nep-rmg New Economics Papers
on Risk Management
Issue of 2023‒02‒20
29 papers chosen by

  1. Quantitative Reverse Stress Testing, Bottom Up By Claudio Albanese; Stéphane Crépey; Stefano Iabichino
  2. Bank Stress Testing, Human Capital Investment and Risk Management By Thomas Schneider; Philip Strahan; Jun Yang
  3. Dynamic conditional mean risk sharing in the compound Poisson surplus model By Denuit, Michel; Robert, Christian Y.
  4. Macroprudential Regulation: A Risk Management Approach By Daniel Dimitrov; Sweder van Wijnbergen
  5. Efficient Risk Estimation for the Credit Valuation Adjustment By Michael B. Giles; Abdul-Lateef Haji-Ali; Jonathan Spence
  6. Nietzsche and Fractal Geometry: a philosophical continuity By Leandro Gualario
  7. Using machine learning to measure financial risk in China By Al-Haschimi, Alexander; Apostolou, Apostolos; Azqueta-Gavaldon, Andres; Ricci, Martino
  8. Chronicle of a death foretold: does higher volatility anticipate corporate default? By Ampudia, Miguel; Busetto, Filippo; Fornari, Fabio
  9. A delayed dual risk model By Lingjiong Zhu
  10. Did Insurers Become Risk-Loving During “Low-for-Long”? The Role of Returns, Ratings, and Regulation By Mr. Juan Sole; Jeroen Brinkhoff
  11. A GRU-Based Dynamic Generative Factor Model for CVaR Portfolio Optimization By Chuting Sun; Wenxuan Ma; Xing Yan
  12. Computation of Expected Shortfall by fast detection of worst scenarios By Bruno Bouchard; Adil Reghai; Benjamin Virrion
  13. Network analysis of the UK reinsurance market By Kotlicki, Artur; Austin, Andrea; Humphry, David; Burnett, Hanna; Ridgill, Philip; Smith, Sam
  14. The impact of changes in bank capital requirements By Raja, Akash
  16. Asymmetric volatility impulse response functions By Hafner, Christian; Herwartz, Helmut
  17. Outlier robust specification of multiplicative time-varying volatility models By Cristina Amado
  18. The quintic Ornstein-Uhlenbeck volatility model that jointly calibrates SPX & VIX smiles By Eduardo Abi Jaber; Camille Illand; Shaun Xiaoyuan Li
  19. Methods in Econophysics: Estimating the Probability Density and Volatility By Moawia Alghalith
  20. Does the Launch of Shanghai Crude Oil Futures Stabilize the Spot Market ? A Financial Cycle Perspective By Dan Zhang; Arash Farnoosh; Zhengwei Ma
  21. Approximations of multi-period liability values by simple formulas By Nils Engler; Filip Lindskog
  22. Statistical Inference for Hüsler–Reiss Graphical Models Through Matrix Completions By Hentschel, Manuel; Engelke, Sebastian; Segers, Johan
  23. The Technology of Decentralized Finance (DeFi) By Raphael Auer; Bernhard Haslhofer; Stefan Kitzler; Pietro Saggese; Friedhelm Victor
  24. The ring-fencing bonus By Erten, Irem; Neamtu, Ioana; Thanassoulis, John
  25. Labor Income Risk and the Cross-Section of Expected Returns By Mykola Pinchuk
  26. When do Default Nudges Work? By Carl Bonander; Mats Ekman; Niklas Jakobsson
  27. Eliminating Disparate Treatment in Modeling Default of Credit Card Clients By Tom, Daniel M. Ph.D.
  28. Fiscal Rules, Independent Fiscal Institutions, and Sovereign Risk By Capraru, Bogdan; Georgescu, George; Sprincean, Nicu
  29. The Law and Economics of AI Liability By Miriam Buiten; Alexandre de Streel; Martin Peitz

  1. By: Claudio Albanese; Stéphane Crépey (UPCité - UFR Mathématiques - Université Paris Cité - UFR Mathématiques [Sciences] - UPCité - Université Paris Cité); Stefano Iabichino
    Abstract: We propose a bottom-up quantitative reverse stress testing framework that identifies forward-looking fragilities tailored to a bank's portfolio, credit and funding strategies, models, and calibration constraints. Thus, instead of relying on historical events, we run a Monte Carlo simulation, and we mine those future states that contribute the most to a bank's cost of capital expressed in terms of scenario differential. We find that such an approach allows identifying both the systemic and idiosyncratic weaknesses of the bank's portfolio, with applications that include solvency risk, extreme events hedging, liquidity risk management, trading and credit limits, model validation and model risk management.
