nep-rmg New Economics Papers
on Risk Management
Issue of 2023‒01‒30
thirteen papers chosen by
Stan Miles
Thompson Rivers University

  1. Reconstructing Volatility: Pricing of Index Options under Rough Volatility By Peter K. Friz; Thomas Wagenhofer
  2. Measuring adequately the benefit of diversification in the extreme quantiles: An inquiry into covariation on the brink of catastrophe By Pierre-Charles Pradier; Guillaume Rideau; Sakina Rrguiti
  3. Propagation of cyber incidents in an insurance portfolio: counting processes combined with compartmental epidemiological models By Caroline Hillairet; Olivier Lopez
  4. Mandatory Retention Rules and Bank Risk By Yuteng Cheng
  5. Intergenerational Sharing ofUnhedgeable Inflation Risk By Damiaan H.J. Chen; Roel M.W.J. Beetsma; Sweder J.G. van Wijnbergen
  6. Measuring tail risk at high-frequency: An $L_1$-regularized extreme value regression approach with unit-root predictors By Julien Hambuckers; Li Sun; Luca Trapin
  7. Cyber contagion: impact of the network structure on the losses of an insurance portfolio By Caroline Hillairet; Olivier Lopez; Louise d'Oultremont; Brieuc Spoorenberg
  8. Risk sharing with deep neural networks By Matteo Burzoni; Alessandro Doldi; Enea Monzio Compagnoni
  9. Altruism and Risk Sharing in Networks By Yann Bramoullé; Renaud Bourlès; Eduardo Perez-Richet
  10. A bankruptcy probability model for assessing credit risk on corporate loans with automated variable selection By Ida Nervik Hjelseth; Arvid Raknerud; Bjørn H. Vatne
  11. Inequality and Risk Preference By Harry Pickard; Thomas Dohmen; Bert van Landeghem
  12. The quintic Ornstein-Uhlenbeck volatility model that jointly calibrates SPX & VIX smiles By Eduardo Abi Jaber; Camille Illand; Shaun; Li
  13. Optimal Liquidation with High Risk Aversion in the Almgren--Chriss Model: A Case Study By Leonid Dolinskyi; Yan Dolinsky

  1. By: Peter K. Friz; Thomas Wagenhofer
    Abstract: In previous works Avellaneda et al. pioneered the pricing and hedging of index options - products highly sensitive to implied volatility and correlation assumptions - with large deviations methods, assuming local volatility dynamics for all components of the index. We here present an extension applicable to non-Markovian dynamics and in particular the case of rough volatility dynamics.
    Date: 2022–12
  2. By: Pierre-Charles Pradier; Guillaume Rideau (BPCE - BPCE); Sakina Rrguiti (UP1 - Université Paris 1 Panthéon-Sorbonne, CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, BPCE - BPCE)
    Abstract: The aim of this work is to better understand the nature of covariation in the vicinity of extremes on financial data and assess whether the usual assumptions and covariation measures fits the actual data. For simplicity, we consider pairs of random variables. In order to identify the shape of the covariation all along the distribution, and particularly as the extreme quantiles are approached, we describe the contribution of each of the variables from a random couple to the quantiles of the weighted sum of these variables. This approach makes sense since it can be interpreted in terms of Value-at-Risk in a financial institution: the VaR of the sum of variables may represent the capital requiremet for a diversified conglomerate, while the sum of VaR of the variables would correspond to the capital requirements for the components of the conglomerate, without taking diversification into account. The ratio of these two quantities appears as a good measure of both the benefit of diversification and the decorrelation of variables. We thus compare the values of quantiles and ratio taken from a representative dataset to the values obtained from various simulations relying on the usual assumptions. The result of this comparison is that the usual assumptions do not correctly model the covariation of the real-word data. In particular, the usual assumptions tend to exaggerate the correlation in the vicinity of extreme loss while the benefit of diversification is uniform across distribution. Additional simulations and modelling assumptions may be required to assess the generality of this result.
    Keywords: Financial conglomerates, Diversification, Value-at-Risk, Capital requirement
    Date: 2022–11
  3. By: Caroline Hillairet (CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique); Olivier Lopez (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité)
    Abstract: In this paper, we propose a general framework to design accumulation scenarios that can be used to anticipate the impact of a massive cyber attack on an insurance portfolio. The aim is also to emphasize the role of countermeasures in stopping the spread of the attack over the portfolio, and to quantify the benefits of implementing such strategies of response. Our approach consists of separating the global dynamic of the cyber event (that can be described through compartmental epidemiological models), the effect on the portfolio, and the response strategy. This general framework allows us to obtain Gaussian approximations for the corresponding processes, and sharp confidence bounds for the losses. A detailed simulation study, which mimics the effects of a Wannacry scenario, illustrates the practical implementation of the method.
