
on Risk Management 
Issue of 2021‒08‒16
43 papers chosen by 
By:  Cosimo Munari (University of Zurich  Department of Banking and Finance; Swiss Finance Institute); Stefan Weber (Leibniz Universität Hannover  House of Insurance); Lutz Wilhelmy (Swiss Re) 
Abstract:  Protection of creditors is a key objective of financial regulation. Where the protection needs are high, i.e., in banking and insurance, regulatory solvency requirements are an instrument to prevent that creditors incur losses on their claims. The current regulatory requirements based on Value at Risk and Average Value at Risk limit the probability of default of financial institutions, but fail to control the size of recovery on creditors' claims in the case of default. We resolve this failure by developing a novel risk measure, Recovery Value at Risk. Our conceptual approach can flexibly be extended and allows the construction of general recovery risk measures for various risk management purposes. By design, these risk measures control recovery on creditors' claims and integrate the protection needs of creditors into the incentive structure of the management. We provide detailed case studies and applications: We analyze how recovery risk measures react to the joint distributions of assets and liabilities on firms' balance sheets and compare the corresponding capital requirements with the current regulatory benchmarks based on Value at Risk and Average Value at Risk. We discuss how to calibrate recovery risk measures to historic regulatory standards. Finally, we show that recovery risk measures can be applied to performancebased management of business divisions of firms and that they allow for a tractable characterization of optimal tradeoffs between risk and return in the context of investment management. 
Date:  2021–04 
URL:  http://d.repec.org/n?u=RePEc:chf:rpseri:rp2158&r= 
By:  Xia Han; Bin Wang; Ruodu Wang; Qinyu Wu 
Abstract:  Expected Shortfall (ES, also known as CVaR) is the most important coherent risk measure in finance, insurance, risk management, and engineering. Recently, Wang and Zitikis (2021) put forward four economic axioms for portfolio risk assessment and provide the first economic axiomatic foundation for the family of ES. In particular, the axiom of no reward for concen tration (NRC) is arguably quite strong, which imposes an additive form of the risk measure on portfolios with a certain dependence structure. We relax the axiom of NRC by introducing the notion of concentration aversion, which does not impose any specific form of the risk measure. It turns out that risk measures with concentration aversion are functions of ES and the expec tation. Together with the other three standard axioms of monotonicity, translation invariance and lower semicontinuity, concentration aversion uniquely characterizes the family of ES. This result enhances the axiomatic theory for ES as no particular additive form needs to be assumed exante. Furthermore, our results provide an axiomatic foundation for the problem of meanES portfolio selection and lead to new explicit formulas for convex and consistent risk measures. 
Date:  2021–08 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2108.05066&r= 
By:  Giovanni Bonaccolto (emlyon business school); Massimiliano Caporin; Bertrand Maillet 
Abstract:  In this article, we first generalize the Conditional AutoRegressive Expected Shortfall (CARES) model by introducing the loss exceedances of all (other) listed companies in the Expected Shortfall related to each firm, thus proposing the CARESX model (where the ‘X', as usual, stands for eXtended in the case of largedimensional problems). Second, we construct a regularized network of US financial companies by introducing the Least Absolute Shrinkage and Selection Operator in the estimation step. Third, we also propose a calibration approach for uncovering the relevant edges between the network nodes, finding that the estimated network structure dynamically evolves through different market risk regimes. We ultimately show that knowledge of the extreme risk network links provides useful information, since the intensity of these links has strong implications on portfolio risk. Indeed, it allows us to design effective risk management mitigation allocation strategies. 
Keywords:  finance,Financial networks,Portfolio analysis,Systemic risk,Expectile regression 
Date:  2021–06–24 
URL:  http://d.repec.org/n?u=RePEc:hal:journl:hal03287947&r= 
By:  Alexandre Carbonneau; Fr\'ed\'eric Godin 
Abstract:  The use of nontranslation invariant risk measures within the equal risk pricing (ERP) methodology for the valuation of financial derivatives is investigated. The ability to move beyond the class of convex risk measures considered in several prior studies provides more flexibility within the pricing scheme. In particular, suitable choices for the risk measure embedded in the ERP framework such as the semimeansquareerror (SMSE) are shown herein to alleviate the price inflation phenomenon observed under Tail ValueatRisk based ERP as documented for instance in Carbonneau and Godin (2021b). The numerical implementation of nontranslation invariant ERP is performed through deep reinforcement learning, where a slight modification is applied to the conventional deep hedging training algorithm (see Buehler et al., 2019) so as to enable obtaining a price through a single training run for the two neural networks associated with the respective long and short hedging strategies. The accuracy of the neural network training procedure is shown in simulation experiments not to be materially impacted by such modification of the training algorithm. 
Date:  2021–07 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2107.11340&r= 
By:  Lloyd, S.; Manuel, E.; Panchev, K. 
Abstract:  We study how foreign financial developments influence the conditional distribution of domestic GDP growth. Within a quantile regression setup, we propose a method to parsimoniously account for foreign vulnerabilities using bilateralexposure weights when assessing downside macroeconomic risks. Using a panel dataset of advanced economies, we show that tighter foreign financial conditions and faster foreign credittoGDP growth are associated with a more severe left tail of domestic GDP growth, even when controlling for domestic indicators. The inclusion of foreign indicators significantly improves estimates of â€˜GDPatRiskâ€™, a summary measure of downside risks. In turn, this yields timevarying estimates of higher GDP growth moments that are interpretable and provide advanced warnings of crisis episodes. Decomposing historical estimates of GDPatRisk into domestic and foreign sources, we show that foreign shocks are a key driver of domestic macroeconomic tail risks. 
