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on Risk Management |
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Issue of 2020‒12‒07
29 papers chosen by |
| By: | Magdalena Tywoniuk (Swiss Finance Institute, Students; University of Geneva - Geneva Finance Research Institute (GFRI); Swiss Finance Institute, Students) |
| Abstract: | Following the 2008 financial crisis, regulation mandates the clearing of the CDS market through Central Clearing Counter-parties (CCPs). Large CCPs are now designated as ’Global Systemically Important Institutions’ (GSIIs), whose unlikely-but-plausible failure threatens global financial market stability. This work examines CCP resilience following a large dealer member’s default and the ensuing default contagion. In unwinding the defaulter’s positions, the CCP faces the price impact of constrained member liquidations and unconstrained members’ predatory selling. The variation margin captures the effect of price-mediated contagion and its amplification. A novel spatial measure captures the covariance between members’ CDS holdings and the CDS being unwound. Key results show: Liquidations by constrained members lower the CCP’s profits and make cds-spreads less informative. There exists a strong conflict between predatory competition and dealer distress, which inadvertently makes dealers prey on themselves. In turn, the adoption of a risk-sharing guarantee fund structure would provide a natural disciplinary mechanism for predation – minimizing overall CCP and member losses. A dynamic simulation, calibrated to OTC market data, supports these theoretical results with parameter magnitudes and sensitivities. Examination of three market liquidity scenarios provides intuition for effective liquidity injection by a Lender of Last Resort. |
| Keywords: | Systemic Risk, CCP Recovery, CDS, CDS Spread Fire Sales, Liquidation, Predation, Price Impact, Contagion, Financial Network, Over the Counter Markets |
| JEL: | G00 G01 G02 G14 G10 G18 G20 G23 G33 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2095&r=all |
| By: | Nicola Borri (LUISS University); Giorgio Di Giorgio (LUISS University) |
| Abstract: | This paper studies the systemic risk contribution of a set of large publicly traded European banks. Over a sample covering the last twenty years and three different crises, we find that all banks in our sample significantly contribute to systemic risk. Moreover, larger banks and banks with a business model more exposed to trading and financial market volatility, contribute more. In the shorter sample characterized by the Covid-19 shock, sovereign default risks significantly affected the systemic risk contribution of all banks. However, the ECB announcement of the Pandemic Emergency Purchasing Programme restored calm in the European banking sector. |
| Keywords: | CoVaR, systemic risk, Covid-19, banking regulation |
| JEL: | G01 G18 G21 G38 |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:lui:casmef:2005&r=all |
| By: | Hamidreza Arian; Hossein Poorvasei; Azin Sharifi; Shiva Zamani |
| Abstract: | Extreme Value Theory (EVT) is one of the most commonly used approaches in finance for measuring the downside risk of investment portfolios, especially during financial crises. In this paper, we propose a novel approach based on EVT called Uncertain EVT to improve its forecast accuracy and capture the statistical characteristics of risk beyond the EVT threshold. In our framework, the extreme risk threshold, which is commonly assumed a constant, is a dynamic random variable. More precisely, we model and calibrate the EVT threshold by a state-dependent hidden variable, called Break-Even Risk Threshold (BRT), as a function of both risk and ambiguity. We will show that when EVT approach is combined with the unobservable BRT process, the Uncertain EVT's predicted VaR can foresee the risk of large financial losses, outperforms the original EVT approach out-of-sample, and is competitive to well-known VaR models when back-tested for validity and predictability. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.06693&r=all |
| By: | Zehra Eksi (Vienna University of Economics and Business, Institute for Statistics and Mathematics); Damir Filipović (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute) |
