGD values tailored to a bank's portfolio will likely be a motivation for a bank to want to move from the foundation to the advanced IRB approach. The appropriate degree of flexibility depends, of course, on what a bank knows about LGD broadly and about differentiated LGDs in particular; consequently supervisors must be able to evaluate "what a bank knows." The key issues around LGD are: 1) What does LGD mean and what is its role in IRB? 2) How is LGD defined and measured? 3) What drives differences in LGD? 4) What approaches can be taken to model or estimate LGD? By surveying the academic and practitioner literature, with supportive examples and illustrations from public data sources, this paper is designed to provides basic answers to these questions. The factors which drive significant differences in LGD include place in the capital structure, presence and quality of collateral, industry and timing of the business cycle.

New Basel Accord, credit risk

Abstract:
Hedge funds, with assets under management approaching an estimated $1.5 trillion in 2006, have become important players in the U.S. and global capital markets. These largely unregulated funds differ from other market participants in their use of a variety of complex trading strategies and instruments, in their liberal use of leverage, in their opacity to outsiders, and in their convex compensation structure. These differences can exacerbate potential market failures stemming from agency problems, externalities, and moral hazard. Counterparty credit risk management (CCRM) practices, used by financial institutions to assess credit risk and limit counterparty exposure, are the first line of defense against market disruptions with potential systemic consequences. This article examines how the unique nature of hedge funds may generate market failures that make counterparty credit risk for exposures to the funds intrinsically more difficult to manage, both for regulated institutions and for policymakers concerned with systemic risk. The authors acknowledge that various market failures, such as the events surrounding the 1998 collapse of hedge fund Long-Term Capital Management, may make CCRM imperfect. However, CCRM has improved significantly since then, and it remains the appropriate starting point for limiting the potential for hedge funds to generate systemic disruptions.

banks, counterparty credit risk management, liquidity

Abstract:
Hedge funds are significant players in the U.S. capital markets, but differ from other market participants in important ways such their use of a wide range of complex trading strategies and instruments, leverage, opacity to outsiders, and their compensation structure. The traditional bulwark against financial market disruptions with potential systemic consequences has been the set of counterparty credit risk management (CCRM) practices by the core of regulated institutions. The characteristics of hedge funds make CCRM more difficult as they exacerbate market failures linked to agency problems, externalities, and moral hazard. Nonetheless, we conclude that CCRM remains the best line of defense against systemic risk and that direct regulation of hedge funds is not desirable.

banks, counterparty credit risk management, liquidity

Abstract:
This paper seeks to put forward a framework, from the perspective of practitioners and policymakers, for how the known, unknown, and unknowable vary by risk type within banking. We define total bank risk in terms of earnings volatility, which can be broken down into five major classes of risk: market, credit, asset/liability, operational, and business risks. For our purposes, risk is known (K) if it can be enumerated, in the sense of being identified, and quantified; it is unknown (U1) if the set of risks can be identified and enumerated but not meaningfully quantified; and it is unknowable (U2) if the existence of the risk or set of risks is not predictable ex ante, let alone quantifiable. Based on these definitions, we position the five sources of bank risk within the K, U1, U2 space based on evidence from industry practice and suggest that K decreases, and U1 and U2 increase, along a spectrum from market risk to credit risk, asset/liability risk, operational risk, and business risk. Using bank-level data we attempt to quantify or size both total bank risk and the contribution from each risk type based on a large sample of earnings volatility data for US bank holding companies over the 1986-2005 period. We find that a) total earnings volatility is protected by minimum regulatory capital requirements at implied credit rating levels ranging from about A- to BBB, depending on the sample; b) when allocating among the different risk types, market risk accounts for only about 5%, credit for almost half, structural interest rate risk for about 18%, and non-financial risks, including both operational and business risk, for about 30% of total risk; c) the diversification benefit, i.e., the difference between the whole and the sum of the parts, is about one-third. Not surprisingly, large banks also seem to experience fewer extreme adverse outcomes.

risk measurement, risk management, capital adequacy

Abstract:
Is volatility forecastability important for long-horizon risk management, or is a traditional constant-volatility assumption adequate? In this paper, the authors address this question, exploring the interface between long-horizon financial risk management and long-horizon volatility forecastability and, in particular, whether long-horizon volatility is forecastable enough such that volatility models are useful for long-horizon risk management.

