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on Risk Management |
| By: | Sanjiv R. Das; Kris James Mitchener; Angela Vossmeyer |
| Abstract: | We employ a unique hand-collected dataset and a novel methodology to examine systemic risk before and after the largest U.S. banking crisis of the 20th century. Our systemic risk measure captures both the credit risk of an individual bank as well as a bank’s position in the network. We construct linkages between all U.S. commercial banks in 1929 and 1934 so that we can measure how predisposed the entire network was to risk, where risk was concentrated, and how the failure of more than 9,000 banks during the Great Depression altered risk in the network. We find that the pyramid structure of the commercial banking system (i.e., the network’s topology) created more inherent fragility, but systemic risk was nevertheless fairly dispersed throughout banks in 1929, with the top 20 banks contributing roughly 18% of total systemic risk. The massive banking crisis that occurred between 1930–33 raised systemic risk per bank by 33% and increased the riskiness of the very largest banks in the system. We use Bayesian methods to demonstrate that when network measures, such as eigenvector centrality and a bank’s systemic risk contribution, are combined with balance sheet data capturing ex ante bank default risk, they strongly predict bank survivorship in 1934. |
| JEL: | E42 E44 G01 G18 G21 L1 N12 N22 |
| Date: | 2018–12 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:25405&r=all |
| By: | Düll, Robert; König, Felix; Ohls, Jana |
| Abstract: | A sovereign debt crisis can have significant knock-on effects in the financial markets and put financial stability at risk. This paper focuses on the transmission of sovereign risk to insurance companies as some of the largest institutional investors in the sovereign bond market. We use a firm level panel dataset that covers large insurance companies, banks and non-financial firms from nine countries over the time period from 1 January 2008 to 1 May 2013. We find significant and robust transmission effects from sovereign risk to domestic insurers. The impact on insurers is not significantly different from that on banks but larger than for non-financial firms. We find that systemically important insurers are more closely linked to the domestic sovereign. Based on European data, we show that risks in sovereign bond portfolios are an important driver of insurer risk, which is not reflected in current insurance regulation (incl. Solvency II in Europe). |
| Keywords: | insurance; sovereign risk; sovereign bond portfolio |
| JEL: | G32 F3 G3 |
| Date: | 2017–08–01 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:83195&r=all |
| By: | Matyas Barczy; Adam Dudas; Jozsef Gall |
| Abstract: | We derive new approximations for the Value at Risk and the Expected Shortfall at high levels of loss distributions with positive skewness and excess kurtosis, and we describe their precisions for notable ones such as for exponential, Pareto type I, lognormal and compound (Poisson) distributions. Our approximations are motivated by extensions of the so-called Normal Power Approximation, used for approximating the cumulative distribution function of a random variable, incorporating not only the skewness but the kurtosis of the random variable in question as well. We show the performance of our approximations in numerical examples and we also give comparisons with some known ones in the literature. |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1811.06361&r=all |
| By: | Vincent Bouvatier (EconomiX - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique); Laetitia Lepetit (LAPE - Laboratoire d'Analyse et de Prospective Economique - IR SHS UNILIM - Institut Sciences de l'Homme et de la Société - UNILIM - Université de Limoges); Pierre-Nicolas Rehault (LAPE - Laboratoire d'Analyse et de Prospective Economique - IR SHS UNILIM - Institut Sciences de l'Homme et de la Société - UNILIM - Université de Limoges); Frank Strobel (University of Birmingham [Birmingham]) |
| Abstract: | We highlight caveats arising in the application of traditional ROA-based Z-scores for the measurement of bank insolvency risk, develop alternative Z-score measures to resolve these issues , and make recommendations for best practice for the US/Europe based on the experience of the …nancial crisis of 2007-2008. Using a probabilistic approach (i) our novel regulatory capital Z-score dominates traditional Z-score measures for both US/Europe; (ii) Z-scores computed with exponentially weighted moments dominate those with moving moments for the US sample, but not for Europe. For both US/Europe, using a multivariate logit approach (i) allows computation of augmented Z-scores that provide probabilities of distress that better discriminate between distressed/surviving banks than the probabilistic approach; (ii) suggests that the ROA-based Z-score using current values of the capital-asset ratio is best, calculated either with moving or exponentially weighted moments. |
