nep-rmg New Economics Papers
on Risk Management
Issue of 2020‒03‒02
twenty-two papers chosen by

  1. Inflated credit ratings, regulatory arbitrage and capital requirements: Do investors strategically allocate bond portfolios? By Martijn Boermans; Bram van der Kroft
  2. Beyond Connectedness: A Covariance Decomposition based Network Risk Model By Umut Akovali
  3. A novel multivariate risk measure: the Kendall VaR By Matthieu Garcin; Dominique Guegan; Bertrand Hassani
  4. On the equivalence between Value-at-Risk and Expected Shortfall in non-concave optimization By An Chen; Mitja Stadje; Fangyuan Zhang
  5. Crowded trades, market clustering, and price instability By Marc van Kralingen; Diego Garlaschelli; Karolina Scholtus; Iman van Lelyveld
  6. Predicting Downside Risks to House Prices and Macro-Financial Stability By Andrea Deghi; Mitsuru Katagiri; Sohaib Shahid; Nico Valckx
  7. Sharing of longevity basis risk in pension schemes with income-drawdown guarantees By Ankush Agarwal; Christian-Oliver Ewald; Yongjie Wang
  8. Invariant measures for fractional stochastic volatility models By Bal\'azs Gerencs\'er; Mikl\'os R\'asonyi
  9. OPEX-risk as a source of CAPEX-bias in monopoly regulation By Gert Brunekreeft; Margarethe Rammerstorfer
  10. Diverging roads: Theory-based vs. machine learning-implied stock risk premia By Grammig, Joachim; Hanenberg, Constantin; Schlag, Christian; Sönksen, Jantje
  11. Predicting Bank Loan Default with Extreme Gradient Boosting By Rising Odegua
  12. Public credit guarantee and financial additionalities across SME risk classes By Emanuele Ciani; Marco Gallo; Zeno Rotondi
  13. What do lab experiments tell us about the real world? The case of lotteries with extreme payoffs By Raman Kachurka; Michał Krawczyk; Joanna Rachubik
  14. The impact of TLTRO2 on the Italian credit market: some econometric evidence By Lucia Esposito; Davide Fantino; Yeji Sung
  15. The network of firms implied by the news By Zheng, Hannan; Schwenkler, Gustavo
  16. Happiness and Gold Prices By Byström, Hans
  17. Operational and cyber risks in the financial sector By Iñaki Aldasoro; Leonardo Gambacorta; Paolo Giudici; Thomas Leach
  18. The climate risk for the finance in Italy By Ivan Faiella; Danila Malvolti
  19. Libra or Librae? Basket based stablecoins to mitigate foreign exchange volatility spillovers By Paolo Giudici; Thomas Leach; Paolo Pagnottoni
  20. Regulators vs. markets: Do differences in their bank risk perceptions affect lending terms? By Delis, Manthos; Kim, Suk-Joong; Politsidis, Panagiotis; Wu, Eliza
  21. The enhancement of resilience to disasters and climate change in the Caribbean through the modernization of the energy sector By Flores, Adrián; Peralta, Leda
  22. Simplifying and Improving the Performance of Risk Adjustment Systems By Thomas G. McGuire; Anna L. Zink; Sherri Rose

  1. By: Martijn Boermans; Bram van der Kroft
    Abstract: This study investigates whether banks and insurance corporations perform regulatory arbitrage by buying bonds with inflated credit ratings. We argue that flaws in minimum capital requirements incentivize risk-taking behavior by financial institutions, diminishing financial stability. We estimate the probability of a bond having an inflated credit rating using conditional credit default swap spread distributions. We merge this data with a unique bond-level portfolio holdings dataset. The results show that banks and insurance corporations invest more in bonds with inflated credit ratings, while this effect is absent for investors who do not face capital requirements based on credit ratings. Consequently, the regulatory capital buffers of banks and insurance corporations are effectively reduced by respectively 13 and 28 percent.