    Keywords: Cyber insurance, emerging risks, counting processes, compartmental epi- demiological models, risk theory, compartmental epidemiological models
    Date: 2021–01–31
  4. By: Yuteng Cheng
    Abstract: This paper studies, theoretically and empirically, the unintended consequences of mandatory retention rules in securitization. The Dodd-Frank Act and the EU Securitisation Regulation both impose a 5% mandatory retention requirement to motivate screening and monitoring. I first propose a novel model showing that while retention strengthens monitoring, it may also encourage banks to shift risk. I then provide empirical evidence supporting this unintended consequence: in the US data, banks shifted toward riskier portfolios after the implementation of the retention rules embedded in Dodd-Frank. Furthermore, the model offers clear, testable predictions about policy and corresponding consequences. In the US data, stricter retention rules caused banks to monitor and shift risk simultaneously. According to the model prediction, such a simultaneous increase occurs only when the retention level is above optimal, which suggests that the current rate of 5% in the US is too high.
    Keywords: Financial institutions; Financial system regulation and policies; Credit risk management
    JEL: G21 G28
    Date: 2023–01
  5. By: Damiaan H.J. Chen (University of Amsterdam); Roel M.W.J. Beetsma (University of Amsterdam); Sweder J.G. van Wijnbergen (University of Amsterdam)
    Abstract: We explore how members of a collective pension scheme can share inflation risks in the absence of suitable financial market instruments. Using intergenerational risk sharing arrangements, risks can be allocated better across the various participants of a collective pension scheme than would be the case in a strictly individual- or cohort-based pension scheme, as these can only lay off risks via existing financial market instruments. Hence, intergenerational sharing of these risks enhances welfare. In view of the sizes of their funded pension sectors, this would be particularly beneficial for the Netherlands and the U.K
    Keywords: pension funds, intergenerational risk sharing, unhedgeable inflation risk, incomplete markets, welfare loss
    JEL: C61 E21 G11 G23
    Date: 2022–12–15
  6. By: Julien Hambuckers; Li Sun; Luca Trapin
    Abstract: We study tail risk dynamics in high-frequency financial markets and their connection with trading activity and market uncertainty. We introduce a dynamic extreme value regression model accommodating both stationary and local unit-root predictors to appropriately capture the time-varying behaviour of the distribution of high-frequency extreme losses. To characterize trading activity and market uncertainty, we consider several volatility and liquidity predictors, and propose a two-step adaptive $L_1$-regularized maximum likelihood estimator to select the most appropriate ones. We establish the oracle property of the proposed estimator for selecting both stationary and local unit-root predictors, and show its good finite sample properties in an extensive simulation study. Studying the high-frequency extreme losses of nine large liquid U.S. stocks using 42 liquidity and volatility predictors, we find the severity of extreme losses to be well predicted by low levels of price impact in period of high volatility of liquidity and volatility.
    Date: 2023–01
  7. By: Caroline Hillairet (CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - X - École polytechnique - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - CNRS - Centre National de la Recherche Scientifique); Olivier Lopez (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité); Louise d'Oultremont; Brieuc Spoorenberg
    Abstract: In this paper, we provide a model that aims to describe the impact of a massive cyber attack on an insurance portfolio, taking into account the structure of the network. Due to the contagion, such an event can rapidly generate consequent damages, and mutualization of the losses may not hold anymore. The composition of the portfolio should therefore be diversified enough to prevent or reduce the impact of such events, with the difficulty that the relationships between actor is difficult to assess. Our approach consists in introducing a multi-group epidemiological model which, apart from its ability to describe the intensity of connections between actors, can be calibrated from a relatively small amount of data, and through fast numerical procedures. We show how this model can be used to generate reasonable scenarios of cyber events, and investigate the response to different types of attacks or behavior of the actors, allowing to quantify the benefit of an efficient prevention policy.
    Keywords: Cyber insurance, cyber risk, compartmental models, multi-SIR, network structures
    Date: 2022–11–01
  8. By: Matteo Burzoni; Alessandro Doldi; Enea Monzio Compagnoni
    Abstract: We consider the problem of optimally sharing a financial position among agents with potentially different reference risk measures. The problem is equivalent to computing the infimal convolution of the risk metrics and finding the so-called optimal allocations. We propose a neural network-based framework to solve the problem and we prove the convergence of the approximated inf-convolution, as well as the approximated optimal allocations, to the corresponding theoretical values. We support our findings with several numerical experiments.