Keywords:  Financial stability, GDPatRisk, International spillovers, Local projections, Quantile regression, Tail risk 
JEL:  E44 E58 F30 F41 F44 G01 
Date:  2021–07–30 
URL:  http://d.repec.org/n?u=RePEc:cam:camdae:2156&r= 
By:  Nizar, Muhammad Afdi; Mansur, Alfan 
Abstract:  A riskbased premium scheme could be a reliable system to determine a fairer deposit insurance premium. This research aimed to assess Indonesian banks’ risk profile, including per size classification and ownership as well as to counterfactually simulate a riskbased deposit insurance system for the individual banks. This research combined analysis of variance (ANOVA) and nonparametric approach applied to 75 banks (2008q12019q3). The results showed that big banks did not necessarily posture better risk management compared to small banks. Also, under the riskbased scheme, banks with better risk management could be rewarded, while less prudent banks could be punished. 
Keywords:  Deposit; premium; flatrate; riskbased; banks; insurance 
JEL:  C12 C54 G21 G28 G3 G30 
Date:  2021–05 
URL:  http://d.repec.org/n?u=RePEc:pra:mprapa:109083&r= 
By:  Stéphane Verani; Pei Cheng Yu 
Abstract:  We show that the supply of life annuities in the U.S. is constrained by interest rate risk. We identify this effect using annuity prices offered by U.S. life insurers from 1989 to 2019 and exogenous variations in contractlevel regulatory capital requirements. The cost of interest rate risk management accounts for at least half of the average life annuity markups or eight percentage points. The contribution of interest rate risk to annuity markups sharply increased after the great financial crisis, suggesting new retirees' opportunities to transfer their longevity risk are unlikely to improve in a persistently low interest rate environment. 
Keywords:  Life insurance; Annuities; Corporate bond market; Retirement; Interest rate risk 
JEL:  G10 G22 G32 
Date:  2021–07–30 
URL:  http://d.repec.org/n?u=RePEc:fip:fedgfe:202144&r= 
By:  Spiridon Penev; Pavel V. Shevchenko; Wei Wu 
Abstract:  We quantify model risk of a financial portfolio whereby a multiperiod meanstandarddeviation criterion is used as a selection criterion. In this work, model risk is defined as the loss due to uncertainty of the underlying distribution of the returns of the assets in the portfolio. The uncertainty is measured by the KullbackLeibler divergence, i.e., the relative entropy. In the worst case scenario, the optimal robust strategy can be obtained in a semianalytical form as a solution of a system of nonlinear equations. Several numerical results are presented which allow us to compare the performance of this robust strategy with the optimal nonrobust strategy. For illustration, we also quantify the model risk associated with an empirical dataset. 
Date:  2021–08 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2108.02633&r= 
By:  Jaydip Sen; Sidra Mehtab 
Abstract:  Designing an optimum portfolio that allocates weights to its constituent stocks in a way that achieves the best tradeoff between the return and the risk is a challenging research problem. The classical meanvariance theory of portfolio proposed by Markowitz is found to perform suboptimally on the realworld stock market data since the error in estimation for the expected returns adversely affects the performance of the portfolio. This paper presents three approaches to portfolio design, viz, the minimum risk portfolio, the optimum risk portfolio, and the Eigen portfolio, for seven important sectors of the Indian stock market. The daily historical prices of the stocks are scraped from Yahoo Finance website from January 1, 2016, to December 31, 2020. Three portfolios are built for each of the seven sectors chosen for this study, and the portfolios are analyzed on the training data based on several metrics such as annualized return and risk, weights assigned to the constituent stocks, the correlation heatmaps, and the principal components of the Eigen portfolios. Finally, the optimum risk portfolios and the Eigen portfolios for all sectors are tested on their return over a period of a sixmonth period. The performances of the portfolios are compared and the portfolio yielding the higher return for each sector is identified. 
Date:  2021–07 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2107.11371&r= 
By:  Pashchenko, Svetlana; Porapakkarm, Ponpoje 
Abstract:  How does the value of life affect annuity demand? To address this question, we construct a portfolio choice problem with three key features: i) agents have access to lifecontingent assets, ii) they always prefer living to dying, iii) agents have nonexpected utility preferences. We show that as utility from being alive increases, annuity demand decreases (increases) if agents are more (less) averse to risk rather than to intertemporal fluctuations. Put differently, if people prefer early resolution of uncertainty, they are less interested in annuities when the value of life is high. Our findings have two important implications. First, we get better understanding of the wellknown annuity puzzle. Second, we argue that the observed low annuity demand provides evidence that people prefer early rather than late resolution of uncertainty. 
Keywords:  annuities, value of a statistical life, portfolio choice problem, lifecontingent assets, longevity insurance 
JEL:  D91 G11 G22 
Date:  2021–04–15 
URL:  http://d.repec.org/n?u=RePEc:pra:mprapa:108886&r= 
By:  Gian Paolo Clemente; Francesco Della Corte; Nino Savelli 
Abstract:  The paper provides a stochastic model useful for assessing the capital requirement for demographic risk. The model extends to the market consistent context classical methodologies developed in a local accounting framework. In particular we provide a unique formulation for different nonparticipating life insurance contracts and we prove analytically that the valuation of demographic profit can be significantly affected by the financial conditions in the market. A case study has been also developed considering a portfolio of life insurance contracts. Results prove the effectiveness of the model in highlighting main drivers of capital requirement evaluation, also compared to local GAAP framework. 