    Keywords: Inflated credit ratings; capital requirements; regulatory arbitrage; Basel III; Solvency II; portfolio choice; securities holdings statistics
    JEL: G11 G21 G22 G24 G28
    Date: 2020–02
  2. By: Umut Akovali (Koc University)
    Abstract: This study extends the Diebold-Yilmaz Connectedness Index (DYCI) methodology and, based on forecast error covariance decompositions, derives a network risk model for a portfolio of assets. As a normalized measure of the sum of variance contributions, system-wide connectedness averages out the information embedded in the covariance matrix in aggregating pairwise directional measures. This actually does matter, especially when there are large differences in asset variances. As a first step towards deriving the network risk model, the portfolio covariance matrix is decomposed to obtain the network-driven component of the portfolio variance using covariance decompositions. A second step shows that a common factor model can be estimated to obtain both the variance and covariance decompositions. In a third step, using quantile regressions, the proposed network risk model is estimated for different shock sizes. It is shown, in contrast to the DYCI model, the dynamic quantile estimation of the network risk model can differentiate even small shocks at both tails. This result is obtained because the network risk model makes full use of information embedded in the covariance matrix. Estimation results show that in two recent episodes of financial market turmoil, the proposed network risk model captures the responses to systemic events better than the system-wide index.
    Keywords: Connectedness; Covariance decomposition; Factor models, Idiosyncratic risk; Portfolio risk; Quantile regressions; Systemic risk; Vector Autoregressions; Variance decomposition.
    JEL: C32 G21
    Date: 2020–02
  3. By: Matthieu Garcin (Natixis Asset Management, Labex ReFi - UP1 - Université Panthéon-Sorbonne); Dominique Guegan (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, Labex ReFi - UP1 - Université Panthéon-Sorbonne); Bertrand Hassani (Grupo Santander, CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, Labex ReFi - UP1 - Université Panthéon-Sorbonne)
    Abstract: The definition of multivariate Value at Risk is a challenging problem, whose most common solutions are given by the lower- and upper-orthant VaRs, which are based on copulas: the lower-orthant VaR is indeed the quantile of the multivariate distribution function, whereas the upper-orthant VaR is the quantile of the multivariate survival function. In this paper we introduce a new approach introducing a total-order multivariate Value at Risk, referred to as the Kendall Value at Risk, which links the copula approach to an alternative definition of multivariate quantiles, known as the quantile surface, which is not used in finance, to our knowledge. We more precisely transform the notion of orthant VaR thanks to the Kendall function so as to get a multivariate VaR with some advantageous properties compared to the standard orthant VaR: it is based on a total order and, for a non-atomic and Rd-supported density function, there is no distinction anymore between the d-dimensional VaRs based on the distribution function or on the survival function. We quantify the differences between this new kendall VaR and orthant VaRs. In particular, we show that the Kendall VaR is less (respectively more) conservative than the lower-orthant (resp. upper-orthant) VaR. The definition and the properties of the Kendall VaR are illustrated using Gumbel and Clayton copulas with lognormal marginal distributions and several levels of risk.
    Keywords: total order,copula,Value at Risk,multivariate quantile,risk measure,Kendall function
    Date: 2018–04
  4. By: An Chen; Mitja Stadje; Fangyuan Zhang
    Abstract: This paper studies a non-concave optimization problem under a Value-at-Risk (VaR) or an Expected Shortfall (ES) constraint. The non-concavity of the problem stems from the non-linear payoff structure of the optimizing investor. We obtain the closed-form optimal wealth with an ES constraint as well as with a VaR constraint respectively, and explicitly calculate the optimal trading strategy for a CRRA (i.e., constant relative risk aversion) utility function. In our non-concave optimization problem, we find that for any VaR-constraint with an arbitrary risk level, there exists an ES-constraint leading to the same investment strategy, assuming that the regulation only protects the debt holders' benefit to a certain level. This differs from the conclusion drawn in Basak and Shapiro (2001) for the concave optimization problem, where VaR and ES lead to different solutions.