| Abstract: | This study deals with the pricing and hedging of single-tranche collateralized debt obligations (STCDOs). We specify an affine two-factor model in which a catastrophic risk component is incorporated. Apart from being analytically tractable, this model has the feature that it captures the dynamics of super-senior tranches, thanks to the catastrophic component. We estimate the factor model based on the iTraxx Europe data with six tranches and four different maturities, using a quasi-maximum likelihood (QML) approach in conjunction with the Kalman filter. We derive the model-based variance-minimizing strategy for the hedging of STCDOs with a dynamically rebalanced portfolio on the underlying swap index. We analyze the actual performance of the variance-minimizing hedge on the iTraxx Europe data. In order to assess the hedging performance further, we run a simulation analysis where normal and extreme loss scenarios are generated via the method of importance sampling. Both in-sample hedging and simulation analysis suggest that the variance-minimizing strategy is most effective for mezzanine tranches in terms of yielding less riskier hedging portfolios and it fails to provide adequate hedge performance regarding equity tranches. |
| Keywords: | single-tranche CDO, affine term-structure of credit spreads, catastrophic risk, variance minimizing hedge |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2094&r=all |
| By: | Seyed Mohammad Sina Seyfi; Azin Sharifi; Hamidreza Arian |
| Abstract: | Monte Carlo Approaches for calculating Value-at-Risk (VaR) are powerful tools widely used by financial risk managers across the globe. However, they are time consuming and sometimes inaccurate. In this paper, a fast and accurate Monte Carlo algorithm for calculating VaR and ES based on Gaussian Mixture Models is introduced. Gaussian Mixture Models are able to cluster input data with respect to market's conditions and therefore no correlation matrices are needed for risk computation. Sampling from each cluster with respect to their weights and then calculating the volatility-adjusted stock returns leads to possible scenarios for prices of assets. Our results on a sample of US stocks show that the Gmm-based VaR model is computationally efficient and accurate. From a managerial perspective, our model can efficiently mimic the turbulent behavior of the market. As a result, our VaR measures before, during and after crisis periods realistically reflect the highly non-normal behavior and non-linear correlation structure of the market. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.07994&r=all |
| By: | Gurgone, Andrea; Iori, Giulia |
| Abstract: | To date, macroprudential policy inspired by the Basel III package is applied irrespective of the network characteristics of the banking system. We study how the implementation of macroprudential policy in the form of additional capital requirements conditional to systemic-risk measures of banks should regard the degree of heterogeneity of financial networks. We adopt a multi-agent approach describing an artificial economy with households, firms, and banks in which occasional liquidity crises emerge. We shape the configuration of the financial network to generate two polar worlds: one is characterized by few banks who lend most of the credit to the real sector while borrowing interbank liquidity. The other shows a higher degree of homogeneity. We focus on a capital buffer for SII and two buffers built on measures of systemic impact and vulnerability. The research suggests that the criteria for the identification of systemic-important banks may change with the network heterogeneity. Thus, capital buffers should be calibrated on the heterogeneity of the financial networks to stabilize the system, otherwise they may be ineffective. Therefore, we argue that prudential regulation should account for the characteristics of the banking networks and tune macroprudential tools accordingly. |
| Keywords: | agent-based model,capital requirements,capital buffers,,financial networks,macroprudential policy,systemic-risk |
| JEL: | C63 D85 E44 G01 G21 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bamber:164&r=all |
| By: | Barbora Stepankova (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Opletalova 26, 110 00, Prague, Czech Republic) |
| Abstract: | Some financial institutions can use internally developed credit risk models to determine their capital requirements. At the same time, the regulatory framework governing such models allows institutions to implement diverse rating systems with no specified penalty for poor model performance. To what extent the resulting model risk { potential for equivalent models to deliver inconsistent outcomes { is prevalent in the economy is largely unknown. We use a unique dataset of 4.9 million probability of default estimates provided by 28 global IRB banks, covering the January 2016 to June 2020 period, to assess the degree of variance in credit risk estimates provided by multiple banks for a single entity. In line with the prior literature, we find that there is a substantial variance in outcomes and that it decreases with the amount of available information about the assessed entity. However, we further show that the level of variance is highly dependent on the entity type, its industry and locations of the entity and contributing banks; banks report a higher deviation from the mean credit risk for foreign entities. Further, we conclude that a considerable part of the variance is systematic, especially for fund models. Finally, utilising the latest available data, we show the massive impact of the COVID-19 pandemic on dispersion of credit estimates. |