capital regulation

Abstract:
This paper presents a new approach to modeling conditional credit loss distributions. Asset value changes of firms in a credit portfolio are linked to a dynamic global macroeconometric model, allowing macro effects to be isolated from idiosyncratic shocks from the perspective of default (and hence loss). Default probabilities are driven primarily by how firms are tied to business cycles, both domestic and foreign, and how business cycles are linked across countries. We allow for firm-specific business cycle effects and the heterogeneity of firm default thresholds using credit ratings. The model can be used, for example, to compute the effects of a hypothetical negative equity price shock in South East Asia on the loss distribution of a credit portfolio with global exposures over one or more quarters. We show that the effects of such shocks on losses are asymmetric and non-proportional, reflecting the highly non-linear nature of the credit risk model.

Risk management, economic interlinkages, loss forecasting, default correlation

Abstract:
Liquidity risk in banking has been attributed to transactions deposits and their potential to spark runs or panics. We show instead that transactions deposits help banks hedge liquidity risk from unused loan commitments. Bank stock-return volatility increases with unused commitments, but the increase is smaller for banks with high levels of transactions deposits. This deposit-lending risk management synergy becomes more powerful during periods of tight liquidity, when nervous investors move funds into their banks. Our results reverse the standard notion of liquidity risk at banks, where runs from depositors had been seen as the cause of trouble.

Abstract:
Unused loan commitments expose banks to systematic liquidity risk, but this exposure can be reduced by combining loan commitments with transactions deposits. We show that bank equity volatility increases with unused loan commitments, but this increase is reduced for banks with high levels of transaction deposits. This deposit-lending synergy becomes even more powerful during periods of tight liquidity, when nervous investors move funds into their banks. Thus, the simultaneous taking of deposits and lending may be thought of as a liquidity hedge.

Liquidity, banking, financial crisis

Abstract:
We examine the question of deposit insurance through the lens of risk management by addressing three key issues: 1) how big should the fund be; 2) how should coverage be priced; and 3) who pays in the event of loss. We propose a risk-based premium system that is explicitly based on the loss distribution faced by the FDIC. The loss distribution can be used to determine the appropriate level of fund adequacy and reserving in terms of a stated confidence interval and to identify risk-based pricing options. We explicitly estimate that distribution using two different approaches and find that reserves are sufficient to cover roughly 99.85% of the loss distribution corresponding to about a BBB+ rating. We then identify three risk-sharing alternatives addressing who is responsible for funding losses in different parts of the loss distribution. We show in an example that expected loss based pricing, while appropriately penalizing riskier banks, also penalizes smaller banks. By contrast, unexpected loss contribution based pricing significantly penalizes very large banks because large exposures contribute disproportionately to overall (FDIC) portfolio risk.

Deposit insurance pricing, loss distribution, risk-based premiums.

Abstract:
We develop a framework for modeling conditional loss distributions through the introduction of risk factor dynamics. Asset value changes of a credit portfolio are linked to a dynamic global macroeconometric model, allowing macro effects to be isolated from idiosyncratic shocks. Default probabilities are driven primarily by how firms are tied to business cycles, both domestic and foreign, and how business cycles are linked across countries. The model is able to control for firm-specific heterogeneity as well as generate multi-period forecasts of the entire loss distribution, conditional on specific macroeconomic scenarios.

Risk Management, Economic Interlinkages, Loss Forecasting, Default Correlation

Abstract:
Market risk management traditionally has focussed on the distribution of portfolio value changes resulting from moves in the midpoint of bid and ask prices. Hence the market risk is really in a "pure" form: risk in an idealized market with no "friction" in obtaining the fair price. However, many markets possess an additional liquidity component that arises from a trader not realizing the mid-price when liquidating her position, but rather the mid-price minus the bid-ask spread. We argue that liquidity risk associated with the uncertainty of the spread, particularly for thinly traded or emerging market securities under adverse market conditions, is an important part of overall risk and is therefore an important component to model.We develop a simple liquidity risk methodology that can be easily and seamlessly integrated into standard value-at-risk models, and we show that ignoring the liquidity effect can produce underestimates of market risk in emerging markets by as much as 25-30%. Furthermore, we show that the BIS inadvertently is already monitoring liquidity risk, and that by not modeling it explicitly and therefore capitalizing against it, banks will be experiencing surprisingly many violations of capital requirements, particularly if their portfolios are concentrated in emerging markets.