    Keywords: quantitative reverse stress testing cost of capital (KVA) model validation model risk trading limits PFE JEL Classification: D81 G13 G28 G32 Mathematics Subject Classification: 91B30 91G20 91G30 91G40, quantitative reverse stress testing, cost of capital (KVA), model validation, model risk, trading limits, PFE
    Date: 2022–12–21
  2. By: Thomas Schneider; Philip Strahan; Jun Yang
    Abstract: This paper studies banks’ investment in risk management practices following the Global Financial Crisis and the advent of stress testing. Banks that experienced greater losses during the Crisis exhibit stronger demand for risk management talents. Banks increase their demand for highly skilled stress test labor in anticipation of a test and following poor performance on a test. Following this higher demand, banks exhibit lower systematic risk and lower profitability. While stress testing has modernized banks’ internal risk management by spurring the acquisition of highly skilled risk management talent, recent changes to the tests could erode its efficacy.
    JEL: G20
    Date: 2023–01
  3. By: Denuit, Michel (Université catholique de Louvain, LIDAM/ISBA, Belgium); Robert, Christian Y. (CREST - ENSAE)
    Abstract: This paper proposes a dynamic risk-sharing procedure, framed into the classical insurance surplus process. Compared to the standard setting where total losses are shared at the end of the period, losses are allocated among participants at their occurrence time in the proposed model. A dynamic version of the conditional mean risk-sharing rule proposed by Denuit and Dhaene (2012) is applied to this end. The analysis adopts two different points of views: a collective one for the pool and an individual one for sharing losses and adjusting the amounts of contributions among participants. These two views are compatible under the compound Poisson risk process. Guarantees can also be added by partnering with an insurer.
    Keywords: Risk pooling ; conditional mean risk sharing ; ruin probability ; mutual exclusivity
    Date: 2022–11–08
  4. By: Daniel Dimitrov; Sweder van Wijnbergen
    Abstract: We address the problem of regulating the size of banks’ macroprudential capital buffers by using market-based estimates of systemic risk combined with a structural framework for credit risk assessment. We develop a set of novel modeling mech- anisms through which capital buffers can be allocated across systemic banks: (1) equalizing the expected impact between a systemic and a non-systemic institution; (2) minimizing the aggregate systemic risk; (3) balancing the social costs and ben- efits of higher capital requirements. We apply the model to the European banking sector and find sometimes substantial differences with the capital buffers currently assigned by national regulators. Since capital buffers are one of the main policy instruments for managing banks’ potential contributions to systemic distress, our findings have substantial implications for systemic risk in the EEA.
    Keywords: systemic risk; regulation; implied market measures; financial institutions; CDS rates
    JEL: G01 G20 G18 G38
    Date: 2023–02
  5. By: Michael B. Giles; Abdul-Lateef Haji-Ali; Jonathan Spence
    Abstract: The valuation of over-the-counter derivatives is subject to a series of valuation adjustments known as xVA, which pose additional risks for financial institutions. Associated risk measures, such as the value-at-risk of an underlying valuation adjustment, play an important role in managing these risks. Monte Carlo methods are often regarded as inefficient for computing such measures. As an example, we consider the value-at-risk of the Credit Valuation Adjustment (CVA-VaR), which can be expressed using a triple nested expectation. Traditional Monte Carlo methods are often inefficient at handling several nested expectations. Utilising recent developments in multilevel nested simulation for probabilities, we construct a hierarchical estimator of the CVA-VaR which reduces the computational complexity by 3 orders of magnitude compared to standard Monte Carlo.
    Date: 2023–01
  6. By: Leandro Gualario (Auteur indépendant)
    Abstract: The purpose of this work is to highlight the epistemological proximity between Nietzsche's philosophy of science and the underlying philosophical principles of fractal geometry, as illustrated in the main work of its creator, French mathematician Benoit Mandelbrot. This work also aims to find the end of this philosophical continuity, finding an important divergence between Nietzsche's philosophy of risk taking and Mandelbrot's legacy in risk management.