| Keywords: | Banking, Credit Risk, Bank Regulation |
| JEL: | C12 G21 G32 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:fau:wpaper:wp2020_44&r=all |
| By: | Megaritis, Anastasios; Vlastakis, Nikolaos; Triantafyllou, Athanasios |
| Abstract: | In this paper we examine the predictive power of latent macroeconomic uncertainty on US stock market volatility and jump tail risk. We find that increasing macroeconomic uncertainty predicts a subsequent rise in volatility and price jumps in the US equity market. Our analysis shows that the latent macroeconomic uncertainty measure of Jurado et al. (2015) has the most significant and long-lasting impact on US stock market volatility and jumps in the equity market when compared to the respective impact of the VIX and other popular observable uncertainty proxies. Our study is the first to show that the latent macroeconomic uncertainty factor outperforms the VIX when forecasting volatility and jumps after the 2007 US Great Recession. We additionally find that latent macroeconomic uncertainty is a common forecasting factor of volatility and jumps of the intraday returns of S&P 500 constituents and has higher predictive power on the volatility and jumps of the equities which belong to the financial sector. Overall, our empirical analysis shows that stock market volatility is significantly affected by the rising degree of unpredictability in the macroeconomy, while it is relatively immune to shocks in observable uncertainty proxies. |
| Keywords: | Jumps, Bipower variation, Realized volatility, Macroeconomic Uncertainty |
| Date: | 2020–11–26 |
| URL: | http://d.repec.org/n?u=RePEc:esy:uefcwp:29200&r=all |
| By: | Kevin J. Stiroh |
| Abstract: | Remarks at the 2020 Climate Risk Symposium, Global Association of Risk Professionals (delivered via videoconference). |
| Keywords: | climate change; supervision; banking; management; Task Force on Climate-Related Financial Risks (TFCR); financial risk |
| Date: | 2020–11–10 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednsp:89062&r=all |
| By: | Ge, S. |
| Abstract: | This paper uses extensive text data to construct firms' links via which local shocks transmit. Using the novel text-based linkages, I estimate a heterogeneous spatial-temporal model which accommodates the contemporaneous and dynamic spillover effects at the same time. I document a considerable degree of local risk spillovers in the market plus sector hierarchical factor model residuals of S&P 500 stocks. The method is found to outperform various previously studied methods in terms of out-of-sample fit. Network analysis of the spatial-temporal model identifies the major systemic risk contributors and receivers, which are of particular interest to microprudential policies. From a macroprudential perspective, a rolling-window analysis reveals that the strength of local risk spillovers increases during periods of crisis, when, on the other hand, the market factor loses its importance. |
| Keywords: | Excess co-movement, weak and strong cross-sectional dependence, local risk spillovers, networks, textual analysis, big data, systemic risk, heterogeneous spatial auto-regressive model (HSAR) |
| JEL: | C33 C58 G10 G12 |
| Date: | 2020–11–26 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:20115&r=all |
| By: | Gulcan Yildirim Gungor; Tuba Pelin Sumer |
