This paper suggests a novel approach to assess corporate sector solvency risk. The approach uses a Bottom-Up Default Analysis that projects probabilities of default of individual firms conditional on macroeconomic conditions and financial risk factors. This allows a direct macro-financial link to assessing corporate performance and facilitates what-if scenarios. When extended with credit portfolio techniques, the approach can also assess the aggregate impact of changes in firm solvency risk on creditor banks’ capital buffers under different macroeconomic scenarios. As an illustration, we apply this approach to the corporate sector of the five largest economies in Latin America.
This Occasional Paper provides an overview of the main challenges facing Hong Kong SAR as it continues to become more closely integrated with the mainland of China. Section I provides an overview of recent macroeconomic developments and the main policy issues in Hong Kong SAR. Section II examines various aspects of the ongoing integration with the mainland, and the associated implications for the structure of the economy, and for macroeconomic and structural policies. Section III examines the medium-term fiscal outlook under different policy scenarios and discusses alternative policy options to restore fiscal balance. Section IV reviews recent developments in the real estate sector and their macroeconomic impacts. Section V presents an econome tric analysis of deflation and its determinants. Section VI examines the factors behind, and the implications of, rising wage inequality in Hong Kong SAR. Section VII presents an overview of recent developments in the financial sector and provides an assessment of Hong Kong SAR’s prospects as an international financial center.
Under adverse macroeconomic conditions, the potential realization of corporate sector vulnerabilities could pose major risks to the economy. This paper assesses corporate vulnerabilities in Indonesia by using a Bottom-Up Default Analysis (BuDA) approach, which allows projecting corporate probabilities of default (PDs) under different macroeconomic scenarios. In particular, a protracted recession and the ensuing currency depreciation could erode buffers on corporate balance sheets, pushing up the probabilities of default (PDs) in the corporate sector to the high levels observed during the Global Financial Crisis. While this is a low-probability scenario, the results suggest the need to closely monitor vulnerabilities and strengthen contingency plans.
A thorough analysis of risks in the banking system requires incorporating banks’ inherent heterogeneity and adaptive behavior in response to shocks and changes in business conditions and the regulatory environment. ABBA is an agent-based model for analyzing risks in the banking system in which banks’ business decisions drive the endogenous formation of interbank networks. ABBA allows for a rich menu of banks’ decisions, contingent on banks’ balance sheet and capital position, including dividend payment rules, credit expansion, and dynamic balance sheet adjustment via risk-weight optimization. The platform serves to illustrate the effect of changes on regulatory requirements on solvency, liquidity, and interconnectedness risk. It could also constitute a basic building block for further development of large, bottom-up agent-based macro-financial models.
In reduced-form pricing models, it is usual to assume a fixed recovery rate to obtain the probability of default from credit default swap prices. An alternative credit risk measure is proposed here: the maximum recovery rate compatible with observed prices. The analysis of the recent debt crisis in Argentina using this methodology shows that the correlation between the maximum recovery rate and implied default probabilities turns negative in advance of the credit event realization. This empirical finding suggests that the maximum recovery rate can be used for constructing early warning indicators of financial distress.
This paper reviews policy tools that have been used and/or are available for policy makers in the region to lean against the wind and review relevant country experiences using them. The instruments examined include: (i) capital requirements, dynamic provisioning, and leverage ratios; (ii) liquidity requirements; (iii) debt-to-income ratios; (iv) loan-to-value ratios; (v) reserve requirements on bank liabilities (deposits and nondeposits); (vi) instruments to manage and limit systemic foreign exchange risk; and, finally, (vii) reserve requirements or taxes on capital inflows. Although the instruments analyzed are mainly microprudential in nature, appropriately calibrated over the financial cycle they may serve for macroprudential purposes.
Model selection and forecasting in stress tests can be facilitated using machine learning techniques. These techniques have proved robust in other fields for dealing with the curse of dimensionality, a situation often encountered in applied stress testing. Lasso regressions, in particular, are well suited for building forecasting models when the number of potential covariates is large, and the number of observations is small or roughly equal to the number of covariates. This paper presents a conceptual overview of lasso regressions, explains how they fit in applied stress tests, describes its advantages over other model selection methods, and illustrates their application by constructing forecasting models of sectoral probabilities of default in an advanced emerging market economy.
This paper explores how the global turmoil affected the risk of banks operating in Chile, and provides evidence that could help strengthen work on vulnerability indicators and off-site supervision. The analysis is based on the study of default risk codependence, or CoRisk, between Chilean banks and global financial institutions. The results suggest that the impact of the global financial crisis was limited, inducing at most a one-rating downgrade to banks operating in Chile. The paper concludes by assessing government measures aimed at reducing systemic risk in the domestic banking sector and the recommendations to allocate SWF assets to domestic banks.