    Date: 2020–02
  5. By: Marc van Kralingen; Diego Garlaschelli; Karolina Scholtus; Iman van Lelyveld
    Abstract: Crowded trades by similarly trading peers influence the dynamics of asset prices, possibly creating systemic risk. We propose a market clustering measure using granular trading data. For each stock the clustering measure captures the degree of trading overlap among any two investors in that stock. We investigate the effect of crowded trades on stock price stability and show that market clustering has a causal effect on the properties of the tails of the stock return distribution, particularly the positive tail, even after controlling for commonly considered risk drivers. Reduced investor pool diversity could thus negatively affect stock price stability
    Keywords: crowded trading; tail-risk; financial stability
    JEL: G02 G14 G20
    Date: 2020–01
  6. By: Andrea Deghi; Mitsuru Katagiri; Sohaib Shahid; Nico Valckx
    Abstract: This paper predicts downside risks to future real house price growth (house-prices-at-risk or HaR) in 32 advanced and emerging market economies. Through a macro-model and predictive quantile regressions, we show that current house price overvaluation, excessive credit growth, and tighter financial conditions jointly forecast higher house-prices-at-risk up to three years ahead. House-prices-at-risk help predict future growth at-risk and financial crises. We also investigate and propose policy solutions for preventing the identified risks. We find that overall, a tightening of macroprudential policy is the most effective at curbing downside risks to house prices, whereas a loosening of conventional monetary policy reduces downside risks only in advanced economies and only in the short-term.
    Date: 2020–01–17
  7. By: Ankush Agarwal; Christian-Oliver Ewald; Yongjie Wang
    Abstract: This work studies a stochastic optimal control problem for a pension scheme which provides an income-drawdown policy to its members after their retirement. To manage the scheme efficiently, the manager and members agree to share the investment risk based on a pre-decided risk-sharing rule. The objective is to maximise both sides' utilities by controlling the manager's investment in risky assets and members' benefit withdrawals. We use stochastic affine class models to describe the force of mortality of the members' population and consider a longevity bond whose coupon payment is linked to a survival index. In our framework, we also investigate the longevity basis risk, which arises when the members' and the longevity bond's reference populations show different mortality behaviours. By applying the dynamic programming principle to solve the corresponding HJB equations, we derive optimal solutions for the single- and sub-population cases. Our numerical results show that by sharing the risk, both manager and members increase their utility. Moreover, even in the presence of longevity basis risk, we demonstrate that the longevity bond acts as an effective hedging instrument.
    Date: 2020–02
  8. By: Bal\'azs Gerencs\'er; Mikl\'os R\'asonyi
    Abstract: We establish that a large class of non-Markovian stochastic volatility models converge to an invariant measure as time tends to infinity. Our arguments are based on a novel coupling idea which is of interest on its own right.
    Date: 2020–02
  9. By: Gert Brunekreeft; Margarethe Rammerstorfer
    Abstract: This paper shows with a formal model that under monopoly regulation, OPEX-risk can be a source for a CAPEX-bias. If OPEX and CAPEX are substitutes, the regulated firm can reduce the risk of the firm and thereby reduce the true cost of capital by rebalancing OPEX and CAPEX. If the allowed rate-of-return on capital is not influenced by the firm’s actions, this creates a margin between the allowed rate-of-return and the true cost of capital. We examine two remedies: first, fixed-OPEX-CAPEX-share (FOCS) which is a variation of TOTEX-regulation and second, OPEX-mark-up. FOCS internalizes the CAPEX-bias and can be implemented easily. The OPEX-mark-up is effective, but it will be challenging to reach the optimum.
    Keywords: Capex-bias, Opex-risk, regulated monopoly
    JEL: K23 L12 L51 L9
    Date: 2020–02
  10. By: Grammig, Joachim; Hanenberg, Constantin; Schlag, Christian; Sönksen, Jantje
    Abstract: We assess financial theory-based and machine learning-implied measurements of stock risk premia by comparing the quality of their return forecasts. In the low signal-to-noise environment of a one month horizon, we find that it is preferable to rely on a theory-based approach instead of engaging in the computerintensive hyper-parameter tuning of statistical models. The theory-based approach also delivers a solid performance at the one year horizon, at which only one machine learning methodology (random forest) performs substantially better. We also consider ways to combine the opposing modeling philosophies, and identify the use of random forests to account for the approximation residuals of the theory-based approach as a promising hybrid strategy. It combines the advantages of the two diverging paths in the finance world.
