nep-rmg New Economics Papers
on Risk Management
Issue of 2021‒07‒19
thirty-one papers chosen by

  1. Computing near-optimal Value-at-Risk portfolios using Integer Programming techniques By Onur Babat; Juan C. Vera; Luis F. Zuluaga
  2. COVID-19, Credit Risk and Macro Fundamentals By Anna Dubinova; Andre Lucas; Sean Telg
  3. Banks' Backtesting Exceptions during the COVID-19 Crash: Causes and Consequences By Alice Abboud; Christopher Anderson; Aaron L. Game; Diana A. Iercosan; Hulusi Inanoglu; David Lynch
  4. Assessment of Risk Management Practices in the Public Sector of Malaysia By Said, Jamaliah; Alam, Md. Mahmudul; Johari, Razana Juhaida
  5. On the Selection of Loss Severity Distributions to Model Operational Risk By Daniel Hadley; Harry Joe; Natalia Nolde
  6. Ordering expectations conditional on a sum, with application to insurance risk pooling By Denuit, Michel; Robert, Christian Y.
  7. Assessing the Safety of Central Counterparties By Mark Paddrik; H. Peyton Young
  8. Market risk factors analysis for an international mining company. Multi-dimensional, heavy-tailed-based modelling By {\L}ukasz Bielak; Aleksandra Grzesiek; Joanna Janczura; Agnieszka Wy{\l}oma\'nska
  9. A Neural Frequency-Severity Model and Its Application to Insurance Claims By Dong-Young Lim
  10. Basel III in Nigeria: making it work By Ozili, Peterson K
  11. Credit scoring using neural networks and SURE posterior probability calibration By Matthieu Garcin; Samuel St\'ephan
  12. Approximations to ultimate ruin probabilities with a Wienner process perturbation By Yacine Koucha; Alfredo D. Egidio dos Reis
  13. Counterparty Choice, Bank Interconnectedness, and Systemic Risk By Andrew Ellul; Dasol Kim
  14. Evaluating the Role of Insurance in Managing Risk of Future Pandemics By Howard Kunreuther; Jason Schupp
  16. Portfolio insurance under rough volatility and Volterra processes By Dupret, Jean-Loup; Hainaut, Donatien
  17. Lending Standards and Borrowing Premia in Unsecured Credit Markets By Kyle Dempsey; Felicia Ionescu
  18. Two-Sample Testing for Tail Copulas with an Application to Equity Indices By Can, S.U.; Einmahl, John; Laeven, Roger
  19. Two-Sample Testing for Tail Copulas with an Application to Equity Indices By Can, S.U.; Einmahl, John; Laeven, Roger
  20. The Growth-at-Risk (GaR) Framework: Implication For Ukraine By Anastasiya Ivanova; Alona Shmygel; Ihor Lubchuk
  21. Hedge Fund Treasury Trading and Funding Fragility: Evidence from the COVID-19 Crisis By Mathias S. Kruttli; Phillip J. Monin; Lubomir Petrasek; Sumudu W. Watugala
  22. Short-term risk management for electricity retailers under rising shares of decentralized solar generation By Russo, Marianna; Kraft, Emil; Bertsch, Valentin; Keles, Dogan
  23. Equity Risk Factors for the Long and Short Run: Pricing and Performance at Different Frequencies By Terri van der Zwan; Erik Hennink; Patrick Tuijp
  24. Collaborative Insurance Sustainability and Network Structure By Arthur Charpentier; Lariosse Kouakou; Matthias L\"owe; Philipp Ratz; Franck Vermet
  25. Robust Decision-Making Under Risk and Ambiguity By Maximilian Blesch; Philipp Eisenhauer
  26. A fractional multi-states model for insurance By Hainaut, Donatien
  27. Vasicek Model Extension. Premature default By Osadchiy, Maksim
  28. Shareholder Liability and Bank Failure By Felipe Aldunate; Dirk Jenter; Arthur Korteweg; Peter Koudijs
  29. Measuring Financial Time Series Similarity With a View to Identifying Profitable Stock Market Opportunities By Rian Dolphin; Barry Smyth; Yang Xu; Ruihai Dong
  30. Soft habits By Knut Anton Mork; Vegard Skonseng Bjerketvedt
  31. The impact of COVID-19 on corporate fragility in the United Kingdom: Insights from a new calibrated firm-level Corporate Sector Agent-Based (CAB) Model By Sebastian Barnes; Robert Hillman; George Wharf; Duncan MacDonald

  1. By: Onur Babat; Juan C. Vera; Luis F. Zuluaga