| Abstract: | [EN] This note aims to estimate credit riskiness of the corporate sector in Turkey with alternative methods for January 2007 -March 2019 period. Initially, probability of default is calculated by option pricing method for the listed companies and the relationship with non-performing loan (NPL) ratio is examined. In the one-year period following the increase (decrease) in the probability of default, a similar upward (downward) movement is observed in the corporate NPL ratio of the banking sector. Since the option pricing method focuses on relatively large scale companies listed on the stock exchange, credit riskiness is also calculated using NPL additions and commercial loan interest rates to increase the comprehensiveness of the study and include financials of the relatively small scale firms (SMEs). Although the sample size and assumptions differ, credit risk indicators estimated by alternative methods move together.Therefore, the credit riskiness indicators estimated with high frequency market data is important for monitoring the financial fragilities of corporate sector and their reflections on asset quality of the banking sector. [TR] Bu calismada, Turkiye’de faaliyet gosteren reel sektor firmalarinin kredi riskliligi alternatif yontemlerle Ocak 2007-Mart 2019 donemi icin tahmin edilmektedir. Oncelikle opsiyon fiyatlama yontemiyle borsaya kote firmalar icin temerrut olasiligi hesaplanmakta ve firma kredisi tahsili gecikmis alacak (TGA) oraniyla arasindaki iliski incelenmektedir. Analiz sonuclarina gore reel sektorun temerrut olasiligindaki artisi (azalisi) izleyen bir yillik surecte bankacilik sektoru TGA oraninda da benzer bir yukari (asagi) yonlu hareket oldugu gorulmektedir. Opsiyon fiyatlama yonteminde borsaya kote gorece buyuk olcekli firmalara odaklanildigi icin, temsil kuvvetini arttirmak ve nispeten kucuk olcekli firmalarin finansal gelismelerini de analize dahil etmek amaciyla kredi riskliligi, TGA ilaveleri ve ticari kredi faiz oranlari kullanilarak da hesaplanmaktadir. Kapsanan orneklem ve varsayimlar farkli olsa da alternatif yontemlerle hesaplanan kredi riski gostergelerinin beraber hareket ettigi gorulmektedir. Dolayisiyla, yuksek frekanstaki piyasa verileri kullanilarak hesaplanan kredi riskliligi gostergelerinin, gecikmeli finansal tabloveri akisina sahip reel kesim firmalarinin finansal kirilganliklarinin izlenmesi ve bankacilik sektoru aktif kalitesine yansimasi icin onemli bir gosterge oldugu degerlendirilmektedir. |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:tcb:econot:2017&r=all |
| By: | Nishimura, Yukihiro; Pestieau, Pierre (Université catholique de Louvain, LIDAM/CORE, Belgium) |
| Abstract: | We consider a society where individuals differ according to their pro- ductivity and their risk of mortality and dependency. We show that ac- cording to the most reasonable estimates of correlations among these three characteristics, if one had to choose between a public pension system and a long-term care social insurance, the latter should be chosen by a utili- tarian social planner. With a Rawlsian planner, the balance between the two schemes does depend on the comparison between the ratio of the sur- vival probability to the dependence risk of the poor with its population average. |
| Keywords: | long term care, pension, mortality risk, optimal taxation, liquidity constraints |
| JEL: | H2 H5 |
| Date: | 2020–10–06 |
| URL: | http://d.repec.org/n?u=RePEc:cor:louvco:2020030&r=all |
| By: | Bogle, David A.; Coyle, Christopher; Turner, John D. |
| Abstract: | What shapes and drives capital market development over the long run? In this paper, using the asset portfolios of UK life assurers, we examine the role of regulation, historical contingency and political reactions to events on the long-run development of the UK capital market. Government response to events such as war, hegemonysecured peace, and the wider macroeconomic environment was the ultimate determinant of major changes in asset allocation since 1800. Furthermore, when we compare the UK with the United States, we find that regulation played a limited role in shaping the asset portfolios of the UK life assurance industry. |
| Keywords: | Capital markets,asset management,life assurance,mutuals,mergers,regulation,United Kingdom,United States |
| JEL: | G11 G22 N20 N40 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:qucehw:202009&r=all |
| By: | World Bank |
| Keywords: | Finance and Financial Sector Development - Insurance & Risk Mitigation Urban Development - Hazard Risk Management Public Sector Development - Public Financial Management |
| Date: | 2020–08 |
| URL: | http://d.repec.org/n?u=RePEc:wbk:wboper:34440&r=all |
| By: | Silvana Pesenti; Qiuqi Wang; Ruodu Wang |
| Abstract: | Optimization of distortion riskmetrics with distributional uncertainty has wide applications in finance and operations research. Distortion riskmetrics include many commonly applied risk measures and deviation measures, which are not necessarily monotone or convex. One of our central findings is a unifying result that allows us to convert an optimization of a non-convex distortion riskmetric with distributional uncertainty to a convex one, leading to great tractability. The key to the unifying equivalence result is the novel notion of closedness under concentration of sets of distributions. Our results include many special cases that are well studied in the optimization literature, including but not limited to optimizing probabilities, Value-at-Risk, Expected Shortfall, and Yaari's dual utility under various forms of distributional uncertainty. We illustrate our theoretical results via applications to portfolio optimization, optimization under moment constraints, and preference robust optimization. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.04889&r=all |