Diebold and Yilmaz (2015) recently introduced variance decomposition networks as tools for quantifying and ranking the systemic risk of individual firms. The nature of these networks and their implied rankings depend on the choice decomposition method. The standard choice is the order invariant generalized forecast error variance decomposition of Pesaran and Shin (1998). The shares of the forecast error variation, however, do not add to unity, making difficult to compare risk ratings and risks contributions at two different points in time. As a solution, this paper suggests using the Lanne-Nyberg (2016) decomposition, which shares the order invariance property. To illustrate the differences between both decomposition methods, I analyzed the global financial system during 2001 – 2016. The analysis shows that different decomposition methods yield substantially different systemic risk and vulnerability rankings. This suggests caution is warranted when using rankings and risk contributions for guiding financial regulation and economic policy.
The 2008/9 financial crisis highlighted the importance of evaluating vulnerabilities owing to interconnectedness, or Too-Connected-to-Fail risk, among financial institutions for country monitoring, financial surveillance, investment analysis and risk management purposes. This paper illustrates the use of balance sheet-based network analysis to evaluate interconnectedness risk, under extreme adverse scenarios, in banking systems in mature and emerging market countries, and between individual banks in Chile, an advanced emerging market economy.
This study finds that equity returns in the banking sector in the wake of the Great Recession and the European sovereign debt crisis have been driven mainly by weak growth prospects and heightened sovereign risk and to a lesser extent, by deteriorating funding conditions and investor sentiment. While the equity return performance in the banking sector has been dismal in general, better capitalized and less leveraged banks have outperformed their peers, a finding that supports policymakers’ efforts to strengthen bank capitalization.
The recent financial crisis has highlighted once more that interconnectedness in the financial system is a major source of systemic risk. I suggest a practical way to levy regulatory capital charges based on the degree of interconnectedness among financial institutions. Namely, the charges are based on the institution’s incremental contribution to systemic risk. The imposition of such capital charges could go a long way towards internalizing the negative externalities associated with too-connected-to-fail institutions and providing managerial incentives to strengthen an institution’s solvency position, and avoid too much homogeneity and excessive reliance on the same counterparties in the financial industry.
This paper develops a model of private debt financing under inefficient financial intermediation. It suggests a mechanism that can generate the following sequence of events observed in the recent Asian crisis: A period of relatively low capital flow despite a steady improvement in economic fundamentals (capital inflow inertia), followed by a fast buildup of capital inflow, and ended with a large capital outflow and domestic credit crunch. Unlike other models requiring large movements in fundamentals or asset prices to explain a financial crisis, this model can exhibit large credit/capital flow swings with moderate changes in the economic and market environment.
Despite increased need for top-down stress tests of financial institutions, performing them is challenging owing to the absence of granular information on banks’ trading and loan portfolios. To deal with these data shortcomings, this paper presents a market-based structural top-down stress testing methodology that relies in market-based measures of a bank's probability of default and structural models of default risk to infer the capital losses they could experience in stress scenarios. As an illustration, the methodology is applied to a set of banks in an advanced emerging market economy.
Dynamic provisions could help to enhance the solvency of individual banks and reduce procyclicality. Accomplishing these objectives depends on country-specific features of the banking system, business practices, and the calibration of the dynamic provisions scheme. In the case of Chile, a simulation analysis suggests Spanish dynamic provisions would improve banks' resilience to adverse shocks but would not reduce procyclicality. To address the latter, other countercyclical measures should be considered.
Systemic risk remains a major concern to policymakers since widespread defaults in the corporate and financial sectors could pose substantial costs to society. Forward-looking measures and/or indicators of systemic default risk are thus needed to identify potential buildups of vulnerability in advance. In this paper, we explain how to construct idiosyncratic and systemic default risk indicators using the information embedded in single-tranche standardized collateralized debt obligations (STCDOs) referencing credit derivatives indices. As an illustration, both risk indicators are constructed for the European corporate sector using midprice quotes for STCDOs referencing the iTraxx Europe index.
We develop a mixed-frequency, tree-based, gradient-boosting model designed to assess the default risk of privately held firms in real time. The model uses data from publicly-traded companies to construct a probability of default (PD) function. This function integrates high-frequency, market-based, aggregate distress signals with low-frequency, firm-level financial ratios, and macroeconomic indicators. When provided with private firms' financial ratios, the model, which we name signal-knowledge transfer learning model (SKTL), transfers insights gained from 35 thousand publicly-traded firms to more than 4 million private-held ones and performs well as an ordinal measure of privately-held firms' default risk.
