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on Risk Management |
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Issue of 2022‒08‒15
nineteen papers chosen by |
| By: | Georgios I. Papayiannis |
| Abstract: | Freight rate derivatives constitute a very popular financial tool in shipping industry, that allows to the market participants and the individuals operating in the field, to reassure their financial positions against the risk occurred by the volatility of the freight rates. The special structure of the shipping market attracted the interest of both academics and practitioners, since pricing of the related traded options which are written on non-storable assets (i.e. the freight service) is not a trivial task. Management of freight risk is of major importance to preserve the viability of shipping operations, especially in periods where shocks appear in the world economy, which introduces uncertainty in the freight rate prices. In practice, the reduction of freight risk is almost exclusively performed by constructing hedging portfolios relying on freight rate options. These portfolios needs to be robust to the market uncertainties, i.e. to choose the portfolio which returns will be as less as it gets affected by the market changes. Especially, for time periods where the future states of the market (even in short term) are extremely ambiguous, i.e. there are a number of different scenarios that can occur, it is of great importance for the firms to decide robustly to these uncertainties. In this work, a framework for the robust treatment of model uncertainty in (a) modeling the freight rates dynamics employing the notion of Wasserstein barycenter and (b) in choosing the optimal hedging strategy for freight risk management, is proposed. A carefully designed simulation study in the discussed hedging problem, employing standard modelling approaches in freight rates literature, illustrates the capabilities of the proposed method with very satisfactory results in approximating the optimal strategy even in high noise cases. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2207.00862&r= |
| By: | Timo Dimitriadis; Yannick Hoga |
| Abstract: | The popular systemic risk measure CoVaR (conditional Value-at-Risk) is widely used in economics and finance. Formally, it is defined as an (extreme) quantile of one variable (e.g., losses in the financial system) conditional on some other variable (e.g., losses in a bank's shares) being in distress and, hence, measures the spillover of risks. In this article, we propose a dynamic "Co-Quantile Regression", which jointly models VaR and CoVaR semiparametrically. We propose a two-step M-estimator drawing on recently proposed bivariate scoring functions for the pair (VaR, CoVaR). Among others, this allows for the estimation of joint dynamic forecasting models for (VaR, CoVaR). We prove the asymptotic normality of the proposed estimator and simulations illustrate its good finite-sample properties. We apply our co-quantile regression to correct the statistical inference in the existing literature on CoVaR, and to generate CoVaR forecasts for real financial data, which are shown to be superior to existing methods. |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2206.14275&r= |
| By: | Yichen Feng; Ming Min; Jean-Pierre Fouque |
| Abstract: | The aim of this paper is to study a new methodological framework for systemic risk measures by applying deep learning method as a tool to compute the optimal strategy of capital allocations. Under this new framework, systemic risk measures can be interpreted as the minimal amount of cash that secures the aggregated system by allocating capital to the single institutions before aggregating the individual risks. This problem has no explicit solution except in very limited situations. Deep learning is increasingly receiving attention in financial modelings and risk management and we propose our deep learning based algorithms to solve both the primal and dual problems of the risk measures, and thus to learn the fair risk allocations. In particular, our method for the dual problem involves the training philosophy inspired by the well-known Generative Adversarial Networks (GAN) approach and a newly designed direct estimation of Radon-Nikodym derivative. We close the paper with substantial numerical studies of the subject and provide interpretations of the risk allocations associated to the systemic risk measures. In the particular case of exponential preferences, numerical experiments demonstrate excellent performance of the proposed algorithm, when compared with the optimal explicit solution as a benchmark. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2207.00739&r= |
| By: | Anthony Coache; Sebastian Jaimungal; \'Alvaro Cartea |
| Abstract: | We propose a novel framework to solve risk-sensitive reinforcement learning (RL) problems where the agent optimises time-consistent dynamic spectral risk measures. Based on the notion of conditional elicitability, our methodology constructs (strictly consistent) scoring functions that are used as penalizers in the estimation procedure. Our contribution is threefold: we (i) devise an efficient approach to estimate a class of dynamic spectral risk measures with deep neural networks, (ii) prove that these dynamic spectral risk measures may be approximated to any arbitrary accuracy using deep neural networks, and (iii) develop a risk-sensitive actor-critic algorithm that uses full episodes and does not require any additional nested transitions. We compare our conceptually improved reinforcement learning algorithm with the nested simulation approach and illustrate its performance in two settings: statistical arbitrage and portfolio allocation on both simulated and real data. |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2206.14666&r= |
| By: | Weronika Ormaniec; Marcin Pitera; Sajad Safarveisi; Thorsten Schmidt |