    Keywords: stock risk premia,return forecasts,machine learning,theorybased return prediction
    JEL: C53 C58 G12 G17
    Date: 2020
  11. By: Rising Odegua
    Abstract: Loan default prediction is one of the most important and critical problems faced by banks and other financial institutions as it has a huge effect on profit. Although many traditional methods exist for mining information about a loan application, most of these methods seem to be under-performing as there have been reported increases in the number of bad loans. In this paper, we use an Extreme Gradient Boosting algorithm called XGBoost for loan default prediction. The prediction is based on a loan data from a leading bank taking into consideration data sets from both the loan application and the demographic of the applicant. We also present important evaluation metrics such as Accuracy, Recall, precision, F1-Score and ROC area of the analysis. This paper provides an effective basis for loan credit approval in order to identify risky customers from a large number of loan applications using predictive modeling.
    Date: 2020–01
  12. By: Emanuele Ciani (Bank of Italy); Marco Gallo (Bank of Italy); Zeno Rotondi (UniCredit)
    Abstract: In this paper we study the functioning of the Italian public guarantee fund (“Fondo Centrale di Garanzia”, FCG) for Small and Medium Enterprises (SMEs). Using an instrumental variable strategy, based on the eligibility for the FCG, we investigate whether the guarantee generated additional loans and/or lower interest rates to SMEs. Differently from previous literature, by focusing on the lending activity of a single large Italian lender we control for the probability of default as assessed by the bank’s internal rating model, and we examine whether the effects of the guarantee differ across firms belonging to different classes of risk. We find that guaranteed firms receive an additional amount of credit equal to 7-8 percent of their total banking exposure. We also estimate a reduction of about 50 basis points of interest rates applied to term loans granted to guaranteed firms. The effects on credit availability are concentrated in the intermediate class of solvent firms, i.e. those neither too safe nor too risky. Conversely, interest rate effects are present in all classes, but for the least risky firms. Finally, we observe a stronger impact of the guarantee for solvent firms with a longer relationship with the bank. This finding questions their ability to reduce financial frictions for very young firms.
    Keywords: credit guarantees, access to credit, banking
    JEL: L25 O12 G28
    Date: 2020–02
  13. By: Raman Kachurka (Faculty of Economic Sciences, University of Warsaw); Michał Krawczyk (Faculty of Economic Sciences, University of Warsaw); Joanna Rachubik (Faculty of Economic Sciences, University of Warsaw)
    Abstract: In this study, we conduct a laboratory experiment in which the subjects make choices between real-world lottery tickets typically purchased by lottery customers. In this way, we are able to reliably offer extremely high potential payoffs, something rarely possible in economic experiments. In a between-subject design, we separately manipulate a number of features that distinguish the situation faced by the customers in the field and by subjects in typical laboratory experiments. We also have the unique opportunity to compare our data to actual sales data provided by the operator of the lottery. Overall, we find the distributions to be highly similar (meaning high external validity of the laboratory experiment). The only manipulation that makes a major difference is that when the probabilities of winning specific amounts are explicitly provided (which is not the case in the field), choices shift towards options with lower payoff variance. We also find that standard laboratory measures of risk posture fail to explain our subjects’ behavior in the main task.