Date:  2021–07 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2107.10891&r= 
By:  JeanMarc Vasnier (CESI  Centre d'Enseignement Supérieur Industriel); Mourad Messaadia (LINEACT  Laboratoire d’Innovation Numérique pour les Entreprises et les Apprentissages au service de la Compétitivité des Territoires  CESI  Centre d'Enseignement Supérieur Industriel); Nicolas Maranzana (LCPI  Laboratoire Conception de Produits et Innovation  Arts et Métiers Sciences et Technologies  HESAM  HESAM Université); Ameziane Aoussat (LCPI  Laboratoire Conception de Produits et Innovation  Arts et Métiers Sciences et Technologies  HESAM  HESAM Université) 
Abstract:  Small and mediumsized enterprises (SMEs) are the spine of the European economy and play a key role in adding value in all sectors of the economy. However, due to a lack of methodology and time, SME entrepreneurs struggle to formalize their strategies and too often remain illprepared to face today's potential crises. This paper aims to propose a Risk Management (RM) tool to identify and assess the impact of risks on specific business strategic dimensions. The hypotheses and robustness of the model are tested using Monte Carlo simulation. The analysis shows that a reduced strategic risk matrix (size [Formula: see text]) could provide the same quality of information as a full strategic risk matrix (size [Formula: see text]) in about 80% of the cases, regardless of the weight of each criterion and the values of each risk factor. The results extend the limited use of RM tool in the field of SME Risk Management. 
Keywords:  SMEs,Risk matrix,Monte Carlo simulation,Strategic risk management,Decision analysis 
Date:  2021 
URL:  http://d.repec.org/n?u=RePEc:hal:journl:hal03295416&r= 
By:  Papp, Gábor; Kondor, Imre; Caccioli, Fabio 
Abstract:  Expected Shortfall (ES), the average loss above a high quantile, is the current financial regulatory market risk measure. Its estimation and optimization are highly unstable against sample fluctuations and become impossible above a critical ratio r = N/T, where N is the number of different assets in the portfolio, and T is the length of the available time series. The critical ratio depends on the confidence level a, which means we have a line of critical points on the αr plane. The large fluctuations in the estimation of ES can be attenuated by the application of regularizers. In this paper, we calculate ES analytically under an `1 regularizer by the method of replicas borrowed from the statistical physics of random systems. The ban on short selling, i.e., a constraint rendering all the portfolio weights nonnegative, is a special case of an asymmetric Ɩ1 regularizer. Results are presented for the outofsample and the insample estimator of the regularized ES, the estimation error, the distribution of the optimal portfolio weights, and the density of the assets eliminated from the portfolio by the regularizer. It is shown that the noshort constraint acts as a high volatility cutoff, in the sense that it sets the weights of the high volatility elements to zero with higher probability than those of the low volatility items. This cutoff renormalizes the aspect ratio r = N/T, thereby extending the range of the feasibility of optimization. We find that there is a nontrivial mapping between the regularized and unregularized problems, corresponding to a renormalization of the order parameters. Expected Shortfall (ES), the average loss above a high quantile, is the current financial regulatory market risk measure. Its estimation and optimization are highly unstable against sample fluctuations and become impossible above a critical ratio r = N/T, where N is the number of different assets in the portfolio, and T is the length of the available time series. The critical ratio depends on the confidence level a, which means we have a line of critical points on the ar plane. The large fluctuations in the estimation of ES can be attenuated by the application of regularizers. In this paper, we calculate ES analytically under an `1 regularizer by the method of replicas borrowed from the statistical physics of random systems. The ban on short selling, i.e., a constraint rendering all the portfolio weights nonnegative, is a special case of an asymmetric Ɩ1 regularizer. Results are presented for the outofsample and the insample estimator of the regularized ES, the estimation error, the distribution of the optimal portfolio weights, and the density of the assets eliminated from the portfolio by the regularizer. It is shown that the noshort constraint acts as a high volatility cutoff, in the sense that it sets the weights of the high volatility elements to zero with higher probability than those of the low volatility items. This cutoff renormalizes the aspect ratio r = N/T, thereby extending the range of the feasibility of optimization. We find that there is a nontrivial mapping between the regularized and unregularized problems, corresponding to a renormalization of the order parameters. 
Keywords:  portfolio optimization; regularization; renormalization 
JEL:  F3 G3 J1 
Date:  2021–05–01 
URL:  http://d.repec.org/n?u=RePEc:ehl:lserod:111051&r= 
By:  Thomas Conlon; John Cotter; Iason Kynigakis 
Abstract:  We examine machine learning and factorbased portfolio optimization. We find that factors based on autoencoder neural networks exhibit a weaker relationship with commonly used characteristicsorted portfolios than popular dimensionality reduction techniques. Machine learning methods also lead to covariance and portfolio weight structures that diverge from simpler estimators. Minimumvariance portfolios using latent factors derived from autoencoders and sparse methods outperform simpler benchmarks in terms of risk minimization. These effects are amplified for investors with an increased sensitivity to riskadjusted returns, during high volatility periods or when accounting for tail risk. 
Date:  2021–07 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2107.13866&r= 
By:  Packham, Natalie; Woebbeking, Fabian 
Abstract:  We develop a general approach for stress testing correlations of financial asset portfolios. The correlation matrix of asset returns is specified in a parametric form, where correlations are represented as a function of risk factors, such as country and industry factors. A sparse factor structure linking assets and risk factors is built using Bayesian variable selection methods. Regular calibration yields a joint distribution of economically meaningful stress scenarios of the factors. As such, the method also lends itself as a reverse stress testing framework: using the Mahalanobis distance or highest density regions (HDR) on the joint risk factor distribution allows to infer worstcase correlation scenarios. We give examples of stress tests on a large portfolio of European and North American stocks. 