| Keywords: | bank,insolvency risk,Z-score,risk measure |
| Date: | 2018–11–28 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-01937929&r=all |
| By: | Harjaat S. Bhamra; Christian Dorion; Alexandre Jeanneret; Michael Weber |
| Abstract: | We develop an asset-pricing model with endogenous corporate policies that explains how inflation jointly impacts real asset prices and corporate default risk. Our model includes two empirically grounded nominal frictions: fixed nominal coupons and sticky profitability. Taken together, these two frictions result in higher real equity prices and credit spreads when inflation falls. An increase in inflation has opposite effects, but with smaller magnitudes. In the cross section, the model predicts the negative impact of inflation on real equity values is stronger for low leverage firms. We find empirical support for the model predictions. |
| Keywords: | low inflation, default risk, equity, leverage, credit spreads |
| JEL: | E44 G12 G32 G33 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_7391&r=all |
| By: | Oliver de Groot (University of St Andrews); Alexander W. Richter (Federal Reserve Bank of Dallas); Nathaniel A. Throckmorton (College of William & Mary) |
| Abstract: | This paper shows the recent success of valuation risk (time-preference shocks in Epstein- Zin utility) in resolving asset pricing puzzles rests sensitively on an undesirable asymptote that occurs because the preference specification fails to satisfy a key restriction on the weights in the Epstein-Zin time-aggregator. In a Bansal-Yaron long-run risk model, our revised valuation risk specification that satisfies the restriction provides a superior empirical fit. The results also show that valuation risk no longer has a major role in matching the mean equity premium and risk-free rate but is crucial for matching the volatility and autocorrelation of the risk-free rate. |
| Keywords: | Epstein-Zin Utility; Valuation Risk; Equity Premium Puzzle; Risk-Free Rate Puzzle |
| JEL: | D81 G12 |
| Date: | 2018–12–17 |
| URL: | http://d.repec.org/n?u=RePEc:san:wpecon:1805&r=all |
| By: | Andreas H Hamel |
| Abstract: | This survey gives an introduction to monetary measures of risk as monotone and cash additive functions on spaces of univariate random variables. Primal and dual representation results as well as several examples are discussed. Principal ways to construct risk measures are given and extensions to more general situations indicated. |
| Date: | 2018–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1812.04354&r=all |
| By: | Sébastien Laurent (Aix-Marseille Univ., CNRS, EHESS, Centrale Marseille, AMSE & Aix-Marseille Graduate School of Management); Shuping Shi (Department of Economics, Macquarie University & Centre for Applied Macroeconomic Analysis (CAMA)) |
| Abstract: | Logarithms of prices of financial assets are conventionally assumed to follow drift-diffusion processes. While the drift term is typically ignored in the infill asymptotic theory and applications, the presence of nonzero drifts is an undeniable fact. The finite sample theory and extensive simulations provided in this paper reveal that the drift component has a nonnegligible impact on the estimation accuracy of volatility and leads to a dramatic power loss of a class of jump identification procedures. We propose an alternative construction of volatility estimators and jump tests and observe significant improvement of both in the presence of nonnegligible drift. As an illustration, we apply the new volatility estimators and jump tests, along with their original versions, to 21 years of 5-minute log-returns of the NASDAQ stock price index. |
| Keywords: | diffusion process, nonzero drift, finite sample theory, volatility estimation, jumps |
| JEL: | C12 C14 |
| Date: | 2018–12 |
| URL: | http://d.repec.org/n?u=RePEc:aim:wpaimx:1843&r=all |
| By: | Danielsson, Jon; Macrae, Robert; Micheler, Eva |
| Abstract: | Brexit is likely to cause considerable disruption for financial markets. Some worry that it may also increase systemic risk. This column revisits the debate and argues that an increase in systemic risk is unlikely. While legal ‘plumbing’ and institutional and regulatory equivalence are of concern, systemic risk is more likely to fall due to increased financial fragmentation and caution by market participants in the face of uncertainty. |
| JEL: | G32 |
| Date: | 2017–05–31 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:85124&r=all |
| By: | Name 1 Dieter Wang Email 1 (VU Amsterdam, De Nederlandsche Bank); Iman (I.P.P.) van Lelyveld (VU Amsterdam, De Nederlandsche Bank); Julia (J.) Schaumburg (VU Amsterdam) |