This simulation-based paper investigates the impact of different methods of dynamic provisioning on bank soundness and shows that this increasingly popular macroprudential tool can smooth provisioning costs over the credit cycle and lower banks’ probability of default. In addition, the paper offers an in-depth guide to implementation that addresses pertinent issues related to data requirements, calibration and safeguards as well as accounting, disclosure and tax treatment. It also discusses the interaction of dynamic provisioning with other macroprudential instruments such as countercyclical capital.
This paper introduces the Asset and Liability Management (ALM) compound option model. The model builds on the observation that the public sector net worth in a multi-period setting corresponds to the value of an option on an option on total government assets. Hence, the ALM compound option model is better suited for analyzing and evaluating the risk profile of public debt than existing one-period models, and is especially useful for analyzing the soundness of exit strategies from the large fiscal expansions undertaken by G-20 countries in the wake of the recent financial crisis. As an illustration, the model is used to analyze the risk profile and sustainability of Australia's public debt under different policies.
This paper draws a link between international capital flows and the real options approach to investment by extending a model of real estate investment. It explains gradual investment, investment booms, and investment during recessions and emphasizes sunk costs, uncertainty, and the value of waiting. The optimal waiting time increases as foreign borrowing becomes more expensive because higher returns are required to cover the sunk costs of investing. The lower the initial level of profitability, the more likely investment will be sequential; conversely, a relatively high initial rate of return will be associated with simultaneous investment.
This paper examines some of the factors that have been influential in keeping inflation low in the United States during 1995–98, despite strong growth and high levels of employment. Our results identify three important variables: declines in import prices, a slowdown in the growth of nonwage labor compensation, and a decline in labor costs. We also reassess the role of labor costs and import prices in determining price inflation.
Systemic Risk: History, Measurement and Regulation presents an overview of this emerging form of risk from a global perspective. Systemic risks endanger entire financial systems, not just individual financial institutions. In this volume, the authors review how systemic risk has evolved over the last 40 years across continents to come to the forefront of regulatory attention. They then discuss transmissions channels, provide a review of systemic risk measures, and describe new regulations that have been introduced, as well as the theory and practice of financial stability committees that have been set up internationally. Overall, the book provides a practical guide to understand, identify, assess and control systemic risk.While the financial research on systemic risk has strongly increased since the events of 2008, this book is a first in providing a detailed yet concise overview of the topic, covering the history of systemic risk, its measurement, and its regulation. The authors provide both academic and practitioner-oriented insights, and draw on their different regions of expertise to provide a global perspective on systemic risk.
This paper builds a model of financial sector vulnerability and integrates it into a macroeconomic framework, typically used for monetary policy analysis. The main question to be answered with the integrated model is whether or not the central bank should include explicitly the financial stability indicator in its monetary policy (interest rate) reaction function. It is found in general, that including distance-to-default (dtd) of the banking system in the central bank reaction function reduces both inflation and output volatility. Moreover, the results are robust to different model calibrations: whenever exchange-rate pass-through is higher; financial vulnerability has a larger impact on the exchange rate, as well as on GDP (or the reverse, there is more effect of GDP on bank's equity - i.e., what we call endogeneity), it is more efficient to include dtd in the reaction function.
Machine learning models are becoming increasingly important in the prediction of economic crises. The models, however, use datasets comprising a large number of predictors (features) which impairs model interpretability and their ability to provide adequate guidance in the design of crisis prevention and mitigation policies. This paper introduces surrogate data models as dimensionality reduction tools in large-scale crisis prediction models. The appropriateness of this approach is assessed by their application to large-scale crisis prediction models developed at the IMF. The results are consistent with economic intuition and validate the use of surrogates as interpretability tools.
This paper introduces the quantile regression- based Distance-to-Default to Probability of Default (DD-PD) mapping, which links individual firms’ DD to their real world PD. Since changes in the DD depend on a handful of parameters, the mapping easily accommodates shocks arising from quantitative and narrative scenarios informed by expert judgment. At end-2020, risks from stock market corrections in the U.S. are concentrated in the energy, financial and technology sectors, and additional bank capital needs could be large. The paper concludes discussing uses of the mapping beyond PD valuation suitable for capital structure analysis, credit portfolio management, and long-term scenario planning analysis.
Despite increasing exchange rate flexibility, central banks in emerging markets still intervene in their foreign exchange markets for several reasons. In doing so, they face many operational questions, including on the degree of transparency and the choice of markets and counterparties. This paper identifies elements of best practice in official foreign exchange intervention, presents survey evidence on intervention practices in developing countries, and assesses the effectiveness of intervention in Mexico and Turkey.
This paper uses a stochastic continuous time model of the firm to study how different corporate governance structures affect the agency cost of debt. In the absence of asymmetric information, it shows that control of the firm by debtholders with a minority stake delays the exit decision and reduces the underinvestment problem. Such a governance structure may play an important role in diminishing conflicts between shareholders and debtholders.
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