    Keywords: Decision making under risk, External validity, Longshot bias, Perception of randomness, Number preferences in lotteries
    JEL: C91 D01 D81 D83 D91
    Date: 2020
  14. By: Lucia Esposito (Bank of Italy); Davide Fantino (Bank of Italy); Yeji Sung (Columbia University)
    Abstract: This paper evaluates the impact of the second series of Targeted Longer-Term Refinancing Operations (TLTRO2) on the amount of credit granted to non-financial private corporations and on the interest rates applied to loans in Italy, using data on credit transactions, bank and firm characteristics and a difference-in-differences approach. We find that TLTRO2 had a positive impact on the Italian credit market, encouraging medium-term lending to firms and reducing credit interest rates. While firms overall benefited from TLTRO2 irrespective of their risk category and size, we document heterogeneous treatment effects. Regarding firms’ risk category, the effects on credit quantities are larger for low-risk firms while those on credit interest rate are larger for high-risk firms. Regarding firms’ size, smaller firms benefited the most both in terms of amounts borrowed and interest rates. Furthermore, our evidence suggests that monetary policy transmission of TLTRO2 is stronger for banks with a low bad debt ratio in their balance sheets.
    Keywords: Unconventional Monetary Policy, Pass-through, Policy Evaluation
    JEL: E51 E52
    Date: 2020–02
  15. By: Zheng, Hannan; Schwenkler, Gustavo
    Abstract: We show that the news is a rich source of data on distressed firm links that drive firm-level and aggregate risks. The news tends to report about links in which a less popular firm is distressed and may contaminate a more popular firm. This constitutes a contagion channel that yields predictable returns and downgrades. Shocks to the degree of news-implied firm connectivity predict increases in aggregate volatilities, credit spreads, and default rates, and declines in output. To obtain our results, we propose a machine learning methodology that takes text data as input and outputs a data-implied firm network. JEL Classification: E32, E44, L11, G10, C82
    Keywords: contagion, machine learning, natural language processing, networks, predictability, risk measurement
    Date: 2020–02
  16. By: Byström, Hans (Department of Economics, Lund University)
    Abstract: We use the Twitter-based Hedonometer happiness index to study the link between happiness and gold price changes. We find no significant correlation between the two when we look at correlations across the entire distributions. However, turning to an extreme value theory (EVT) modeling of the tails of the non-normally distributed happiness distribution we find that during particularly depressing days the gold price often goes up. In a sense, gold is found to serve as a happiness-related safe haven, i.e. as a hedge against extreme unhappiness.
    Keywords: Twitter; happiness; Hedonometer; gold price; tail; extreme value theory
    JEL: D83 G14
    Date: 2020–02–24
  17. By: Iñaki Aldasoro; Leonardo Gambacorta; Paolo Giudici; Thomas Leach
    Abstract: We use a unique cross-country dataset at the loss event level to document the evolution and characteristics of banks' operational risk. After a spike following the great financial crisis, operational losses have declined in recent years. The spike is largely accounted for by losses due to improper business practices in large banks that occurred in the run-up to the crisis but were recognised only later. Operational value-at-risk can vary substantially - from 6% to 12% of total gross income - depending on the method used. It takes, on average, more than a year for operational losses to be discovered and recognised in the books. However, there is significant heterogeneity across regions and event types. For instance, improper business practices and internal fraud events take longer to be discovered. Operational losses are not independent of macroeconomic conditions and regulatory characteristics. In particular, we show that credit booms and periods of excessively accommodative monetary policy are followed by larger operational losses. Better supervision, on the other hand, is associated with lower operational losses. We provide an estimate of losses due to cyber events, a subset of operational loss events. Cyber losses are a small fraction of total operational losses, but can account for a significant share of total operational value-at-risk.
    Keywords: operational risks, financial institutions, cyber risks, time to discovery, value-at-risk
    JEL: D5 D62 D82 G2 H41
    Date: 2020–02
  18. By: Ivan Faiella (Banca d'Italia); Danila Malvolti (Ministry of Economy and Finance)
    Abstract: The increasing attention paid to the possible consequences of climate change for the financial sector has strengthened international cooperation on green finance, with initiatives from both the industry and the institutions. International surveys show that so far there has been no adequate growth in awareness of the risks linked to climate change and the opportunities linked to the transition towards a low carbon economy. Evidence acquired on Climate-Related Financial Risk (CRFR) disclosure in Italy has confirmed the same conclusions. We have therefore identified three steps with the aim of encouraging financial institutions to take CRFR into account in their corporate risk management strategies: 1) create a information hub to gather the information required for assessing the CRFR; 2) compile a list of the information not yet available; 3) define standard methodologies that allow the climate scenarios to be part of the decision-making processes of financial institutions.