Abstract:
We examine why these regulatory differences exist and what they imply for differences in minimum capital requirements for position risk. We consider differences in the definition and measurement of regulatory capital and we quantify differences in the capital charges for position risk by reference to a model portfolio that contains a variety of financial instruments including equity, fixed income instruments, swaps, foreign exchange positions, and options - instruments that may appear in the portfolios of securities firms, banks or insurance companies. For most leading firms in the financial services industry, however, market forces, not minimum regulatory capital requirements, appear to play the dominant role in firms' capital decisions. Thus we conclude by considering measures to enhance market discipline.

Risk management, Value-at-Risk, Capital Regulation, Market Risk

Abstract:
Credit migration or transition matrices, which characterize the expected changes in credit quality of obligors, are cardinal inputs to applications such as asset pricing and risk management. We propose a new metric for comparing these matrices (a mobility index) by first subtracting the identity matrix, focusing the analysis on the dynamics, and then taking the average of the singular values for the resulting matrix. This yields a metric which has an intuitively-appealing "size" related to the average probability of migration of the original matrix. We also propose a new mobility index performance criterion which is particularly relevant for credit migration matrices, namely that it be distribution discriminatory, i.e. sensitive to the distribution of off-diagonal probability mass. We demonstrate the advantages of the proposed metric over more traditional cell-by-cell distance metrics and eigenvalue-based mobility indices. We then apply these metrics to credit rating histories of S&P rated U.S. obligors from 1981-2001.

credit migration matrix, matrix norm, mobility indices, singular values, risk management

Abstract:
Credit migration matrices are cardinal inputs to many risk management applications. Their accurate estimation is therefore critical. We explore three approaches, cohort and two variants of duration - time homogeneous and non-homogeneous - and the resulting differences, both statistically through matrix norms and economically through credit portfolio and credit derivative models. We develop a testing procedure to assess statistically the differences between migration matrices using bootstrap techniques. The method can have substantial economic import: economic credit risk capital differences between economic regimes, recession vs. expansion, can be as large as difference implied by different estimation techniques. Ignoring the efficiency gain inherent in the duration methods by using the cohort method instead is more damaging that making a (possibly false) assumption of time-homogeneity.

Credit risk, risk management, matrix norms, bootstrapping, credit derivatives

Abstract:
Despite mounting evidence to the contrary, credit migration matrices, used in many credit risk and pricing applications, are typically assumed to be generated by a simple Markov process. Based on empirical evidence we propose a parsimonious model that is a mixture of (two) Markov chains, where the mixing is on the speed of movement among credit ratings. We estimate this model using credit rating histories and show that the mixture model statistically dominates the simple Markov model and that the differences between two models can be economically meaningful. The non-Markov property of our model implies that the future distribution of a firm's ratings depends not only on its current rating but also on its past rating history. Indeed we find that two firms with identical current credit ratings can have substantially different transition probability vectors. We also find that conditioning on the state of the business cycle or industry group does not remove the heterogeneity with respect to the rate of movement. We go on to compare the performance of mixture and Markov chain using out-of sample predictions.

Risk management, credit risk, credit derivatives

Abstract:
Credit migration matrices are cardinal inputs to many risk management applications; their accurate estimation is therefore critical. We explore two approaches: cohort and two variants of duration - one imposing, the other relaxing time homogeneity - and the resulting differences, both statistically through matrix norms and economically using a credit portfolio model. We propose a new metric for comparing these matrices based on singular values and apply it to credit rating histories of S&P rated U.S. firms from 1981-2002. We show that the migration matrices have been increasing in "size" since the mid-1990s, with 2002 being the "largest" in the sense of being the most dynamic. We develop a testing procedure using bootstrap techniques to assess statistically the differences between migration matrices as represented by our metric. We demonstrate that it can matter substantially which estimation method is chosen: economic credit risk capital differences implied by different estimation techniques can be as large as differences between economic regimes, recession vs. expansion. Ignoring the efficiency gain inherent in the duration methods by using the cohort method instead is more damaging than imposing a (possibly false) assumption of time homogeneity.