    Keywords: Epistemology, Mathematics, Fractal Geometry, Finance, Risk, Risk Management, Models, Nietzsche, Mandelbrot
    Date: 2023–01–13
  7. By: Al-Haschimi, Alexander; Apostolou, Apostolos; Azqueta-Gavaldon, Andres; Ricci, Martino
    Abstract: We develop a measure of overall financial risk in China by applying machine learning techniques to textual data. A pre-defined set of relevant newspaper articles is first selected using a specific constellation of risk-related keywords. Then, we employ topical modelling based on an unsupervised machine learning algorithm to decompose financial risk into its thematic drivers. The resulting aggregated indicator can identify major episodes of overall heightened financial risks in China, which cannot be consistently captured using financial data. Finally, a structural VAR framework is employed to show that shocks to the financial risk measure have a significant impact on macroeconomic and financial variables in China and abroad. JEL Classification: C32, C65, E32, F44, G15
    Keywords: China, financial risk, LDA, machine learning, textual analysis, topic modelling
    Date: 2023–01
  8. By: Ampudia, Miguel (Bank for International Settlements); Busetto, Filippo (Bank of England); Fornari, Fabio (Bank of England)
    Abstract: We test whether a simple measure of corporate insolvency based on equity return volatility – and denoted as Distance to Insolvency (DI) – delivers better predictions of corporate default than the widely-used Expected Default Frequency (EDF) measure computed by Moody’s. We look at the predictive power that current DIs and EDFs have for future defaults, both at a firm-level and at an aggregate level. At the granular level, both DIs and EDFs anticipate corporate defaults, but the DI contains information over and above the EDF, especially at longer forecasting horizons. At an aggregate level the DI shows superior forecasting power compared to the EDF, for horizons between three and twelve months. We illustrate the predictive power of the DI measure by examining how corporate defaults would have evolved during Covid-19 had the ECB not implemented the pandemic emergency purchase programme (PEPP).
    Keywords: Default probability; equity volatility; Distance to Insolvency; Expected Default Frequency
    JEL: C53 C58 G33
    Date: 2022–10–28
  9. By: Lingjiong Zhu
    Abstract: In this paper, we study a dual risk model with delays in the spirit of Dassios-Zhao. When a new innovation occurs, there is a delay before the innovation turns into a profit. We obtain large initial surplus asymptotics for the ruin probability and ruin time distributions. For some special cases, we get closed-form formulas. Numerical illustrations will also be provided.
    Date: 2023–01
  10. By: Mr. Juan Sole; Jeroen Brinkhoff
    Abstract: European life insurance companies are important bond investors and had traditionally played a stabilizing role in financial markets by pursuing “buy-and-hold” investment strategies. However, since the onset of the ultra-low interest rates era in 2008, observers noted a decline in the credit quality of insurers’ bond portfolios. The commonly-held explanation for this deterioration is that low returns pushed insurers to become more risk-taking. We argue that other factors—such as surging rating downgrades, bond revaluations, and regulatory changes—also played a key role. We estimate that rating changes, revaluations, and search for yield each account for about one-third each of the total deterioration in credit quality. This result has important policy implications as it reestablishes the view that insurers’ investment behavior tends to be passive through the cycle—rather than risk-seeking.
    Keywords: Life Insurance sector; financial stability; credit ratings; bond revaluation; life insurance insurance company; bond portfolio; investment behavior; bond investor; Bonds; Insurance companies; Corporate bonds; Sovereign bonds; Bond ratings; Global
    Date: 2022–09–30
  11. By: Chuting Sun; Wenxuan Ma; Xing Yan
    Abstract: The dynamic portfolio construction problem requires dynamic modeling of the joint distribution of multivariate stock returns. To achieve this, we propose a dynamic generative factor model which uses random variable transformation as an implicit way of distribution modeling and relies on the GRU network for the dynamic modeling. The proposed model captures the dynamic dependence among multivariate stock returns, especially focusing on the tail-side properties. We also propose a two-step iterative algorithm to train the model and then predict the time-varying model parameters (including the time-invariant tail parameters). At each time, we can easily generate new samples from the learned generative model, and we further perform CVaR portfolio optimization with the samples to form a dynamic portfolio strategy. Numerical experiments on stock data show that our strategy using the proposed model leads to a wiser investment that promises a higher reward while presenting lower tail risk and smaller maximum drawdown.