    Abstract: Value-at-Risk (VaR) is one of the main regulatory tools used for risk management purposes. However, it is difficult to compute optimal VaR portfolios; that is, an optimal risk-reward portfolio allocation using VaR as the risk measure. This is due to VaR being non-convex and of combinatorial nature. In particular, it is well known that the VaR portfolio problem can be formulated as a mixed integer linear program (MILP) that is difficult to solve with current MILP solvers for medium to large-scale instances of the problem. Here, we present an algorithm to compute near-optimal VaR portfolios that takes advantage of this MILP formulation and provides a guarantee of the solution's near-optimality. As a byproduct, we obtain an algorithm to compute tight lower bounds on the VaR portfolio problem that outperform related algorithms proposed in the literature for this purpose. The near-optimality guarantee provided by the proposed algorithm is obtained thanks to the relation between minimum risk portfolios satisfying a reward benchmark and the corresponding maximum reward portfolios satisfying a risk benchmark. These alternate formulations of the portfolio allocation problem have been frequently studied in the case of convex risk measures and concave reward functions. Here, this relationship is considered for general risk measures and reward functions. To illustrate the efficiency of the presented algorithm, numerical results are presented using historical asset returns from the US financial market.
    Date: 2021–06
  2. By: Anna Dubinova (Vrije Universiteit Amsterdam); Andre Lucas (Vrije Universiteit Amsterdam); Sean Telg (Vrije Universiteit Amsterdam)
    Abstract: We investigate the relationship between macro fundamentals and credit risk, rating migrations and defaults during the start of the COVID-19 pandemic. We find that credit risk models that use macro fundamentals as covariates overestimate credit risk incidence due to the unprecedented drops in economic activity in the first lockdowns. We argue that this break in the macro-credit linkage is less affected if we take an unobserved components modeling framework, both at shorter and longer credit risk horizons.
    Keywords: COVID-19, credit risk, macro fundamentals, frailty factors, dynamic latent factors
    JEL: G21 C22
    Date: 2021–06–28
  3. By: Alice Abboud; Christopher Anderson; Aaron L. Game; Diana A. Iercosan; Hulusi Inanoglu; David Lynch
    Abstract: Banks' numerous and simultaneous backtesting exceptions in March 2020, during the COVID-19-related market crash, would have amplified their already-large spike in market risk capital requirements in the absence of regulatory intervention. This note provides background on how backtesting exceptions affect capital requirements generally, the source of those exceptions during the COVID-19 crash, and how regulators exercised discretion to mitigate the unintended capital increase.
    Date: 2021–07–08
  4. By: Said, Jamaliah; Alam, Md. Mahmudul (Universiti Utara Malaysia); Johari, Razana Juhaida
    Abstract: Public sectors around the world, especially in the developing counties, are not functioning well due to widespread fraud, governance, corruption, and inefficacy. For this reason, the world’s public sectors need to improve their efficacy by using a sound risk management system. This study attempts to comprehend the phenomenon of current risk management practices among the public sector employees in different service schemes in Malaysia. A questionnaire survey was utilized to collect primary data from 194 department heads in Malaysia’s federal ministries. The collected data was analysed using descriptive statistics and factor analysis. Findings revealed that 94.7% of respondents agreed to implementing risk management in their respective departments, but the level of priority for these risk management factors differs based on the service schemes. This study will assist policymakers to identify what is needed to enhance risk management practices in the public sector.