| By: | L. Bauwens; E. Otranto |
| Abstract: | Time series of realized covariance matrices can be modelled in the conditional autoregressive Wishart model family via dynamic correlations or via dynamic covariances. Extended parameterizations of these models are proposed, which imply a specific and time-varying impact parameter of the lagged realized covariance (or correlation) on the next conditional covariance (or correlation) of each asset pair. The proposed extensions guarantee the positive definiteness of the conditional covariance or correlation matrix with simple parametric restrictions, while keeping the number of parameters fixed or linear with respect to the number of assets. An empirical study on twenty-nine assets reveals that the extended models have superior forecasting performances than their simpler versions. |
| Keywords: | realized covariances;dynamic covariances and correlations;Hadamard exponential matri |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:cns:cnscwp:202007&r=all |
| By: | Yao Axel Ehouman |
| Abstract: | This paper re-examines the dependence structure between uncertainty in oil prices and sovereign credit risk of oil exporters. To address this issue, we employ a copula approach that allows us to capture a myriad of complex and nonlinear dependence structures. Empirical analyses involve daily data of the 5-year sovereign credit default swaps spreads and the crude oil implied volatility from January 2010 to May 2019, covering a sample of ten oil-exporting countries. Except for Brazil and Venezuela, our results provide evidence of significant positive and upper tail dependence in the relationship between oil market uncertainty and oil exporters’ sovereign risk. Overall, our findings highlight that high uncertainty in oil prices coincides with large-scale increases in the sovereign credit risk of oil-exporting countries, supporting the hypothesis that investors, exposed to economic losses from risk events in oil exporters, are all the more pessimistic that prevails high uncertainty about future oil prices. Our findings have implications for oil exporter’ policymakers as well as investors. |
| Keywords: | Copula; Dependence; Oil market; Sovereign credit risk; Uncertainty |
| JEL: | C1 F3 G1 Q4 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:drm:wpaper:2020-31&r=all |
| By: | Schmidhammer, Christoph; Hille, Vanessa; Wiedemann, Arnd |
| Abstract: | This paper analyses theperformance ofmaturity transformation strategiesduring a period of high and low interest rates. Based on German government bond yieldsfrom September 1972 to May 2019,we construct a rolling window of bond ladders where long-term assets are financed by short-term liabilities. Risk and return increase significantly with maturity gaps for both sample periods. During theperiod of low interest rates,dominant strategies can be observed for short-term and medium-term gaps.With respect to different financial reporting standards,weaddress maturity transformation results froman earnings-based perspective as well as froma market value-based perspective. |
| Keywords: | Maturity Transformation,Bond Ladders,Period of Low Interest Rates,Performance,Interest Rate Risk |
| JEL: | G11 G12 G21 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bubdps:582020&r=all |
| By: | Hirdesh K. Pharasi; Eduard Seligman; Suchetana Sadhukhan; Thomas H. Seligman |
| Abstract: | Based on previous developments of the concept of market states using correlation matrices, in the present paper we address the dynamical evolution of correlation matrices in time. This will imply minor modifications to the market states themselves, due to increased attention to the transition matrix between the states. We will introduce trajectories of the correlation matrices by considering one day shifts for the epoch used to calculate the correlation matrices and will visualize both the states and the trajectories after dimensional scaling. This approach using dynamics improves the options of risk assessment and opens the door to dynamical treatments of markets and shows noise suppression in a new light. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.05984&r=all |