    Date: 2023–01
  12. By: Bruno Bouchard (CEREMADE - CEntre de REcherches en MAthématiques de la DEcision - Université Paris Dauphine-PSL - PSL - Université Paris sciences et lettres - CNRS - Centre National de la Recherche Scientifique); Adil Reghai (Natixis Asset Management); Benjamin Virrion (CEREMADE - CEntre de REcherches en MAthématiques de la DEcision - Université Paris Dauphine-PSL - PSL - Université Paris sciences et lettres - CNRS - Centre National de la Recherche Scientifique, Natixis Asset Management)
    Abstract: We consider a multi-step algorithm for the computation of the historical expected shortfall such as defined by the Basel Minimum Capital Requirements for Market Risk. At each step of the algorithm, we use Monte Carlo simulations to reduce the number of historical scenarios that potentially belong to the set of worst scenarios. The number of simulations increases as the number of candidate scenarios is reduced and the distance between them diminishes. For the most naive scheme, we show that the L p-error of the estimator of the Expected Shortfall is bounded by a linear combination of the probabilities of inversion of favorable and unfavorable scenarios at each step, and of the last step Monte Carlo error associated to each scenario. By using concentration inequalities, we then show that, for sub-gamma pricing errors, the probabilities of inversion converge at an exponential rate in the number of simulated paths. We then propose an adaptative version in which the algorithm improves step by step its knowledge on the unknown parameters of interest: mean and variance of the Monte Carlo estimators of the different scenarios. Both schemes can be optimized by using dynamic programming algorithms that can be solved off-line. To our knowledge, these are the first non-asymptotic bounds for such estimators. Our hypotheses are weak enough to allow for the use of estimators for the different scenarios and steps based on the same random variables, which, in practice, reduces considerably the computational effort. First numerical tests are performed.
    Keywords: Expected Shortfall, ranking and selection, sequential design, Bayesian filter
    Date: 2021–03–26
  13. By: Kotlicki, Artur (Bank of England); Austin, Andrea (Australian Energy Regulator); Humphry, David (Bank of England); Burnett, Hanna (Bank of England); Ridgill, Philip (Bank of England); Smith, Sam (Bank of England)
    Abstract: We provide an empirical analysis of the network structure of the UK reinsurance sector based on 2016 Solvency II regulatory data. We examine counterparty credit risk originating from reinsurance contracts as a source of financial contagion in the insurance industry. The granularity of the Solvency II data provides a new opportunity for detailed analysis of the actual exposures in the system, detection of potential systemic vulnerabilities, and reinsurance spirals. In our multi-layered network approach, we incorporate information on reinsurance contract risk types and ownership structure for both life and non-life insurers. Our findings suggest that the UK reinsurance sector exhibits the ‘small-world’ property with a scale-free, core-periphery structure and topological characteristics common to other financial networks. These characteristics of risk dispersion from the periphery to the core make the network ‘robust-yet-fragile’ to financial shocks. We explore the robustness of the network to adverse shocks through a stress-simulation exercise, where we find it robust to system wide shocks affecting the value of total investments, and to idiosyncratic shocks applied to large, highly interconnected reinsurers.
    Keywords: Reinsurance; systemic risk; financial contagion; scale-free network
    JEL: D85 G01 G22 G28
    Date: 2023–01–23
  14. By: Raja, Akash (Bank of England)
    Abstract: This paper studies how banks respond to capital regulation using confidential data on bank‑specific requirements in the UK. Banks do adjust their capital ratios following changes in requirements, though the pass-through is incomplete. While they lower capital ratios following a loosening of requirements, they eat into their existing capital buffers when facing tighter regulatory minima. I find that the main adjustment channels have changed since the financial crisis. Prior to the crisis, banks responded to changes in their requirements through capital accumulation and loan quantities; however, they have since then primarily altered the risk composition of assets.
    Keywords: Capital requirements; microprudential policy; banking; capital ratios
    JEL: E58 G21 G28
    Date: 2023–01–23
  15. By: Sakib, S M Nazmuz
    Abstract: The future development when an insurance company is in a difficult circumstance can be described by a stochastic process which the insurance company is tasked to manage effectively in order to achieve best goal of the company. Application of an effective risk or loss management model in an insurance company brings in more revenue for the insurer and less conditional pay-out of claims to the insured. Insurance losses, risks and premium calculation or pricing have been active and essential topics in insurance and actuarial literatures but most of these literatures did not only stand the test of time due to dynamic nature of insurance principles and practices in highly evolving environment but also lack the intuitive and detailed standard rating logic to adjust loss rating to a particular experience. There is a need to strike a balance in charging an appropriate and equitable premium by applying a suitable loss model that gives a sufficient uniquely determined solution that will not necessarily put an insurer or the insured in an uncertain awkward business situations. Therefore, the objective of this research is to obtain sufficient conditions for convergence of algorithm towards a fixed point under typical insurance loss and actuarial circumstances to achieve a uniquely determined solution. At the end, a unique fixed point was determined and the algorithm formulated converges towards that point through straightforward and simplified generalised formulae and functions.