| Abstract: | This paper revisits the credit spread puzzle in bank CDS spreads from the perspective of information contagion. The puzzle, first detected in corporate bonds, consists of two stylized facts: Structural determinants of credit risk not only have low explanatory power but also fail to capture common factors in the residuals (Collin-Dufresne et al., 2001). For the case of banks, we hypothesize that the puzzle exists because of omitted network effects. We therefore extend the structural models to account for information spillovers based on bank business model similarities. To capture this channel, we propose and construct a new intuitive measure for portfolio overlap using the complete asset holdings of the largest banks in the Eurozone. Incorporating the network information into the structural model for bank credit spreads increases explanatory power and removes a systemic common factor as well as a North-South common factor from the residuals. Furthermore, neglecting the network likely overstates the importance of structural determinants. |
| Keywords: | Information contagion; credit spread puzzle; bank business model similarities; portfolio overlap measure; dynamic network effects model |
| JEL: | G01 G21 C32 C33 C38 |
| Date: | 2018–12–22 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20180100&r=all |
| By: | Sébastien Laurent (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - Ecole Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique, Aix-Marseille Graduate School of Management); Shuping Shi (Department of Economics, Macquarie University , Centre for Applied Macroeconomic Analysis (CAMA)) |
| Abstract: | Logarithms of prices of financial assets are conventionally assumed to follow drift-diffusion processes. While the drift term is typically ignored in the infill asymptotic theory and applications, the presence of nonzero drifts is an undeniable fact. The finite sample theory and extensive simulations provided in this paper reveal that the drift component has a nonnegligible impact on the estimation accuracy of volatility and leads to a dramatic power loss of a class of jump identification procedures. We propose an alternative construction of volatility estimators and jump tests and observe significant improvement of both in the presence of nonnegligible drift. As an illustration, we apply the new volatility estimators and jump tests, along with their original versions, to 21 years of 5-minute log-returns of the NASDAQ stock price index. |
| Keywords: | diffusion process,nonzero drift,finite sample theory,volatility estimation,jumps |
| Date: | 2018–12 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:halshs-01944449&r=all |
| By: | Dieter Wang; Iman van Lelyveld; Julia Schaumburg |
| Abstract: | This paper revisits the credit spread puzzle in bank CDS spreads from the perspective of information contagion. The puzzle, rst detected in corporate bonds, consists of two stylized facts: Structural determinants of credit risk not only have low explanatory power but also fail to capture a systematic common factor in the residuals (Collin-Dufresne et al., 2001). For the case of banks, we hypothesize that the puzzle exists because of omitted network effects. We therefore extend the structural models to account for information spillovers based on bank business model similarities. To capture this channel, we propose and construct a new intuitive measure for portfolio overlap using the complete asset holdings of the largest banks in the Eurozone. Incorporating the network information into the structural model for bank credit spreads increases explanatory power and explains the systemic common factor in the residuals. |
| Keywords: | Information contagion; credit spread puzzle; bank business model similarities; portfolio overlap measure; dynamic network effects model |
| JEL: | G01 G21 C32 C33 C38 |
| Date: | 2018–12 |
| URL: | http://d.repec.org/n?u=RePEc:dnb:dnbwpp:619&r=all |
| By: | Nadine M Walters; Conrad Beyers; Gusti van Zyl; Rolf van den Heever |
| Abstract: | We present a network-based framework for simulating systemic risk that considers shock propagation in banking systems. In particular, the framework allows the modeller to reflect a top-down framework where a shock to one bank in the system affects the solvency and liquidity position of other banks, through systemic market risks and consequential liquidity strains. We illustrate the framework with an application using South African bank balance sheet data. Spikes in simulated assessments of systemic risk agree closely with spikes in documented subjective assessments of this risk. This indicates that network models can be useful for monitoring systemic risk levels. The model results are sensitive to liquidity risk and market sentiment and therefore the related parameters are important considerations when using a network approach to systemic risk modelling. |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1811.04223&r=all |
| By: | Ana Sasi-Brodesky (Bank of Israel) |