| Abstract: | Estimating value-at-risk on time series data with possibly heteroscedastic dynamics is a highly challenging task. Typically, we face a small data problem in combination with a high degree of non-linearity, causing difficulties for both classical and machine-learning estimation algorithms. In this paper, we propose a novel value-at-risk estimator using a long short-term memory (LSTM) neural network and compare its performance to benchmark GARCH estimators. Our results indicate that even for a relatively short time series, the LSTM could be used to refine or monitor risk estimation processes and correctly identify the underlying risk dynamics in a non-parametric fashion. We evaluate the estimator on both simulated and market data with a focus on heteroscedasticity, finding that LSTM exhibits a similar performance to GARCH estimators on simulated data, whereas on real market data it is more sensitive towards increasing or decreasing volatility and outperforms all existing estimators of value-at-risk in terms of exception rate and mean quantile score. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2207.10539&r= |
| By: | Chase Englund |
| Abstract: | This paper uses public data disclosed in eight bank holding companies' "living wills", or Resolution Plans, to examine and test how operational resilience can contribute to financial system stability. The banks, each subject to the Large Institution Supervision Coordinating Committee (LISCC) supervisory program, interact in a complex network of Financial Market Utilities (FMUs). By employing complementary public data on operational exposures and benchmarks for operational disruption developed in existing research, we construct plausible estimates of how various disruption events would impact the financial system. This paper provides a tangible, reproducible example of how concepts discussed in recent regulatory agency guidance on operational resilience can be employed for risk analysis and scenario testing. It also demonstrates how network mapping can aid in this type of analysis. The estimates generated here indicate that disruptions stemming from tail-end operational risk events extend beyond absolute financial losses, and are likely to be large enough to pose a systemic risk to the financial system. |
| Date: | 2022–07–01 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfn:2022-07-01-2&r= |
| By: | Sebastian Jaimungal; Silvana M. Pesenti; Leandro S\'anchez-Betancourt |
| Abstract: | Given an n-dimensional stochastic process X driven by P-Brownian motions and Poisson random measures, we seek the probability measure Q, with minimal relative entropy to P, such that the Q-expectations of some terminal and running costs are constrained. We prove existence and uniqueness of the optimal probability measure, derive the explicit form of the measure change, and characterise the optimal drift and compensator adjustments under the optimal measure. We provide an analytical solution for Value-at-Risk (quantile) constraints, discuss how to perturb a Brownian motion to have arbitrary variance, and show that pinned measures arise as a limiting case of optimal measures. The results are illustrated in a risk management setting -- including an algorithm to simulate under the optimal measure -- where an agent seeks to answer the question: what dynamics are induced by a perturbation of the Value-at-Risk and the average time spent below a barrier on the reference process? |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2206.14844&r= |
| By: | J. du Pisanie; J. S. Allison; I. J. H. Visagie |
| Abstract: | Credit risk scorecards are logistic regression models, fitted to large and complex data sets, employed by the financial industry to model the probability of default of a potential customer. In order to ensure that a scorecard remains a representative model of the population one tests the hypothesis of population stability; specifying that the distribution of clients' attributes remains constant over time. Simulating realistic data sets for this purpose is nontrivial as these data sets are multivariate and contain intricate dependencies. The simulation of these data sets are of practical interest for both practitioners and for researchers; practitioners may wish to consider the effect that a specified change in the properties of the data has on the scorecard and its usefulness from a business perspective, while researchers may wish to test a newly developed technique in credit scoring. We propose a simulation technique based on the specification of bad ratios, this is explained below. Practitioners can generally not be expected to provide realistic parameter values for a scorecard; these models are simply too complex and contain too many parameters to make such a specification viable. However, practitioners can often confidently specify the bad ratio associated with two different levels of a specific attribute. That is, practitioners are often comfortable with making statements such as "on average a new customer is 1.5 times as likely to default as an existing customer with similar attributes". We propose a method which can be used to obtain parameter values for a scorecard based on specified bad ratios. The proposed technique is demonstrated using a realistic example and we show that the simulated data sets adhere closely to the specified bad ratios. The paper provides a link to a github project in which the R code used in order to generate the results shown can be found. |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2206.11344&r= |
| By: | Elisa Al\`os; Frido Rolloos; Kenichiro Shiraya |
| Abstract: | This paper shows the relationship between the forward start volatility swap price and the forward start zero vanna implied volatility of forward start options in rough volatility models. It is shown that in the short time-to-maturity limit the approximation error in the leading term of the correlated case with $H\in(0,\frac12)$ does not depend on the time to forward start date, but only on the difference between the maturity date and forward start date and on the Hurst parameter $H$. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2207.10370&r= |