Keywords:  Correlation stress testing,reverse stress testing,factor selection,scenario selection,Bayesian variable selection,market risk management 
JEL:  G11 G32 
Date:  2021 
URL:  http://d.repec.org/n?u=RePEc:zbw:irtgdp:2021012&r= 
By:  Matthieu Garcin (ESILV  Ecole Supérieure d'Ingénieurs Léonard de Vinci); Samuel Stéphan (ESILV  Ecole Supérieure d'Ingénieurs Léonard de Vinci, SAMM  Statistique, Analyse et Modélisation Multidisciplinaire (SAmosMarin Mersenne)  UP1  Université Paris 1 PanthéonSorbonne) 
Abstract:  In this article we compare the performances of a logistic regression and a feed forward neural network for credit scoring purposes. Our results show that the logistic regression gives quite good results on the dataset and the neural network can improve a little the performance. We also consider different sets of features in order to assess their importance in terms of prediction accuracy. We found that temporal features (i.e. repeated measures over time) can be an important source of information resulting in an increase in the overall model accuracy. Finally, we introduce a new technique for the calibration of predicted probabilities based on Stein's unbiased risk estimate (SURE). This calibration technique can be applied to very general calibration functions. In particular, we detail this method for the sigmoid function as well as for the Kumaraswamy function, which includes the identity as a particular case. We show that stacking the SURE calibration technique with the classical Platt method can improve the calibration of predicted probabilities. 
Keywords:  Deep learning,credit scoring,calibration,SURE 
Date:  2021–07–15 
URL:  http://d.repec.org/n?u=RePEc:hal:wpaper:hal03286760&r= 
By:  Lykourgos Alexiou (Athens University of Economics and Business); Amit Goyal (University of Lausanne; Swiss Finance Institute); Alexandros Kostakis (University of Liverpool Management School; University of Manchester  Manchester Business School); Leonidas Rompolis (Athens University of Economics and Business  Department of Accounting and Finance) 
Abstract:  We document that implied volatility (IV) curves extracted from shortterm equity options frequently become concave prior to the earnings announcement day (EAD) reflecting a bimodal riskneutral distribution for the underlying stock price. Firms with concave IV curves exhibit significantly higher absolute stock returns on EAD and higher realized volatility after the announcement, as compared to firms with nonconcave IV curves. Hence, concavity in the IV curve constitutes an exante optionbased signal for event risk in the underlying stock. Returns on deltaneutral straddles around EADs are significantly lower in the presence of concave IV curves, showing that investors pay a high premium to hedge against this event risk. 
Keywords:  Earnings Announcement, Event Risk, RiskNeutral Distribution, Implied Volatility Curve 
Date:  2021–05 
URL:  http://d.repec.org/n?u=RePEc:chf:rpseri:rp2148&r= 
By:  Gianni De Nicolo (Johns Hopkins University  Carey Business School; CESifo (Center for Economic Studies and Ifo Institute)); Nataliya Klimenko (University of Zurich); Sebastian Pfeil (Erasmus University Rotterdam (EUR)  Erasmus School of Economics (ESE); Erasmus Research Institute of Management (ERIM)); JeanCharles Rochet (Swiss Finance Institute; University of Geneva  Geneva Finance Research Institute (GFRI); University of Zurich  Swiss Banking Institute (ISB)) 
Abstract:  We build a stylized dynamic general equilibrium model with financial frictions to analyze costs and benefits of capital requirements in the shortterm and longterm. We show that since increasing capital requirements limits the aggregate loan supply, the equilibrium loan rate spread increases, which raises bank profitability and the markettobook value of bank capital. Hence, banks build up larger capital buffers which (i) lowers the public losses in case of a systemic crisis and (ii) restores the banking sector’s lending capacity after the shortterm credit crunch induced by tighter regulation. We confirm our model’s dynamic implications in a panel VAR estimation, which suggests that bank lending has even increased in the longrun after the implementation of Basel III capital regulation. 
Keywords:  Bank capital requirements, credit crunch, systemic risk 
JEL:  E21 E32 F44 G21 G28 
Date:  2021–06 
URL:  http://d.repec.org/n?u=RePEc:chf:rpseri:rp2152&r= 
By:  Eduardo Abi Jaber (CES  Centre d'économie de la Sorbonne  UP1  Université Paris 1 PanthéonSorbonne  CNRS  Centre National de la Recherche Scientifique, UP1 UFR27  Université Paris 1 PanthéonSorbonne  UFR Mathématiques & Informatique  UP1  Université Paris 1 PanthéonSorbonne) 
Abstract:  We establish an explicit expression for the conditional Laplace transform of the integrated Volterra Wishart process in terms of a certain resolvent of the covariance function. The core ingredient is the derivation of the conditional Laplace transform of general Gaussian processes in terms of Fredholm's determinant and resolvent. Furthermore , we link the characteristic exponents to a system of nonstandard infinite dimensional matrix Riccati equations. This leads to a second representation of the Laplace transform for a special case of convolution kernel. In practice, we show that both representations can be approximated by either closed form solutions of conventional Wishart distributions or finite dimensional matrix Riccati equations stemming from conventional linearquadratic models. This allows fast pricing in a variety of highly flexible models, ranging from bond pricing in quadratic short rate models with rich autocorrelation structures, long range dependence and possible default risk, to pricing basket options with covariance risk in multivariate rough volatility models. 
Keywords:  Wishart processes,Gaussian processes,Fredholm's determinant,quadratic short rate models,rough volatility models 
Date:  2021 
URL:  http://d.repec.org/n?u=RePEc:hal:journl:hal02367200&r= 
By:  René M. Stulz; James G. Tompkins; Rohan Williamson; Zhongxia (Shelly) Ye 
Abstract:  We develop a theory of bank board risk committees. With this theory, such committees are valuable even though there is no expectation that bank risk is lower if the bank has a wellfunctioning risk committee. As predicted by our theory (1) many large and complex banks voluntarily chose to have a risk committee before the DoddFrank Act forced bank holding companies with assets in excess of $10 billion to have a board risk committee, and (2) establishing a board risk committee does not reduce a bank’s risk on average. Using unique interview data, we show that the work of risk committees is consistent with our theory in part. 