Risk management, credit risk, credit derivatives

Abstract:
This paper examines the common factors that drive the returns of U.S. bank holding companies from 1997 to 2005. We compare a range of market models from a basic one-factor model to a nine-factor model that includes the standard Fama-French factors and additional factors thought to be particularly relevant for banks such as interest and credit variables. We show that the market factor clearly dominates in explaining bank returns, followed by the Fama-French factors. The bank-specific factors are not informative, particularly for the largest banks, which take advantage of protection in the form of interest rate and credit derivatives. Even in our broadest model, however, considerable residual variation remains, with the mean pairwise correlation of residuals for the largest banks near 0.25. This finding suggests that important hidden factors remain. A principal component analysis shows that this residual variance is relatively diffuse, although the largest banks do tend to load in the same direction on the first component. Relative to the returns of large firms in other sectors, bank returns are relatively well explained with standard risk factors, and both the residual correlation and degree of factor loading agreement are not particularly large. These results have clear implications both for public policymakers seeking to quantify those shared bank exposures that create systemic risk and to portfolio managers seeking to devise optimal diversification strategies.

commercial banks, risk management, portfolio choice, systemic risk

Abstract:
In this paper we conduct a systematic comparison of confidence intervals around estimated probabilities of default (PD) using several analytical approaches as well as parametric and nonparametric bootstrap methods. We do so for two different PD estimation methods, cohort and duration (intensity), with 22 years of credit ratings data. We find that the bootstrapped intervals for the duration based estimates are relatively tight when compared to either analytic or bootstrapped intervals around the less efficient cohort estimator. We show how the large differences between the point estimates and confidence intervals of these two estimators are consistent with non-Markovian migration behavior. Surprisingly, even with these relatively tight confidence intervals, it is impossible to distinguish notch-level PDs for investment grade ratings, e.g., a PDAA- from a PDA+. However, once the speculative grade barrier is crossed, we are able to distinguish quite cleanly notch-level estimated PDs. Conditioning on the state of the business cycle helps: it is easier to distinguish adjacent PDs in recessions than in expansions.

Risk management, credit risk, bootstrap

Abstract:
In this paper we conduct a systematic comparison of confidence intervals around estimated probabilities of default (PD) using several analytical approaches as well as parametric and nonparametric bootstrap methods. We do so for two different PD estimation methods, cohort and duration (intensity), with 22 years of credit ratings data. We find that the bootstrapped intervals for the duration based estimates are relatively tight when compared to either analytic or bootstrapped intervals around the less efficient cohort estimator. We show how the large differences between the point estimates and confidence intervals of these two estimators are consistent with non-Markovian migration behavior. Surprisingly, even with these relatively tight confidence intervals, it is impossible to distinguish notch-level PDs for investment grade ratings, e.g. a PDAA- from a PDA+. However, once the speculative grade barrier is crossed, we are able to distinguish quite cleanly notch-level estimated PDs. Conditioning on the state of the business cycle helps: it is easier to distinguish adjacent PDs in recessions than in expansions.

Risk management, credit risk, bootstrap

Abstract:
This paper considers a simple model of credit risk and derives the limit distribution of losses under different assumptions regarding the structure of systematic risk and the nature of exposure or firm heterogeneity. We derive fat-tailed correlated loss distributions arising from Gaussian (i.e. non-fat-tailed) risk factors and explore the potential for (and limit of) risk diversification. Where possible the results are generalized to non-Gaussian distributions. The theoretical results indicate that if the firm parameters are heterogeneous but come from a common distribution, for sufficiently large portfolios there is no scope for further risk reduction through active portfolio management. However, if the firm parameters come from different distributions, say for different sectors or countries, then further risk reduction is possible, even asymptotically, by changing the portfolio weights. In either case, neglecting parameter heterogeneity can lead to underestimation of expected losses. But, once expected losses are controlled for, neglecting parameter heterogeneity can lead to overestimation of risk, whether measured by unexpected loss or value-at-risk. We examine the impact of sectoral and geographic diversification on credit losses empirically using returns for firms in the U.S. and Japan across seven sectors and find that ignoring this heterogeneity results in far riskier credit portfolios. Risk, is reduced significantly when parameter heterogeneity is properly taken into account.