    Date: 2020–06–29
  5. By: Daniel Hadley; Harry Joe; Natalia Nolde
    Abstract: Accurate modeling of operational risk is important for a bank and the finance industry as a whole to prepare for potentially catastrophic losses. One approach to modeling operational is the loss distribution approach, which requires a bank to group operational losses into risk categories and select a loss frequency and severity distribution for each category. This approach estimates the annual operational loss distribution, and a bank must set aside capital, called regulatory capital, equal to the 0.999 quantile of this estimated distribution. In practice, this approach may produce unstable regulatory capital calculations from year-to-year as selected loss severity distribution families change. This paper presents truncation probability estimates for loss severity data and a consistent quantile scoring function on annual loss data as useful severity distribution selection criteria that may lead to more stable regulatory capital. Additionally, the Sinh-arcSinh distribution is another flexible candidate family for modeling loss severities that can be easily estimated using the maximum likelihood approach. Finally, we recommend that loss frequencies below the minimum reporting threshold be collected so that loss severity data can be treated as censored data.
    Date: 2021–07
  6. By: Denuit, Michel (Université catholique de Louvain, LIDAM/ISBA, Belgium); Robert, Christian Y. (ENSAE, Paris, France)
    Abstract: This paper establishes general comparison results for conditional expectations given sums of independent random variables, in terms of convex and related stochastic order relations. It is shown that these expectations decrease in the number of terms comprised in the conditioning sums and inherit their variability. Additional inequalities are obtained under regression dependence in the sum. These results are applied to study diversification effects resulting from pooling independent insurance losses according to the risk allocation rule proposed by Denuit and Dhaene (2012). New convergence results are obtained from the decreasingness of the conditional expectations in the number of participants, with respect to the convex order. It is shown that the variance of individual contributions tends to 0 in many cases so that the contribution tends to the corresponding expected loss.
    Keywords: conditional expectation ; law of large number ; convex order ; increasing convex order ; dilation order ; directionally convex order ; insurance risk pooling
    Date: 2021–01–01
  7. By: Mark Paddrik (Office of Financial Research); H. Peyton Young (Office of Financial Research)
    Abstract: We propose a general framework for empirically assessing a central counterparty's capacity to cope with severe financial stress. Using public disclosure data for global central counterparties (CCPs), we show how to estimate the probability that a CCP could cover any specified fraction of payment defaults by its members. This framework supplements conventional standards of risk management such as Cover 2, and provides a comparative and comprehensive approach to assessing risk protection across CCPs that is not predicated on a specific number of member defaults. We apply the approach to a wide range of CCPs in different geographical jurisdictions and asset classes and find that there are substantial differences in protection coverage. In particular, large European CCPs appear to be significantly safer than their counterparts in Asia-Pacific and North America. These differences are also reflected in supervisory data that provide CCP members' risk assessments of the CCPs to which they belong.
    Keywords: central counterparty, default waterfall, guarantee fund, default probability
    Date: 2021–07–14
  8. By: {\L}ukasz Bielak; Aleksandra Grzesiek; Joanna Janczura; Agnieszka Wy{\l}oma\'nska
    Abstract: Mining companies to properly manage their operations and be ready to make business decisions, are required to analyze potential scenarios for main market risk factors. The most important risk factors for KGHM, one of the biggest companies active in the metals and mining industry, are the price of copper (Cu), traded in US dollars, and the Polish zloty (PLN) exchange rate (USDPLN). The main scope of the paper is to understand the mid- and long-term dynamics of these two risk factors. For a mining company it might help to properly evaluate potential downside market risk and optimise hedging instruments. From the market risk management perspective, it is also important to analyze the dynamics of these two factors combined with the price of copper in Polish zloty (Cu in PLN), which jointly drive the revenues, cash flows, and financial results of the company. Based on the relation between analyzed risk factors and distribution analysis, we propose to use two-dimensional vector autoregressive (VAR) model with the $\alpha-$stable distribution. The non-homogeneity of the data is reflected in two identified regimes: first - corresponding to the 2008 crisis and second - to the stable market situation. As a natural implication of the model fitted to market assets, we derive the dynamics of the copper price in PLN, which is not a traded asset but is crucial for the KGHM company risk exposure. A comparative study is performed to demonstrate the effect of including dependencies of the assets and the implications of the regime change. Since for various international companies, risk factors are given rather in the national than the market currency, the approach is universal and can be used in different market contexts, like mining or oil companies, but also other commodities involved in the global trading system.