JEL:  G21 G28 G34 
Date:  2021–07 
URL:  http://d.repec.org/n?u=RePEc:nbr:nberwo:29106&r= 
By:  FelixBenedikt Liebrich 
Abstract:  We consider the problem of finding Paretooptimal allocations of risk among finitely many agents. The associated individual risk measures are law invariant, but with respect to agentdependent and potentially heterogeneous reference probability measures. Moreover, we assume that the individual risk assessments are consistent with the respective secondorder stochastic dominance relations. We do not assume their convexity though. A simple sufficient condition for the existence of Pareto optima is provided. Its proof combines local comonotone improvement with a Dieudonn\'etype argument, which also establishes a link of the optimal allocation problem to the realm of "collapse to the mean" results. Finally, we extend the results to capital requirements with multidimensional security markets. 
Date:  2021–08 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2108.05791&r= 
By:  Wenyuan Wang; Xiang Yu; Xiaowen Zhou 
Abstract:  Motivated by recent developments in risk management based on the U.S. bankruptcy code, we revisit De Finetti optimal dividend problems by incorporating the reorganization process and regulator's intervention documented in Chapter 11 bankruptcy. The resulting surplus process, bearing financial stress towards the more subtle concept of bankruptcy, corresponds to nonstandard spectrally negative Levy processes with endogenous regime switching. In both models without and with fixed transaction costs, some explicit expressions of the expected net present values under a barrier strategy, new to the literature, are established in terms of scale functions. With the help of these expressions, when the tail of the Levy measure is logconvex, the optimal dividend control in each problem is verified to be of the barrier type and the associated optimal barrier can be obtained in analytical form. 
Date:  2021–08 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2108.01800&r= 
By:  Michele Azzone; Roberto Baviera 
Abstract:  Empirical studies have emphasized that the equity implied volatility is characterized by a negative skew inversely proportional to the square root of the timetomaturity. We examine the shorttimetomaturity behavior of the implied volatility smile for pure jump exponential additive processes. An excellent calibration of the equity volatility surfaces has been achieved by a class of these additive processes with powerlaw scaling. The two powerlaw scaling parameters are $\beta$, related to the variance of jumps, and $\delta$, related to the smile asymmetry. It has been observed, in option market data, that $\beta=1$ and $\delta=1/2$. In this paper, we prove that the implied volatility of these additive processes is consistent, in the shorttime, with the equity market empirical characteristics if and only if $\beta=1$ and $\delta=1/2$. 
Date:  2021–08 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2108.02447&r= 
By:  Francesca Biagini; Lukas Gonon; Thomas Reitsam 
Abstract:  This article examines neural networkbased approximations for the superhedging price process of a contingent claim in a discrete time market model. First we prove that the $\alpha$quantile hedging price converges to the superhedging price at time $0$ for $\alpha$ tending to $1$, and show that the $\alpha$quantile hedging price can be approximated by a neural networkbased price. This provides a neural networkbased approximation for the superhedging price at time $0$ and also the superhedging strategy up to maturity. To obtain the superhedging price process for $t>0$, by using the Doob decomposition it is sufficient to determine the process of consumption. We show that it can be approximated by the essential supremum over a set of neural networks. Finally, we present numerical results. 
Date:  2021–07 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2107.14113&r= 
By:  Kennedy, Gerard (Central Bank of Ireland); Killeen, Neill (Central Bank of Ireland); Skouralis, Alexandros (Central Bank of Ireland); Velasco, Sofia (Central Bank of Ireland); Wosser, Michael (Central Bank of Ireland) 
Abstract:  This Note documents developments in the commercial real estate (CRE) market in Ireland since the onset of the COVID19 shock as well as examining the factors determining the outlook. The CRE market is important to monitor from a financial stability perspective owing to its size and systemic interlinkages to both the real economy and the wider financial system. We show that the CRE market in Ireland has experienced a downward adjustment in valuations since the onset of the COVID19 shock with the retail sector particularly affected. We highlight that components of the CRE market such as the retail and office sectors are particularly vulnerable to both nearterm and structural implications of the COVID19 shock such as the rise of online shopping and increased working from home practices. We combine a range of analytical approaches including forecast modelling techniques, the extension of the growthatrisk framework to CRE and scenario analysis to assess the potential downside risks to the CRE market in Ireland. 
Date:  2021–06 
URL:  http://d.repec.org/n?u=RePEc:cbi:fsnote:4/fs/21&r= 
By:  Abdoulaye Sy (CERDI  Centre d'Études et de Recherches sur le Développement International  CNRS  Centre National de la Recherche Scientifique  UCA  Université Clermont Auvergne); Catherine AraujoBonjean (CERDI  Centre d'Études et de Recherches sur le Développement International  CNRS  Centre National de la Recherche Scientifique  UCA  Université Clermont Auvergne); MarieEliette Dury (CERDI  Centre d'Études et de Recherches sur le Développement International  CNRS  Centre National de la Recherche Scientifique  UCA  Université Clermont Auvergne); Nourddine Azzaoui (LMBP  Laboratoire de Mathématiques Blaise Pascal  CNRS  Centre National de la Recherche Scientifique  UCA  Université Clermont Auvergne); Arnaud Guillin (LMBP  Laboratoire de Mathématiques Blaise Pascal  CNRS  Centre National de la Recherche Scientifique  UCA  Université Clermont Auvergne) 
Abstract:  A critical stage in drought hazard assessment is the definition of a drought event, and the measure of its intensity. Actually, the classical approach imposes to all climatic region the same set of thresholds for drought severity classification, hence resulting in a loss of information on rare events in the distribution tails, which are precisely the most important to catch in risk analysis. In order to better assess extreme events, we resort to an extreme value mixture model with a normal distribution for the bulk and a Generalized Pareto distribution for the upper and lower tails, to estimate the intensity of extreme droughts and their occurrence probability. Compare to the standard approach to drought hazard, which relies on a standardized precipitation index and a classification of drought intensity established from the cumulative standard normal distribution function, our approach allows the drought threshold and the occurrence probability of drought to depend on the specific characteristics of each precipitation distribution. An application to the West Africa region shows that the accuracy of our mixture model is higher than that of the standard model. The mixture performs better at modelling the lowest percentiles and specifically the return level of the centennial drought, which is generally overestimated in the standard approach. 