Risk management, correlated defaults, credit loss distributions, heterogeneity, diversification

Abstract:
A financial institution such as a bank is ultimately exposed to macroeconomic fluctuations in the countries to which it has exposure, the most acute example being commercial lending to companies whose fortunes fluctuate with aggregate demand. It was this risk management need for financial institutions which motivated us to build a compact global macroeconometric model capable of generating (point as well as density) forecasts for a core set of macroeconomic factors for a set of regions and countries which explicitly allows for interconnections and dependencies that exist between national and international factors in a coherent and consistent manner. This paper provides such a global modeling framework by making use of recent advances in the analysis of cointegrating systems. In an unrestricted VAR model covering N countries/regions, the number of unknown parameters will be unfeasibly large (around p(4N-1)+1, where p is the order of the VAR), requiring a more parsimonious solution. We first estimate individual country (or region) specific vector error correcting models, where the domestic macroeconomic variables are related to corresponding foreign variables constructed exclusively to match the international trade pattern of the country under consideration. The individual country models are then combined in a consistent and cohesive manner to generate forecasts for all the variables in the world economy simultaneously. We estimate the model using quarterly data from 1979Q1 to 1999Q1 and perform contagion analysis by investigating the transmission of shocks of one variable to the rest of the world.

Economic interlinkages, global macroeconometric modeling, risk management

Abstract:
The potential for portfolio diversification is driven broadly by two characteristics: the degree to which systematic risk factors are correlated with each other and the degree of dependence individual firms have to the different types of risk factors. Using a global vector autoregressive macroeconometric model accounting for about 80% of world output, we propose a model for exploring credit risk diversification across industry sectors and across different countries or regions. We find that full firm-level parameter heterogeneity along with credit rating information matters a great deal for capturing differences in simulated credit loss distributions. Imposing homogeneity results in overly skewed and fat-tailed loss distributions. These differences become more pronounced in the presence of systematic risk factor shocks: increased parameter heterogeneity reduces shock sensitivity. Allowing for regional parameter heterogeneity seems to better approximate the loss distributions generated by the fully heterogeneous model than allowing just for industry heterogeneity. The regional model also exhibits less shock sensitivity.

Risk management, default dependence, economic interlinkages, portfolio choice

Abstract:
In a sharp turnaround from their fortunes in the 1990-91 recession, banks came through the 2001 recession reasonably well. A look at industry and economy-wide developments in the intervening years suggests that banks fared better largely because of more effective risk management. In addition, they benefited from a decline in short-term interest rates and the relative mildness of the 2001 downturn.

risk management, bank performance, risk-based pricing, diversification

Abstract:
This paper considers a simple model of credit risk and derives the limit distribution of losses under different assumptions regarding the structure of systematic and idiosyncratic risks and the nature of firm heterogeneity. It documents a rich and complex interaction between the underlying model parameters and the resulting loss distributions. The theoretical results indicate that neglecting heterogeneity in firm returns and/or default thresholds leads to underestimation of expected losses (EL), and its effect on portfolio risk is ambiguous. But once EL is controlled for, neglecting parameter heterogeneity leads to overestimation of risk. These results are verified empirically where it is shown that heterogeneity in the default threshold or unconditional probability of default, measured for instance by a credit rating, is of first order importance in affecting the shape of the loss distribution: including ratings heterogeneity alone results in a more than one-quarter drop in loss volatility and a more than one-half drop in 99.9% VaR, the level to which the risk weights of the New Basel Accord are calibrated.

Risk management, correlated defaults, factor models, portfolio choice

Abstract:
In theory the potential for credit risk diversifcation for banks could be substantial. Portfolios are large enough that idiosyncratic risk is diversifed away leaving exposure to systematic risk. The potential for portfolio diversifcation is driven broadly by two characteristics: the degree to which systematic risk factors are correlated with each other and the degree of dependence individual firms have to the different types of risk factors. We propose a model for exploring these dimensions of credit risk diversifcation: across industry sectors and across different countries or regions. We find that full firm-level parameter heterogeneity matters a great deal for capturing differences in simulated credit loss distributions. Imposing homogeneity results in overly skewed and fat-tailed loss distributions. These differences become more pronounced in the presence of systematic risk factor shocks: increased parameter heterogeneity greatly reduces shock sensitivity. Allowing for regional parameter heterogeneity seems to better approximate the loss distributions generated by the fully heterogeneous model than allowing just for industry heterogeneity. The regional model also exhibits less shock sensitivity.