    Keywords: climate change, financial risk, Italy
    JEL: G21 P48 Q54
    Date: 2020–02
  19. By: Paolo Giudici (Università di Pavia); Thomas Leach (Università di Pavia); Paolo Pagnottoni (Università di Pavia)
    Abstract: The paper aims to assess, from an empirical viewpoint, the advantages of a stablecoin whose value is derived from a basket of underlying currencies, against a stablecoin which is pegged to the value of one major currency, such as the dollar. To this aim, we ?rst ?nd the optimal weights of the currencies that can comprise our basket. We then employ volatility spillover decomposition methods to understand which foreign currency mostly drives the others. We then look at how the stability of either stablecoin is affected by currency shocks, by means of VAR models and impulse response functions. Our empirical ?ndings show that our basket based stablecoin is less volatile than all single currencies. This results is fundamental for policy making, and especially for emerging markets with a high level of remittances: a librae (basket based stable coin) can preserve their value during turbolent times better than a libra (single currency based stable coin).
    Keywords: : Cryptocurrencies; Fintech; Stablecoins; Spillover; Variance decomposition.
    JEL: C01 C32 C58 G21 G32
    Date: 2020–02
  20. By: Delis, Manthos; Kim, Suk-Joong; Politsidis, Panagiotis; Wu, Eliza
    Abstract: We quantify the differences between market and regulatory assessments of bank portfolio risk, showing that larger differences significantly reduce corporate lending rates. Specifically, to entice borrowers, banks reduce spreads by approximately 4.1% following a one standard deviation increase in our measure for bank asset-risk differences. This amounts to an interest income loss of USD 1.95 million on a loan of average size and duration. The separate effects of market and regulatory risk are much less potent. Our study reveals a disciplinary-competition effect in favor of corporate borrowers when there is information asymmetry between investors and bank regulators.
    Keywords: bank portfolio risk; markets vs. regulators; syndicated loans; cost of credit; market discipline; competition
    JEL: G2 G21 G33
    Date: 2020–02–08
  21. By: Flores, Adrián; Peralta, Leda
    Abstract: The Caribbean region is prone to disasters due to its geographic location. The exposures and resulting impacts of these disasters are aggravated by persistent social, economic and environmental vulnerabilities. Compounded with the region’s current dependence on imported fossil fuels and financial constraints, this study seeks to stimulate discussions around the complementarity of energy with every societal sector as well as its links with disaster risk management, and promote government-wide management that integrates energy policies, disaster management and climate change impacts.
    Date: 2020–01–28
  22. By: Thomas G. McGuire; Anna L. Zink; Sherri Rose
    Abstract: Risk-adjustment systems used to pay health plans in individual health insurance markets have evolved towards better “fit” of payments to plan spending, at the individual and group levels, generally achieved by adding variables used for risk adjustment. Adding variables demands further plan and provider-supplied data. Some data called for in the more complex systems may be easily manipulated by providers, leading to unintended “upcoding” or to unnecessary service utilization. While these drawbacks are recognized, they are hard to quantify and are difficult to balance against the concrete, measurable improvements in fit that may be attained by adding variables to the formula. This paper takes a different approach to improving the performance of health plan payment systems. Using the HHS-HHC V0519 model of plan payment in the Marketplaces as a starting point, we constrain fit at the individual and group level to be as good or better than the current payment model while reducing the number of variables called for in the model. Opportunities for simplification are created by the introduction of three elements in design of plan payment: reinsurance (based on high spending or plan losses), constrained regressions, and powerful machine learning methods for variable selection. We first drop all variables relying on drug claims. Further major reductions in the number of diagnostic-based risk adjustors are possible using machine learning integrated with our constrained regressions. The fit performance of our simpler alternatives is as good or better than the current HHS-HHC V0519 formula.
    JEL: I11 I13 I18
    Date: 2020–02

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