    Date: 2023–01–08
  16. By: Hafner, Christian (Université catholique de Louvain, LIDAM/ISBA, Belgium); Herwartz, Helmut (University of Göttingen)
    Abstract: Volatility impulse response functions (VIRFs) have been introduced to unravel the effects of shocks on (co-)variances for the case of classical multivariate GARCH specifications. This paper proposes generalized VIRFs for the case of asymmetric specifications which capture stylized features such as the leverage effect. In a bivariate application comprising a global equity index and gold prices, we show that generalized VIRFs can be used to reassess the role of gold as a safe-haven asset.
    Keywords: Multivariate GARCH ; leverage effect ; volatility impulse response analysis ; safe haven
    JEL: C32 G15
    Date: 2022–11–18
  17. By: Cristina Amado (NIPE/Center for Research in Economics and Management, University of Minho, Portugal; and CREATES and Aarhus University)
    Abstract: Nonstationarity and outlying observations are commonly encountered in financial time series. It is thus expected that models are able to accommodate these stylized facts and the techniques used are suitable to specify such models. In this paper we relax the assumption of stationarity and consider the problem of detecting smooth changes in the unconditional variance in the presence of outliers. It is found by simulation that the misspecifi cation test for constancy of the unconditional variance in GARCH models can be severely adversely affected in the presence of additive outliers. An outlier robust specifi cation procedure is also proposed to mitigate the effects of outliers for building multiplicative time-varying volatility models. The outlier robust variant of the test is shown to perform better than the conventional test in terms of size and power. An application to commodity returns illustrates the usefulness of the robust specifi cation procedure.
    Keywords: Conditional heteroskedasticity; Testing parameter constancy; Model specification; Time-varying unconditional variance; Outliers.
    JEL: C12 C32 C51 C52
    Date: 2022
  18. By: Eduardo Abi Jaber (X - École polytechnique); Camille Illand (AXA Investment Managers, Multi Asset Client Solutions, Quantitative Research - AXA); Shaun Xiaoyuan Li (UP1 - Université Paris 1 Panthéon-Sorbonne, AXA Investment Managers, Multi Asset Client Solutions, Quantitative Research - AXA)
    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.
    Keywords: SPX and VIX modeling, Stochastic volatility, Pricing, Calibration
    Date: 2022–12–21
  19. By: Moawia Alghalith
    Abstract: We discuss and analyze some recent literature that introduced pioneering methods in econophysics. In doing so, we review recent methods of estimating the volatility, volatility of volatility, and probability densities. These methods will have useful applications in econophysics and finance.
    Date: 2022–11
  20. By: Dan Zhang (CUP - China University of Petroleum Beijing, IFPEN - IFP Energies nouvelles - IFPEN - IFP Energies nouvelles, IFP School); Arash Farnoosh (IFPEN - IFP Energies nouvelles - IFPEN - IFP Energies nouvelles, IFP School); Zhengwei Ma (CUP - China University of Petroleum Beijing)
    Abstract: Based on the examination of price discovery between Shanghai crude oil futures and the spot market, this paper explores whether the introduction of Shanghai crude oil futures can play a stabilizing role in the spot market, alleviating the impact of the financial cycle risk on the crude oil market from March 2018 to December 2019. The results show that there is only a uni-directional relationship of the spot price to futures price, and spot plays a leading role in price discovery. The risk of the financial cycle will increase the volatility of spot price, and the introduction of crude oil futures market can increase the impact of the financial cycle on the spot market. The additional research on the microcosmic mechanism of Shanghai crude oil futures indicates that crude oil futures market mainly influences the spot market fluctuation through the behaviour of traders: speculation increases price volatility in the spot market, which is more pronounced in the high volatility of the financial cycle as oppose to hedging transaction.