Keywords:  Mixture model,Generalized pareto distribution,Drought,Extreme value theory 
Date:  2021–07 
URL:  http://d.repec.org/n?u=RePEc:hal:wpaper:hal03297023&r= 
By:  Andrea Bergesio (University of Zurich  Department of Banking and Finance; Swiss Finance Institute); Pablo KochMedina (University of Zurich  Department of Banking and Finance; Swiss Finance Institute); Cosimo Munari (University of Zurich  Department of Banking and Finance; Swiss Finance Institute) 
Abstract:  Within the context of expected utility and in a discrete loss setting, we provide a complete account of the demand for insurance by strictlyrisk averse agents and riskneutral firms when they enjoy limited liability. When exposed to a bankrupting, binary loss and under actuarially fair prices, individuals and firms will either fully insure or not insure at all. The decision to insure will depend on whether the benefits the insuree derives from insurance after having compensated the damaged party are sufficiently attractive to justify the premium paid. When the loss is nonbinary, even when prices are actuarially fair, any amount of coinsurance can be optimal depending on the nature of the loss. 
Keywords:  insurance, riskaverse agent, riskneutral firm, franchise value, limited liability 
JEL:  D21 D81 G22 G32 G33 
Date:  2021–05 
URL:  http://d.repec.org/n?u=RePEc:chf:rpseri:rp2157&r= 
By:  Ruf, Johannes; Wang, Weiguan 
Abstract:  We study neural networks as nonparametric estimation tools for the hedging of options. To this end, we design a network, named HedgeNet, that directly outputs a hedging strategy. This network is trained to minimize the hedging error instead of the pricing error. Applied to endofday and tick prices of S&P 500 and Euro Stoxx 50 options, the network is able to reduce the mean squared hedging error of the BlackScholes benchmark significantly. However, a similar benefit arises by simple linear regressions that incorporate the leverage effect. 
Keywords:  benchmarking; BlackScholes; data Leakage; hedging error; leverage effect; statistical hedging; Taylor & Francis deal 
JEL:  J1 C1 
Date:  2021–06–30 
URL:  http://d.repec.org/n?u=RePEc:ehl:lserod:107811&r= 
By:  Jan Matas; Jan Posp\'i\v{s}il 
Abstract:  Rough Volterra volatility models are a progressive and promising field of research in derivative pricing. Although rough fractional stochastic volatility models already proved to be superior in real market data fitting, techniques used in simulation of these models are still inefficient in terms of speed and accuracy. This paper aims to present the accurate tools and techniques that could be used also in nowadays largely emerging pricing methods based on machine learning. In particular, we compare three widely used simulation methods: the Cholesky method, the Hybrid scheme, and the rDonsker scheme. We also comment on implementation of variance reduction techniques. In particular, we show the obstacles of the socalled turbocharging technique whose performance is sometimes contra productive. To overcome these obstacles, we suggest several modifications. 
Date:  2021–07 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2108.01999&r= 
By:  Haering, Alexander 
Abstract:  In this study I analyze how lottery framing and lottery display type affect the degree of higherorder risk preferences. I explore differences by comparing reduced and compound lottery framing, and by comparing lotteries in an urnstyle and in a spinnerstyle display format. Overall, my findings show that individual behavior is influenced by lottery framing but not by display format. 
Keywords:  Risk aversion,prudence,temperance,higherorder risk preferences,lottery framing 
JEL:  C91 D81 
Date:  2021 
URL:  http://d.repec.org/n?u=RePEc:zbw:rwirep:913&r= 
By:  Bureau Benjamin,; Duquerroy Anne,; Giorgi Julien,; Lé Mathias,; Scott Suzanne,; Vinas Frédéric 
Abstract:  Taking advantage of detailed firmlevel data on VAT returns, we estimate the monthly impact of the Covid19 crisis on the turnover of more than 645,000 French firms. Our approach, based on a microsimulation model, is innovative in a triple way. Firstly, we quantify the activity loss with respect to a counterfactual situation in which the crisis would not have hit. Secondly, we estimate this shock at the firm level, enabling a thorough analysis of activity loss heterogeneity throughout the crisis. In particular, we shade light on the dispersion of the shock both within and between industries. We show that the industry the firm operates in explains up to 48% of the monthly activity shocks’ variance weighted by employment, a much larger share than in a normal year. Finally, we leverage our monthly firmlevel data on sales to show how corporate activity has evolved along four distinct trajectories throughout 2020. The main determinant of belonging to a given profile of activity is the firm industry – defined at a very granular level. Conditional on industry, the activity trajectory is also correlated with the ability to adapt some firms have demonstrated during the crisis in terms of organization and production. 