| By: | Barham, Jim |
| Keywords: | Risk and Uncertainty, Health Economics and Policy |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:ags:usao21:321010&r= |
| By: | Ryan, Ellen |
| Abstract: | The investment fund sector has expanded dramatically since the crisis of 2008-2009. As the sector grows, so do the implications of its risk-taking for the wider financial system and real economy. This paper provides empirical evidence for the existence of wide- spread risk-taking incentives in the investment fund sector, with a particular focus on incentives for synchronised, cyclical risk-taking which could have systemic effects. Incentives arise from the positive response of investors to returns achieved through cyclical risk-taking and non-linearities in the relationship between fund returns and fund flows, which may keep managers from fully internalising the effects of adverse outcomes on their portfolios. The fact that market discipline may not be sufficient to ensure prudential behaviour among managers, combined with the externalities of this risk-taking for the wider system, creates a clear case for macroprudential regulatory intervention. JEL Classification: G23, G11, G28 |
| Keywords: | Financial stability, incentive, investment funds, risk-taking |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:srk:srkwps:2022134&r= |
| By: | Jędrzej Białkowski (University of Canterbury); Martin T. Bohl; Devmali Perera |
| Abstract: | The derivative accounting standard requires hedging to satisfy the 80-125 rule to be eligible to apply the hedge accounting treatment. This means the hedging relationship should achieve hedging effectiveness within the 80% - 125% level to qualify for hedge accounting. The appropriateness of this screening criterion is questioned in the existing literature, and there is hardly any empirical evidence to justify the suitability of this threshold level of hedge effectiveness. By applying meta-analysis methodology for 1699 hedge ratios collected from previous academic studies in commodity futures hedging, we show that the average optimal hedge ratio in commodity futures hedging in the academic literature mostly overlaps with the 80-125 threshold. |
| Keywords: | Commodity markets, Derivative accounting, Hedging effectiveness, Metaanalysis, Optimal hedge ratio, 80-125 rule, Publication bias |
| JEL: | C01 M41 Q02 |
| Date: | 2022–07–01 |
| URL: | http://d.repec.org/n?u=RePEc:cbt:econwp:22/12&r= |
| By: | Michaël Allouche (CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique); Emmanuel Gobet (CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique); Clara Lage (CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique); Edwin Mangin (BNPP) |
| Abstract: | Rating Migration Matrix is a crux to assess credit risks. Modeling and predicting these matrices are then an issue of great importance for risk managers in any financial institution. As a challenger to usual parametric modeling approaches, we propose a new structured dictionary learning model with auto-regressive regularization that is able to meet key expectations and constraints: small amount of data, fast evolution in time of these matrices, economic interpretability of the calibrated model. To show the model applicability, we present a numerical test with real data. The source code and the data are available at https://github.com/michael-allouche/ dictionary-learning-RMM.git for the sake of reproducibility of our research. |
| Keywords: | Rating Migration Matrix,Dictionary learning,auto-regressive modeling,interpretability |
| Date: | 2022–07–07 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03715954&r= |
| By: | Salmerón Garrido, José Antonio; Nunno, Giulia Di; D'Auria, Bernardo |
| Abstract: | Default risk calculus emerges naturally in a portfolio optimization problem whenthe risky asset is threatened with a bankruptcy. The usual stochastic control techniques do not hold in this case and some additional assumptions are generally added to achieve the optimization in a before-and-after default context. We show how it is possible to avoid one of theses restrictive assumptions, the so-called Jacod density hypothesis, by using the framework of the forward integration. In particular, in the logarithmic utility case, in order to get the optimal portfolio the right condition it is proved to be the intensity hypothesis. We use the anticipating calculus to analyze the existence of the optimal portfolio for the logarithmic utility, and than under the assumption of existence of the optimal portfolio we prove the semimartingale decomposition of the risky asset in the filtration enlarged with the default process. |
| Keywords: | Optimal Portfolio; Default Risk; Progressive Enlargement; Forward Integrals; Malliavin Calculus |
| Date: | 2022–07–06 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:35411&r= |
| By: | Xiqian Cai (Institute of Economics, School of Economics and Wang Yanan Institute for Studies in Economics, Xiamen University); Lata Gangadharan (Department of Economics, Monash Business School, Monash University); Yi Lu (School of Economics and Management, Tsinghua University); Xiaojian Zhao (Department of Economics, Monash Business School, Monash University) |
| Abstract: | In the modern economy, entrepreneurship is associated with individuals' tendency to invest in risky projects. We conjecture that societies with a historical background in sea fishing are more likely than other societies to exhibit risk taking behaviors in modern times, as the earliest sea fishers needed to be sufficiently risk seeking to venture into the unpredictable ocean. We examine the effect of a sea fishing legacy on risk attitudes in modern societies and find that ancestors' dependence on sea fishing increases risk-taking preferences and eco- nomically related characteristics. This approach provides a novel explanation for the origin of individuals'preference for risk. |