    Keywords: Shanghai crude oil futures, Price discovery, Stabilization, Financial cycle
    Date: 2022–01
  21. By: Nils Engler; Filip Lindskog
    Abstract: This paper is motivated by computational challenges arising in multi-period valuation in insurance. Aggregate insurance liability cashflows typically correspond to stochastic payments several years into the future. However, insurance regulation requires that capital requirements are computed for a one-year horizon, by considering cashflows during the year and end-of-year liability values. This implies that liability values must be computed recursively, backwards in time, starting from the year of the most distant liability payments. Solving such backward recursions with paper and pen is rarely possible, and numerical solutions give rise to major computational challenges. The aim of this paper is to provide explicit and easily computable expressions for multi-period valuations that appear as limit objects for a sequence of multi-period models that converge in terms of conditional weak convergence. Such convergence appears naturally if we consider large insurance portfolios such that the liability cashflows, appropriately centered and scaled, converge weakly as the size of the portfolio tends to infinity.
    Date: 2023–01
  22. By: Hentschel, Manuel (University of Geneva); Engelke, Sebastian (University of Geneva); Segers, Johan (Université catholique de Louvain, LIDAM/ISBA, Belgium)
    Abstract: The severity of multivariate extreme events is driven by the dependence between the largest marginal observations. The Hüsler–Reiss distribution is a versatile model for this extremal dependence, and it is usually parameterized by a variogram matrix. In order to represent conditional independence relations and obtain sparse parameterizations, we introduce the novel Hüsler–Reiss precision matrix. Similarly to the Gaussian case, this matrix appears naturally in density representations of the Hüsler–Reiss Pareto distribution and encodes the extremal graphical structure through its zero pattern. For a given, arbitrary graph we prove the existence and uniqueness of the completion of a partially specified Hüsler–Reiss variogram matrix so that its precision matrix has zeros on non-edges in the graph. Using suitable estimators for the parameters on the edges, our theory provides the first consistent estimator of graph structured Hüsler–Reiss distributions. If the graph is unknown, our method can be combined with recent structure learning algorithms to jointly infer the graph and the corresponding parameter matrix. Based on our methodology, we propose new tools for statistical inference of sparse Hüsler–Reiss models and illustrate them on large flight delay data in the U.S.
    Keywords: Extreme value analysis ; multivariate generalized Pareto distribution ; sparsity ; variogram
    Date: 2022–10–27
  23. By: Raphael Auer; Bernhard Haslhofer; Stefan Kitzler; Pietro Saggese; Friedhelm Victor
    Abstract: Decentralized Finance (DeFi) is a new financial paradigm that leverages distributed ledger technologies to offer services such as lending, investing, or exchanging cryptoassets without relying on a traditional centralized intermediary. A range of DeFi protocols implements these services as a suite of smart contracts, ie software programs that encode the logic of conventional financial operations. Instead of transacting with a counterparty, DeFi users thus interact with software programs that pool the resources of other DeFi users to maintain control over their funds. This paper provides a deep dive into the overall architecture, the technical primitives, and the financial functionalities of DeFi protocols. We analyse and explain the individual components and how they interact through the lens of a DeFi stack reference (DSR) model featuring three layers: settlement, applications and interfaces. We discuss the technical aspects of each layer of the DSR model. Then, we describe the financial services for the most relevant DeFi categories, ie decentralized exchanges, lending protocols, derivatives protocols and aggregators. The latter exploit the property that smart contracts can be "composed", ie utilize the functionalities of other protocols to provide novel financial services. We discuss how composability allows complex financial products to be assembled, which could have applications in the traditional financial industry. We discuss potential sources of systemic risk and conclude by mapping out an agenda for research in this area.
    Keywords: financial engineering, decentralized finance, DeFi, blockchain, ethereum, DLT, cryptocurrencies, stablecoins, cryptoassets
    JEL: E42 E58 F31 G19 G23 L50 O33 G12
    Date: 2023–01
  24. By: Erten, Irem (Warwick Business School, University of Warwick); Neamtu, Ioana (Bank of England); Thanassoulis, John (Warwick Business School, University of Warwick, CEPR)
    Abstract: We study the impact of ring-fencing on bank risk using short-term repo rates. Exploiting confidential data on the near-universe of sterling-denominated repo transactions, we find compelling evidence that banking groups subject to ring-fencing are perceived to be safer; repo investors lend to ring-fenced groups at lower rates, controlling for bank characteristics and collateral risk. Ring-fenced groups charge more to supply liquidity. We show that these effects are driven by the ring-fenced subsidiary; the other subsidiaries are not adversely impacted by ring-fencing to any meaningful extent. We further document that the banking groups reduce their risk-taking after the imposition of the fence. Our paper suggests that structural reforms can have a significant beneficial impact on risk in the banking system.