Risk management, default dependence, economic interlinkages, portfolio choice

Abstract:
This paper considers the problem of forecasting real and financial macroeconomic variables across a large number of countries in the global economy. To this end a global vector autoregressive (GVAR) model previously estimated over the 1979Q1-2003Q4 period by Dees, de Mauro, Pesaran, and Smith (2007), is used to generate out-of-sample one quarter and four quarters ahead forecasts of real output, inflation, real equity prices, exchange rates and interest rates over the period 2004Q1-2005Q4. Forecasts are obtained for 134 variables from 26 regions made up of 33 countries covering about 90% of world output. The forecasts are compared to typical benchmarks: univariate autoregressive and random walk models. Building on the forecast combination literature, the effects of model and estimation uncertainty on forecast outcomes are examined by pooling forecasts obtained from different GVAR models estimated over alternative sample periods. Given the size of the modeling problem, and the heterogeneity of economies considered ¿ industrialised, emerging, and less developed countries ¿ as well as the very real likelihood of possibly multiple structural breaks, averaging forecasts across both models and windows makes a significant difference. Indeed the double-averaged GVAR forecasts performed better than the benchmark competitors, especially for output, inflation and real equity prices.

forecasting using GVAR, structural breaks and forecasting, average forecasts across models and windows, financial and macroeconomic forecasts

Abstract:
Recent literature has trumpeted the claim that extreme value theory (EVT) holds promise for accurate estimation of extreme quantiles and tail probabilities of financial asset returns, and hence hold promise for advances in the management of extreme financial risks. Our view, based on a disinterested assessment of EVT from the vantage point of financial risk management, is that the recent optimism is partly appropriate but also partly exaggerated, and that at any rate much of the potential of EVT remains latent. We substantiate this claim by sketching a number of pitfalls associate with use of EVT techniques. More constructively, we show how certain of the pitfalls can be avoided, and we sketch a number of explicit research directions that will help the potential of EVT to be realized.

Abstract:
Despite overwhelming evidence to the contrary, credit migration matrices, used in many credit risk and pricing applications, are typically assumed to be generated by a simple Markov process. In this paper we propose a parsimonious model that is a mixture of (two) Markov chains. We estimate this model using credit rating histories and show that the mixture model statistically dominates the simple Markov model and that the differences between two models can be economically meaningful. The non-Markov property of our model implies that the future distribution of a firm's ratings depends not only on its current rating but also on its past rating history. Indeed we find that two firms with identical credit ratings can have substantially different transition probability vectors.

Risk management, credit risk, credit derivatives

Abstract:
We report evidence from the equity market that unused loan commitments expose banks to systematic liquidity risk, especially during crises such as the one observed in the fall of 1998. We also find, however, that banks with higher levels of transactions deposits had lower risk during the 1998 crisis than other banks. These banks experienced large inflows of funds just as they were needed -- when liquidity demanded by firms taking down funds from commercial paper backup lines of credit peaked. Our evidence suggests that combining loan commitments with deposits mitigates liquidity risk, and that this deposit-lending synergy is especially powerful during period of crises as nervous investors move funds into their banks.

Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Abstract:
The potential for portfolio diversification is driven broadly by two characteristics: the degree to which systematic risk factors are correlated with each other and the degree of dependence individual firms have to the different types of risk factors. Using a global vector autoregressive macroeconomic model accounting for about 80% of world output, we propose a model for exploring credit risk diversification across industry sectors and across different countries or regions. We find that full firm-level parameter heterogeneity along with credit rating information matters a great deal for capturing differences in simulated credit loss distributions. These differences become more pronounced in the presence of systematic risk factor shocks: increased parameter heterogeneity reduces shock sensitivity. Allowing for regional parameter heterogeneity seems to better approximate the loss distributions generated by the fully heterogenous model than allowing just for industry heterogeneity. The regional model also exhibits less shock sensitivity.

Abstract:
The possibility of exact maximum likelihood estimation of many observation-driven models remains an open question. Often only approximate maximum likelihood estimation is attempted, because the unconditional density needed for exact estimation is not known in closed form. Using simulation and nonparametric density estimation techniques that facilitate empirical likelihood evaluation, we develop an exact maximum likelihood procedure. We provide an illustrative application to the estimation of ARCH models, in which we compare the sampling properties of the exact estimator to those of several competitors. We find that, especially in situations of small samples and high persistence, efficiency gains are obtained. We conclude with a discussion of directions for future research, including application of our methods to panel data models.

Abstract:
Liquidity risk in banking has been attributed to transactions deposits and their potential to spark runs or panics. We show instead that transactions deposits help banks hedge liquidity risk from unused loan commitments. Bank stock-return volatility increases with unused commitments, but only for banks with low levels of transactions deposits. This deposit-lending hedge becomes more powerful during periods of tight liquidity, when nervous investors move funds into their banks. Our results reverse the standard notion of liquidity risk at banks, where runs from depositors had been seen as the cause of trouble.

G18, G21