| Abstract: | This paper examines default events in Israel's corporate bond market between 2008 and 2015. Using a sample of 106 distress events, the variation in expected recovery rates over time is analyzed. The value of distressed firms at the time of default was found to be mostly influenced by the financial conditions of peers in the industry and in the market. In particular, low liquidity and high average leverage ratios of other market participants had a negative effect on the anticipated recovery rate. Firm-specific characteristics were found to have negligible effect on expected recovery rates. Average recovery and default rates are shown to compare well with the experience in other countries. |
| Keywords: | recovery rates, default, bond market, Israel, market price |
| JEL: | G33 |
| Date: | 2017–07 |
| URL: | http://d.repec.org/n?u=RePEc:boi:wpaper:2107.17&r=all |
| By: | International Monetary Fund |
| Abstract: | The financial system has been resilient through the severe recession. Banks and investment funds dominate Brazil’s financial system landscape. The banking sector has continued to be well-capitalized, profitable, and liquid. Profitability has been supported by prudent lending standards, high interest margins and robust fee income, despite record loan losses. Outstanding nonperforming loans have increased marginally during the recession largely because banks have actively written off bad loans. The investment fund industry has also been solid, enjoying a steady growth of assets under management without experiencing net outflows, in aggregate, during the recession. Market-based indicators point to relatively low levels of systemic risk in 2017. However, the outlook for the nonbank sector will become more challenging in the environment of lower interest rates, as lower returns will affect investment income and a search for yield may increase risk-taking. |
| Date: | 2018–11–30 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfscr:18/344&r=all |
| By: | Qiang Zhang; Rui Luo; Yaodong Yang; Yuanyuan Liu |
| Abstract: | Volatility is a quantity of measurement for the price movements of stocks or options which indicates the uncertainty within financial markets. As an indicator of the level of risk or the degree of variation, volatility is important to analyse the financial market, and it is taken into consideration in various decision-making processes in financial activities. On the other hand, recent advancement in deep learning techniques has shown strong capabilities in modelling sequential data, such as speech and natural language. In this paper, we empirically study the applicability of the latest deep structures with respect to the volatility modelling problem, through which we aim to provide an empirical guidance for the theoretical analysis of the marriage between deep learning techniques and financial applications in the future. We examine both the traditional approaches and the deep sequential models on the task of volatility prediction, including the most recent variants of convolutional and recurrent networks, such as the dilated architecture. Accordingly, experiments with real-world stock price datasets are performed on a set of 1314 daily stock series for 2018 days of transaction. The evaluation and comparison are based on the negative log likelihood (NLL) of real-world stock price time series. The result shows that the dilated neural models, including dilated CNN and Dilated RNN, produce most accurate estimation and prediction, outperforming various widely-used deterministic models in the GARCH family and several recently proposed stochastic models. In addition, the high flexibility and rich expressive power are validated in this study. |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1811.03711&r=all |
| By: | Lucia Cipolina-Kun; Ignacio Ruiz; Mariano Zero-Medina Laris |
| Abstract: | The use of CVA to cover credit risk is widely spread, but has its limitations. Namely, dealers face the problem of the illiquidity of instruments used for hedging it, hence forced to warehouse credit risk. As a result, dealers tend to offer a limited OTC derivatives market to highly risky counterparties. Consequently, those highly risky entities rarely have access to hedging services precisely when they need them most. In this paper we propose a method to overcome this limitation. We propose to extend the CVA risk-neutral framework to compute an initial margin (IM) specific to each counterparty, which depends on the credit quality of the entity at stake, transforming the effective credit rating of a given netting set to AAA, regardless of the credit rating of the counterparty. By transforming CVA requirement into IM ones, as proposed in this paper, an institution could rely on the existing mechanisms for posting and calling of IM, hence ensuring the operational viability of this new form of managing warehoused risk. The main difference with the currently standard framework is the creation of a Specific Initial Margin, that depends in the credit rating of the counterparty and the characteristics of the netting set in question. In this paper we propose a methodology for such transformation in a sound manner, and hence this method overcomes some of the limitations of the CVA framework. |
| Date: | 2018–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1812.09407&r=all |