    Keywords: Ring-fencing; repo markets; risk-taking
    JEL: G12 G18 G21
    Date: 2023–01–23
  25. By: Mykola Pinchuk
    Abstract: This paper explores asset pricing implications of unemployment risk from sectoral shifts. I proxy for this risk using cross-industry dispersion (CID), defined as a mean absolute deviation of returns of 49 industry portfolios. CID peaks during periods of accelerated sectoral reallocation and heightened uncertainty. I find that expected stock returns are related cross-sectionally to the sensitivities of returns to innovations in CID. Annualized returns of the stocks with high sensitivity to CID are 5.9% lower than the returns of the stocks with low sensitivity. Abnormal returns with respect to the best factor model are 3.5%, suggesting that common factors can not explain this return spread. Stocks with high sensitivity to CID are likely to be the stocks, which benefited from sectoral shifts. CID positively predicts unemployment through its long-term component, consistent with the hypothesis that CID is a proxy for unemployment risk from sectoral shifts.
    Date: 2023–01
  26. By: Carl Bonander; Mats Ekman; Niklas Jakobsson
    Abstract: Nudging is a burgeoning topic in science and in policy, but evidence on the effectiveness of nudges among differentially-incentivized groups is lacking. This paper exploits regional variations in the roll-out of the Covid-19 vaccine in Sweden to examine the effect of a nudge on groups whose intrinsic incentives are different: 16-17-year-olds, for whom Covid-19 is not dangerous, and 50-59-year-olds, who face a substantial risk of death or severe disease. The response is strong in the younger but absent in the older age group, consistent with the theory that nudges work best for choices that are not meaningful to the individual.
    Date: 2023–01
  27. By: Tom, Daniel M. Ph.D.
    Abstract: A recent online search for model performance for benchmarking purposes reveals evidence of disparate treatment on a prohibitive basis in ML models appearing in the search result. Using our logistic regression with AI approach, we are able to build a superior credit model without any prohibitive and other demographic characteristics (gender, age, marital status, level of education) from the default of credit card clients dataset in the UCI Machine Learning Repository. We compare our AI flashlight beam search result to exhaustive search approach in the space of all possible models, and the AI search finds the highest separation/highest likelihood models efficiently after evaluating a small number of model candidates.
    Date: 2023–01–17
  28. By: Capraru, Bogdan (Romania Fiscal Council); Georgescu, George (Romania Fiscal Council); Sprincean, Nicu (Romania Fiscal Council)
    Abstract: This paper explores the implications of fiscal rules and independent fiscal institutions (IFIs) on sovereign risk. We employ a dynamic panel model for a sample composed of 24 countries members of the European Union over the period 2007-2019 and document that fiscal rules contain sovereign default risk measured by the credit default swap (CDS) spreads on sovereign bonds. IFIs, through monitoring compliance with numerical fiscal rules and assuring the transparency of the budgetary process, lead to a reduction in the likelihood of sovereign default, especially those that went through a process of institutional reform. Moreover, having developed financial markets accompanied by both fiscal rules and independent fiscal institutions contribute to a reduction in sovereign CDS premia against the backdrop of increased sovereign risk induced by more developed financial markets.
    Keywords: Fiscal rules; independent fiscal institutions; sovereign CDS spreads; sovereign risk
    JEL: E62 G15 H63
    Date: 2023–02
  29. By: Miriam Buiten; Alexandre de Streel; Martin Peitz
    Abstract: When AI systems possess the characteristics of autonomy and unpredictability, they present challenges for the existing liability framework. (Semi)-autonomous AI systems shift control over these systems away from users and towards producers, while errors of AI systems may be difficult to foresee. Policymakers are faced with the questions when existing civil liability rules do not adequately cover risks arising in the context of AI systems, and how they then should be adapted. This paper addresses these two questions for EU non-contractual liability rules. It considers how liability rules affect the incentives of producers, users, and bystanders that may be harmed by AI. The paper provides concrete recommendations for updating the EU Product Liability Directive and for an EU liability framework for owners and users of AI.
    Keywords: Artificial Intelligence, EU law
    JEL: K13 O33
    Date: 2022–11

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