| By: | P\'al Andr\'as Papp; Roger Wattenhofer |
| Abstract: | We consider financial networks, where banks are connected by contracts such as debts or credit default swaps. We study the clearing problem in these systems: we want to know which banks end up in a default, and what portion of their liabilities can these defaulting banks fulfill. We analyze these networks in a sequential model where banks announce their default one at a time, and the system evolves in a step-by-step manner. We first consider the reversible model of these systems, where banks may return from a default. We show that the stabilization time in this model can heavily depend on the ordering of announcements. However, we also show that there are systems where for any choice of ordering, the process lasts for an exponential number of steps before an eventual stabilization. We also show that finding the ordering with the smallest (or largest) number of banks ending up in default is an NP-hard problem. Furthermore, we prove that defaulting early can be an advantageous strategy for banks in some cases, and in general, finding the best time for a default announcement is NP-hard. Finally, we discuss how changing some properties of this setting affects the stabilization time of the process, and then use these techniques to devise a monotone model of the systems, which ensures that every network stabilizes eventually. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.10485&r=all |
| By: | Theissen, Erik; Yilanci, Can |
| Abstract: | Risk-adjusted momentum returns are usually estimated by sorting stocks into a regularly rebalanced long-short portfolio based on their prior return and then running a full-sample regression of the portfolio returns on a set of factors (portfolio-level risk adjustment). This approach implicitly assumes constant factor exposure of the momentum portfolio. However, momentum portfolios are characterized by high turnover and time-varying factor exposure. We propose to estimate the risk exposure at the stock-level. The risk-adjusted return of the momentum portfolio in month t then is the actual return minus the weighted average of the expected returns of the component stocks (stock-level risk adjustment). Based on evidence from the universe of CRSP stocks, from sub-periods and size-based sub-samples, from volatility-scaled momentum strategies (Barroso and Santa-Clara 2015) and from an international sample covering 20 developed countries, we conclude that the momentum effect may be much weaker than previously thought. |
| Keywords: | Momentum,Risk adjustment,Time-series regression |
| JEL: | C58 G12 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cfrwps:2009&r=all |
| By: | Laurent Ferrara; Matteo Mogliani; Jean-Guillaume Sahuc |
| Abstract: | Monitoring changes in financial conditions provides valuable information on the contribution of financial risks to future economic growth. For that purpose, central banks need real-time indicators to adjust promptly the stance of their policy. We extend the quarterly Growth-at-Risk (GaR) approach of Adrian et al. (2019) by accounting for the high-frequency nature of financial conditions indicators. Specifically, we use Bayesian mixed data sampling (MIDAS) quantile regressions to exploit the information content of both a financial stress index and a financial conditions index leading to real-time high-frequency GaR measures for the euro area. We show that our daily GaR indicator (i) provides an early signal of GDP downturns and (ii) allows day-to-day assessment of the effects of monetary policies. During the first six months of the Covid-19 pandemic period, it has provided a timely measure of tail risks on euro area GDP. |
| Keywords: | Growth-at-Risk, mixed-data sampling, Bayesian quantile regressions, financial conditions, euro area. |
| JEL: | C22 E37 E44 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:een:camaaa:2020-97&r=all |
| By: | Flavio Angelini; Katia Colaneri; Stefano Herzel; Marco Nicolosi |
| Abstract: | We study the optimal asset allocation problem for a fund manager whose compensation depends on the performance of her portfolio with respect to a benchmark. The objective of the manager is to maximise the expected utility of her final wealth. The manager observes the prices but not the values of the market price of risk that drives the expected returns. The estimates of the market price of risk get more precise as more observations are available. We formulate the problem as an optimization under partial information. The particular structure of the incentives makes the objective function not concave. We solve the problem via the martingale method and, with a concavification procedure, we obtain the optimal wealth and the investment strategy. A numerical example shows the effect of learning on the optimal strategy. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.07871&r=all |