    Date: 2022–12
  9. By: Yann Bramoullé (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique); Renaud Bourlès (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique, IUF - Institut Universitaire de France - M.E.N.E.S.R. - Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche); Eduardo Perez-Richet (Sciences Po - Sciences Po, CEPR - Center for Economic Policy Research - CEPR)
    Abstract: We provide the first analysis of the risk-sharing implications of altruism networks. Agents are embedded in a fixed network and care about each other. We explore whether altruistic transfers help smooth consumption and how this depends on the shape of the network. We find that altruism networks have a first-order impact on risk. Altruistic transfers generate efficient insurance when the network of perfect altruistic ties is strongly connected. We uncover two specific empirical implications of altruism networks. First, bridges can generate good overall risk sharing, and, more generally, the quality of informal insurance depends on the average path length of the network. Second, large shocks are well-insured by connected altruism networks. By contrast, large shocks tend to be badly insured in models of informal insurance with frictions. We characterize what happens for shocks that leave the structure of giving relationships unchanged. We further explore the relationship between consumption variance and centrality, correlation in consumption streams across agents, and the impact of adding links.
    Keywords: Altruism, Networks, Risk Sharing, Informal Insurance
    Date: 2021–06
  10. By: Ida Nervik Hjelseth; Arvid Raknerud; Bjørn H. Vatne
    Abstract: We propose an econometric model for predicting the share of bank debt held by bankrupt firms by combining a novel set of firm-level financial variables and macroeconomic indicators. Our firm-level data include payment remarks in the form of debt collections from private agencies and attachments from private and public agencies and cover all Norwegian limited liability companies for the period 2010–2021. We use logistic Lasso regressions to select bankruptcy predictors from a large set of potential predictors, comparing a highly sparse variable selection criterion (“the one standard error rule†) with the minimum cross validation error (CVE) criterion. Moreover, we examine the implications of using debt shares as weights in the estimation and find that weighting has a large impact on variable selection and predictions and, generally, leads to lower out-of-sample prediction errors than alternative approaches. Debt weighting combined with sparse variable selection gives the best predictions of the risk of bankruptcy in firms holding high shares of the bank debt.
    Keywords: Bankruptcy prediction, credit risk, corporate bank debt, Lasso, weighted logistic regression
    JEL: C25 C33 C53 G33 D22
    Date: 2022–06–20
  11. By: Harry Pickard (Newcastle University Business School, Newcastle University, United Kingdom); Thomas Dohmen (Economics Department, University of Bonn, Germany); Bert van Landeghem (Department of Economics, University of Sheffield, United Kingdom)
    Abstract: This paper studies the relationship between income inequality and risk taking. Increased income inequality is likely to enlarge the scope for upward comparisons and, in the presence of reference-dependent preferences, to increase willingness to take risks. Using a globally representative dataset on risk preference in 76 countries, we empirically document that the distribution of income in a country has a positive and significant link with the preference for risk. This relationship is remarkably precise and holds across countries and individuals, as well as alternate measures of inequality. We find evidence that individuals who are more able to understand inequality and individuals who fall behind their inherent point of reference increase their preference for risk. Two complementary instrumental variable approaches support a causal interpretation of our results.
    Keywords: Income inequality; risk preference; risk sensitivity
    JEL: D91 O15 D81 D01
    Date: 2023–01
  12. By: Eduardo Abi Jaber (Xiaoyuan); Camille Illand (Xiaoyuan); Shaun (Xiaoyuan); Li
    Abstract: The quintic Ornstein-Uhlenbeck volatility model is a stochastic volatility model where the volatility process is a polynomial function of degree five of a single Ornstein-Uhlenbeck process with fast mean reversion and large vol-of-vol. The model is able to achieve remarkable joint fits of the SPX-VIX smiles with only 6 effective parameters and an input curve that allows to match certain term structures. Even better, the model remains very simple and tractable for pricing and calibration: the VIX squared is again polynomial in the Ornstein-Uhlenbeck process, leading to efficient VIX derivative pricing by a simple integration against a Gaussian density; simulation of the volatility process is exact; and pricing SPX products can be done efficiently and accurately by standard Monte Carlo techniques with suitable antithetic and control variates.
    Date: 2022–12
  13. By: Leonid Dolinskyi; Yan Dolinsky
    Abstract: We consider the Bachelier model with linear price impact. Exponential utility indifference prices are studied for vanilla European options in the case where the investor is required to liquidate her position at the maturity date. Our main result is establishing a non-trivial scaling limit for a vanishing price impact which is inversely proportional to the risk aversion. We compute the limit of the corresponding utility indifference prices and find explicitly a family of portfolios which are asymptotically optimal.
    Date: 2023–01

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