Keywords:  Covid19 ; business dynamics ; microsimulation ; nonfinancial corporations 
JEL:  D22 G38 H32 
Date:  2021 
URL:  http://d.repec.org/n?u=RePEc:bfr:banfra:823&r= 
By:  Sydow, Matthias; Schilte, Aurore; Covi, Giovanni; Deipenbrock, Marija; Del Vecchio, Leonardo; Fiedor, Paweł; Fukker, Gábor; Gehrend, Max; Gourdel, Régis; Grassi, Alberto; Hilberg, Björn; Kaijser, Michiel; Kaoudis, Georgios; Mingarelli, Luca; Montagna, Mattia; Piquard, Thibaut; Salakhova, Dilyara; Tente, Natalia 
Abstract:  This paper shows how the combined endogenous reaction of banks and investment funds to an exogenous shock can amplify or dampen losses to the financial system compared to results from singlesector stress testing models. We build a new model of contagion propagation using a very large and granular data set for the euro area. Based on the economic shock caused by the Covid19 outbreak, we model three sources of exogenous shocks: a default shock, a market shock and a redemption shock. Our contagion mechanism operates through a dual channel of liquidity and solvency risk. The joint modelling of banks and funds provides new insights for the assessment of financial stability risks. Our analysis reveals that adding the fund sector to our model for banks leads to additional losses through fire sales and a further depletion of banks’ capital ratios by around one percentage point. JEL Classification: D85, G01, G21, G23, L14 
Keywords:  fire sales, liquidity, overlapping portfolios, price impact, stress testing 
Date:  2021–08 
URL:  http://d.repec.org/n?u=RePEc:ecb:ecbwps:20212581&r= 
By:  Foresti, Pasquale; Napolitano, Oreste 
Abstract:  The development of effective risk sharing mechanisms is one of the main passages for the success and longevity of a monetary union. In this paper, we study risk sharing, measured as income and consumption smoothing, in the EMU. As we employ timevarying estimations, we are able to retrieve time patterns of risk sharing for each member country and to compare them with the degree of economic asymmetry within the EMU. Other than documenting the need for stronger risk sharing mechanisms in the EMU, our results also suggest that much more attention should be dedicated to fostering homogeneity in risk sharing across member countries. We document the existence of increasing heterogeneity in the risk sharing capacity between member countries that can potentially exacerbate and amplify the impact of asymmetric shocks and further destabilize the EMU. 
Keywords:  consumption smoothing; economic asymmetry; EMU; income smoothing; risk sharing 
JEL:  J1 F3 G3 
Date:  2021–07–14 
URL:  http://d.repec.org/n?u=RePEc:ehl:lserod:111483&r= 
By:  Han Lin Shang; Fearghal Kearney 
Abstract:  This paper presents static and dynamic versions of univariate, multivariate, and multilevel functional timeseries methods to forecast implied volatility surfaces in foreign exchange markets. We find that dynamic functional principal component analysis generally improves outofsample forecast accuracy. More specifically, the dynamic univariate functional timeseries method shows the greatest improvement. Our models lead to multiple instances of statistically significant improvements in forecast accuracy for daily EURUSD, EURGBP, and EURJPY implied volatility surfaces across various maturities, when benchmarked against established methods. A stylised trading strategy is also employed to demonstrate the potential economic benefits of our proposed approach. 
Date:  2021–07 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2107.14026&r= 
By:  Tommaso Perez (Bank of Italy); Francesco Potente (Bank of Italy); Andrea Carboni (Bank of Italy); Alberto Di Iorio (Bank of Italy); Jacopo Raponi (Bank of Italy) 
Abstract:  Level 2 (L2) and Level 3 (L3) assets and liabilities represent a substantial portion of European banksâ€™ balance sheets, and valuing them is extremely difficult, since no liquid market prices are available. This paper relies on a large panel of euroarea banks between 2014 and 2019, and two different econometric frameworks, in order to estimate the relationship between the holdings of selected instruments (L2, L3 and NonPerforming Loans, NPLs) and banksâ€™ key performance and risk profile metrics, namely Credit Default Swaps (CDSs), PricetoBook (PtB) ratios and Zscores. It finds that larger holdings of L2 tend to be associated with higher CDSs, at least in the short run, while larger amounts of NPLs and L3 tend to characterize banks with higher CDSs, lower PtB ratios and worse Zscores, other things being equal. 
Keywords:  fair value accounting, level 2 instruments, level 3 instruments, nonperforming loans, prudential regulation, panel data models 
JEL:  G21 G28 C33 M41 
Date:  2021–07 
URL:  http://d.repec.org/n?u=RePEc:bdi:opques:qef_633_21&r= 
By:  Eghbal Rahimikia; Stefan Zohren; SerHuang Poon 
Abstract:  We develop FinText, a novel, stateoftheart, financial word embedding from Dow Jones Newswires Text News Feed Database. Incorporating this word embedding in a machine learning model produces a substantial increase in volatility forecasting performance on days with volatility jumps for 23 NASDAQ stocks from 27 July 2007 to 18 November 2016. A simple ensemble model, combining our word embedding and another machine learning model that uses limit order book data, provides the best forecasting performance for both normal and jump volatility days. Finally, we use Integrated Gradients and SHAP (SHapley Additive exPlanations) to make the results more 'explainable' and the model comparisons more transparent. 
Date:  2021–08 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2108.00480&r= 
By:  Nguyen, Hoang (Örebro University School of Business); Nguyen, TrongNghia (The University of Sydney Business School); Tran, MinhNgoc (The University of Sydney Business School) 
Abstract:  Stock returns are considered as a convolution of two random processes that are the return innovation and the volatility innovation. The correlation of these two processes tends to be negative which is the socalled leverage effect. In this study, we propose a dynamic leverage stochastic volatility (DLSV) model where the correlation structure between the return innovation and the volatility innovation is assumed to follow a generalized autoregressive score (GAS) process. We founnd that the leverage effect is reinforced in the market downturn period and weakened in the market upturn period. 