| Keywords: | Risk preferences; Sea-fishing legacy; Cross-country differences |
| JEL: | Z10 |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:mos:moswps:2022-17&r= |
| By: | Maddalena Galardo (Bank of Italy); Valerio Vacca (Bank of Italy) |
| Abstract: | We use a rich dataset on bank loans to Italian firms matched to information on firms’ and banks’ characteristics, and exploit the implementation of Basel III reforms in Italy to investigate the impact of higher risk-based capital requirements on credit supply. While we do not address the steady state impact of capital requirements, we find that the introduction of higher requirements is associated with credit tightening in the early years after the reform. Banks affected to a larger extent by the new requirements tighten credit supply towards risky firms in favour of sounder ones. We also show that banks with particularly strong or particularly weak pre-reform capital positions tighten the credit to a lesser extent, i.e., the lending supply response is U-shaped with respect to initial capital, as predicted by the forced safety effect (Bahaj and Malherbe 2020). Finally, firms borrowing more from less capitalized banks were only partially able to switch their lenders, experienced a worsening in lending conditions and invested less compared to other firms after Basel III implementation. |
| Keywords: | financial institutions, Basel III, capital requirements, forced safety effect, lending conditions |
| JEL: | G21 G28 G38 |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:bdi:wptemi:td_1372_22&r= |
| By: | Konietschke, Paul; Ongena, Steven; Ponte Marques, Aurea |
| Abstract: | How do banks respond to changes in capital requirements as a result of the stress tests? Does the disclosure of stress test results matter? To answer these questions, we study the impact of European stress tests on banks’ lending, their corresponding risk-taking, the ensuing effect on their profitability and the respective publication effect. Exploiting the centralised European stress tests in conjunction with two unique confidential databases containing (i) stress test information for the 2016 and 2018 exercises covering a total of 93 and 87 banks, respectively; and (ii) quarterly supervisory information on approximately 1000 banks (stress-tested and non-tested), allow us to implement a dynamic differencein-differences strategy for a comparable sample of banks. We find that banks participating in the stress tests reallocate credit away from riskier borrowers and towards safer ones in the household sector, making them in general safer but also less profitable. This is especially the case for the set of banks part of the Supervisory Review and Evaluation Process with undisclosed stress tests, which were also not disclosing their Pillar 2 Requirements voluntarily. Our results confirm that the publication of capital requirements can have a disciplinary effect since banks publishing their requirements tend to have more robust capital ratios, which improves market discipline and financial stability. JEL Classification: E51, E58, G21, G28 |
| Keywords: | Credit supply, Financial stability, Market discipline, Profitability, Stress-testing |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20222679&r= |
| By: | Jan Sun |
| Abstract: | The consumer finance literature has emphasized the importance of income and ex pense risk for the evaluation of bankruptcy regimes. Single and married households differ in the risks they face. In this paper, I build the first quantitative consumer default model that explicitly models singles and couples. I calibrate my model to the United States in 2019 and estimate (medical) expense shocks separately for single and married individuals. My calibrated model generates large differences in bankruptcy rates across marital status as in the data. I examine how the preferred degree of bankruptcy leniency differs between singles and couples. There are several channels at work: Differences on the income side between singles and couples cause couples to prefer a stricter bankruptcy regime due to the intra-household insurance channel. However, increased risk for couples due to divorce and on the expense side outweigh the first channel. The net effect is that couples prefer more lenient bankruptcy than singles. My findings suggest that marital status is important to take into account for the evaluation of bankruptcy regimes. |
| Keywords: | Consumer Credit, Bankruptcy, Default, Bankruptcy Regulation, Marital Status |
| JEL: | D13 D14 D15 E21 E49 G18 G51 J12 K35 |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:bon:boncrc:crctr224_2022_361&r= |
| By: | Jackie Grant; Mark Hindmarsh; Sergey E. Koposov |
| Abstract: | We present the first global analysis of the impact of the April 2022 cuts to the future pensions of members of the Universities Superannuation Scheme. For the 196,000 active members, if Consumer Price Inflation (CPI) remains at its historic average of 2.5%, the distribution of the range of cuts peaks between 30%-35%. This peak increases to 40%-45% cuts if CPI averages 3.0%. The global loss across current USS scheme members, in today's money, is calculated to be 16-18 billion GBP, with most of the 71,000 staff under the age of 40 losing between 100k-200k GBP each, for CPI averaging 2.5%-3.0%. A repeated claim made during the formal consultation by the body representing university management (Universities UK) that those earning under 40k GBP would receive a "headline" cut of 12% to their future pension is shown to be a serious underestimate for realistic CPI projections. |
| Date: | 2022–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2206.06201&r= |