| By: | José-Luis Peydró; Andrea Polo; Enrico Sette |
| Abstract: | We show that risk mitigating incentives dominate risk shifting incentives in fragile banks. Risk shifting could be particularly severe in banking since it is the most opaque industry and banks are one of the most leveraged corporations with very low skin in the game. To analyze this question, we exploit security trading by banks during financial crises, as banks can easily and quickly change their risk exposure within their security portfolio. However, in contrast with the risk shifting hypothesis, we find that less capitalized banks take relatively less risk after financial market stress shocks. We show this using the supervisory ISIN-bank-month level dataset from Italy with all securities for each bank. Our results are over and above capital regulation as we show lower reach-for-yield effects by less capitalized banks within government bonds (with zero risk weights) or within securities with the same rating and maturity in the same month (which determines regulatory capital). Effects are robust to controlling for the covariance with the existence portfolio, and less capitalized banks, if anything, reduce concentration risk. Further, effects are stronger when uncertainty is higher, despite that risk shifting motives may be then higher. Moreover, three separate tests – based on different accounting portfolios (trading book versus held to maturity), the distribution of capital and franchise value – suggest that bank own incentives, instead of supervision, are the main drivers. Results are confirmed if we consider other sources of balance sheet fragility and different measures of risk-taking. Finally, evidence from the recent COVID-19 shock corroborates findings from the Global Financial Crisis and the Euro Area Sovereign Crisis. |
| Keywords: | risk shifting, financial crises, securities, bank capital, interbank funding, concentration risk, uncertainty, risk weights, available for sale, held to maturity, trading book, COVID-19 |
| JEL: | G01 G21 G28 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:bge:wpaper:1219&r=all |
| By: | Riza Demirer (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026- 1102, USA); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Jacobus Nel (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany) |
| Abstract: | We extend the literature on the effect of rare disaster risks on commodities by examining the effect of the El Nino- Southern Oscillation (ENSO) on crude oil via the recently developed k-th order nonparametric causality-in-quantiles framework, utilizing a long range historical data set spanning the period 1876:01 to 2020:10. The methodology allows us to test for the predictive role of ENSO over the entire conditional distribution of not only real oil returns but also its volatility, by controlling for misspecification due to uncaptured nonlinearity and regime-changes. Empirical findings show that the Southern Oscillation Index (SOI), measuring the ENSO cycle, not only predicts real oil returns, but also volatility, over the entirety of the respective conditional distributions. The findings highlight the role of rare disaster risks over not only financial markets, but also commodities with significant implications for policymakers and investors. |
| Keywords: | El Nino-Southern Oscillation, Real Oil Returns and Volatility, Higher-Order Nonparametric Causality in Quantiles Test |
| JEL: | C22 Q41 Q55 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:2020104&r=all |
| By: | Yuanhua Feng (Paderborn University); Jan Beran (University of Konstanz); Sebastian Letmathe (Paderborn University); Sucharita Ghosh (Swiss Federal Research Institute WSL) |
| Abstract: | Volatility modelling is applied in a wide variety of disciplines, namely finance, en- vironment and societal disciplines, where modelling conditional variability is of in- terest e.g. for incremental data. We introduce a new long memory volatility model, called FI-Log-GARCH. Conditions for stationarity and existence of fourth moments are obtained. It is shown that any power of the squared returns shares the same memory parameter. Asymptotic normality of sample means is proved. The practical performance of the proposal is illustrated by an application to one-day rolling forecasts of the VaR (value at risk) and ES (expected shortfall). Comparisons with FIGARCH, FIEGARCH and FIAPARCH models are made using a criterion based on different traffic light test. The results of this paper indicate that the FI-Log- GARCH often outperforms the other models, and thus provides a useful alternative to existing long memory volatility models. |