Keywords:  Dynamic leverage; GAS; stochastic volatility (SV) 
JEL:  C11 C52 C58 
Date:  2021–05–20 
URL:  http://d.repec.org/n?u=RePEc:hhs:oruesi:2021_014&r= 
By:  Sania Wadud; Robert D. Durand; Marc Gronwald 
Abstract:  This paper examines the effect of financialisation of futures markets has on the relationship between crude oil futures and equities by using the VARDCCGARCH model. Specifically, by accounting for the systematic patterns of commodity price volatility, namely, seasonality and maturity effects for the prefinancialisation (19932003) and postfinancialisation (20042019) period. While speculation that reflects noncommercial investors’ activity is found to have a negative impact on crude oil futures’ volatility before the financialisation period, open interest as a measure of liquidity has a negative effect after 2004. The finding indicates weakening seasonality in crude oil futures and diminishing Samuelson maturity effect i.e. volatility of the contract increases as it nears to expiration since financialisation. This confirms the importance of accounting for volatility dynamics while contributing to financialisation debate. 
Keywords:  financialisation, volatility dynamics, Samuelson hypothesis, correlation, seasonality 
JEL:  C32 G12 G15 
Date:  2021 
URL:  http://d.repec.org/n?u=RePEc:ces:ceswps:_9202&r= 
By:  Li, Erqian; Härdle, Wolfgang; Dai, Xiaowen; Tian, Maozai 
Abstract:  The proportional subdistribution hazards (PSH) model is popularly used to deal with competing risks data. Censored quantile regression provides an important supplement as well as variable selection methods, due to large numbers of irrelevant covariates in practice. In this paper, we study variable selection procedures based on penalized weighted quantile regression for competing risks models, which is conveniently applied by researchers. Asymptotic properties of the proposed estimators including consistency and asymptotic normality of nonpenalized estimator and consistency of variable selection are established. Monte Carlo simulation studies are conducted, showing that the proposed methods are considerably stable and efficient. A real data about bone marrow transplant (BMT) is also analyzed to illustrate the application of proposed procedure. 
Keywords:  Competing risks,Cumulative incidence function,KaplanMeier estimator,Redistribution method 
JEL:  C00 
Date:  2021 
URL:  http://d.repec.org/n?u=RePEc:zbw:irtgdp:2021013&r= 
By:  Christian Bayer; Simon Breneis 
Abstract:  We consider rough stochastic volatility models where the variance process satisfies a stochastic Volterra equation with the fractional kernel, as in the rough Bergomi and the rough Heston model. In particular, the variance process is therefore not a Markov process or semimartingale, and has quite low H\"olderregularity. In practice, simulating such rough processes thus often results in high computational cost. To remedy this, we study approximations of stochastic Volterra equations using an $N$dimensional diffusion process defined as solution to a system of ordinary stochastic differential equation. If the coefficients of the stochastic Volterra equation are Lipschitz continuous, we show that these approximations converge strongly with superpolynomial rate in $N$. Finally, we apply this approximation to compute the implied volatility smile of a European call option under the rough Bergomi and the rough Heston model. 
Date:  2021–08 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2108.05048&r= 
By:  Zihao Wang; Kun Li; Steve Q. Xia; Hongfu Liu 
Abstract:  We investigate the effectiveness of different machine learning methodologies in predicting economic cycles. We identify the deep learning methodology of BiLSTM with Autoencoder as the most accurate model to forecast the beginning and end of economic recessions in the U.S. We adopt commonlyavailable macro and marketcondition features to compare the ability of different machine learning models to generate good predictions both insample and outofsample. The proposed model is flexible and dynamic when both predictive variables and model coefficients vary over time. It provided good outofsample predictions for the past two recessions and early warning about the COVID19 recession. 
Date:  2021–07 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2107.10980&r= 
By:  NAKASHIMA, KIYOTAKA; Ogawa, Toshiaki 
Abstract:  This study examines the impact of strengthening bank capital supervision on bank behavior in the incomplete enforcement of regulations. In a dynamic model of banks facing persistent idiosyncratic shocks, banks accumulate regulatory capital and decrease charter value and lending in the short run, while in the long run, the banking system achieves stability. To test the shortrun implications, we utilize the introduction of the prompt corrective action program in Japan as a quasinatural experiment. Using some empirical specifications with bank and loanlevel data, we find empirical evidence consistent with the theoretical predictions. 
Keywords:  regulatory surveillance; incomplete enforcement; heterogeneous bank model; prompt corrective action; bank capital ratio; credit crunch 
JEL:  G00 G21 G28 
Date:  2021–08–11 
URL:  http://d.repec.org/n?u=RePEc:pra:mprapa:109147&r= 
By:  Bureau Benjamin,; Duquerroy Anne,; Giorgi Julien,; Lé Mathias,; Scott Suzanne,; Vinas Frédéric 
Abstract:  Using rich granular data for over 645 000 French firms in 2020, this paper builds a microsimulation model to assess the impact of the Covid19 crisis on corporate liquidity. Going beyond the aggregate picture, we document that while net debt has been fairly stable at the macroeconomic level, individual heterogeneity is widespread. Significant dispersion in changes in net debt prevails both between and within industries, before as well as after public support. We show that the probability to experience a negative liquidity shock  as well as the intensity of this shock  are negatively correlated with the initial credit quality of the firm (based on Banque de France internal ratings). Our model also finds that public support dampens significantly the impact of Covid on the dispersion of liquidity shocks and brings back the distribution of liquidity shocks closer to its precrisis path but with fatter tails. 
Keywords:  Covid19; Microsimulation; Nonfinancial Corporations; Cash Holding; Debt 
JEL:  D22 G32 G38 
Date:  2021 
URL:  http://d.repec.org/n?u=RePEc:bfr:banfra:824&r= 