| Keywords: | FI-Log-GARCH, stationary solutions, finite fourth moments, covariance structure, rolling forecasting VaR and ES, traffic light test of ES |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:pdn:ciepap:137&r=all |
| By: | Marco Caliendo; Deborah A. Cobb-Clark; Cosima Obst; Arne Uhlendorff |
| Abstract: | We analyze workers’ risk preferences and training investments. Our conceptual framework differentiates between the investment risk and insurance mechanisms underpin-ning training decisions. Investment risk leads risk-averse workers to train less; they undertake more training if it insures them against future losses. We use the German Socio-Economic Panel (SOEP) to demonstrate that risk affinity is associated with more training, implying that, on average, investment risks dominate the insurance benefits of training. Crucially, this relationship is evident only for general training; there is no relationship between risk attitudes and specific training. Thus, as expected, risk preferences matter more when skills are transferable – and workers have a vested interest in training outcomes – than when they are not. Finally, we provide evidence that the insurance benefits of training are concentrated among workers with uncertain employment relationships or limited access to public insurance schemes. |
| Keywords: | Human Capital Investment, Work-related Training, Risk Preferences |
| JEL: | J24 C23 D81 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:diw:diwsop:diw_sp1113&r=all |
| By: | Jorge Gonz\'alez C\'azares; Aleksandar Mijatovi\'c |
| Abstract: | We develop a computational method for expected functionals of the drawdown and its duration in exponential L\'evy models. It is based on a novel simulation algorithm for the joint law of the state, supremum and time the supremum is attained of the Gaussian approximation of a general L\'evy process. We bound the bias for various locally Lipschitz and discontinuous payoffs arising in applications and analyse the computational complexities of the corresponding Monte Carlo and multilevel Monte Carlo estimators. Monte Carlo methods for L\'evy processes (using Gaussian approximation) have been analysed for Lipschitz payoffs, in which case the computational complexity of our algorithm is up to two orders of magnitude smaller when the jump activity is high. At the core of our approach are bounds on certain Wasserstein distances, obtained via the novel SBG coupling between a L\'evy process and its Gaussian approximation. Numerical performance, based on the implementation in the dedicated GitHub repository, exhibits a good agreement with our theoretical bounds. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.06618&r=all |
| By: | Ghamami, Samim; Glasserman, Paul; Young, Hobart |
| Abstract: | This paper studies the spread of losses and defaults in financial networks with two interrelated features: collateral requirements and alternative contract termination rules. When collateral is committed to a firm’s counterparties, a solvent firm may default if it lacks sufficient liquid assets to meet its payment obligations. Collateral requirements can thus increase defaults and payment shortfalls. Moreover, one firm may benefit from the failure of another if the failure frees collateral committed by the surviving firm, giving it additional resources to make other payments. Contract termination at default may also improve the ability of other firms to meet their obligations through access to collateral. As a consequence of these features, the timing of payments and collateral liquidation must be carefully specified to establish the existence of payments that clear the network. Using this framework, we show that dedicated collateral may lead to more defaults than pooled collateral; we study the consequences of illiquid collateral for the spread of losses through fire sales; we compare networks with and without selective contract termination; and we analyze the impact of alternative resolution and bankruptcy stay rules that limit the seizure of collateral at default. Under an upper bound on derivatives leverage, full termination reduces payment shortfalls compared with selective termination. |
| Keywords: | contagion; OTC markets; financial regulation; network; fire sales; collateral; automatic stays for qualified financial contracts; forthcoming |
| JEL: | J50 |
| Date: | 2020–11–03 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:107496&r=all |