    Date: 2021–07
  9. By: Dong-Young Lim
    Abstract: This paper proposes a flexible and analytically tractable class of frequency-severity models based on neural networks to parsimoniously capture important empirical observations. In the proposed two-part model, mean functions of frequency and severity distributions are characterized by neural networks to incorporate the non-linearity of input variables. Furthermore, it is assumed that the mean function of the severity distribution is an affine function of the frequency variable to account for a potential linkage between frequency and severity. We provide explicit closed-form formulas for the mean and variance of the aggregate loss within our modelling framework. Components of the proposed model including parameters of neural networks and distribution parameters can be estimated by minimizing the associated negative log-likelihood functionals with neural network architectures. Furthermore, we leverage the Shapely value and recent developments in machine learning to interpret the outputs of the model. Applications to a synthetic dataset and insurance claims data illustrate that our method outperforms the existing methods in terms of interpretability and predictive accuracy.
    Date: 2021–06
  10. By: Ozili, Peterson K
    Abstract: Basel III is a framework to preserve the stability of the international banking system. Nigeria adopts Basel capital framework for capital regulation in the banking sector. This article is a policy discussion on how to make Basel III work in Nigeria. The significance of Basel III is discussed, and some ideas to consider when implementing Basel III to make it work in Nigeria, are provided. Under Basel III, the Nigerian banking system should expect better capital quality, higher levels of capital, the imposition of minimum liquidity requirement for banks, reduced systemic risk, and a transitional arrangement for transitioning across Basel I and II. This article also emphasizes that (i) there should be enough time for the transition to Basel III in Nigeria, (ii) a combination of micro- and macro- prudential regulations is needed; and (iii) the need to repair the balance sheets of banks, in preparation for Basel III. The study recommends that the Nigerian regulator should enforce strict market discipline and ensure effective supervision under the Basel framework. There should be international cooperation between the domestic bank regulator and bank regulators in other countries. The regulator should have a contingency plan to reassure the public of the safety of their deposits, and there should be emergency liquidity solutions to support the financial system in bad times.
    Keywords: Basel III, Bank Business Models, Bank Performance, Financial Stability, Capital Regulation, Bank Regulation, Nigeria
    JEL: G01 G20 G21 G22 G23 G24 G28 G29
    Date: 2021
  11. By: Matthieu Garcin; Samuel St\'ephan
    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.
    Date: 2021–07
  12. By: Yacine Koucha; Alfredo D. Egidio dos Reis
    Abstract: In this paper, we adapt the classic Cram\'er-Lundberg collective risk theory model to a perturbed model by adding a Wiener process to the compound Poisson process, which can be used to incorporate premium income uncertainty, interest rate fluctuations and changes in the number of policyholders. Our study is part of a Master dissertation, our aim is to make a short overview and present additionally some new approximation methods for the infinite time ruin probabilities for the perturbed risk model. We present four different approximation methods for the perturbed risk model. The first method is based on iterative upper and lower approximations to the maximal aggregate loss distribution. The second method relies on a four-moment exponential De Vylder approximation. The third method is based on the first-order Pad\'e approximation of the Renyi and De Vylder approximations. The last method is the second order Pad\'e-Ramsay approximation. These are generated by fitting one, two, three or four moments of the claim amount distribution, which greatly generalizes the approximations. We test the precision of approximations using a combination of light and heavy tailed distributions for the individual claim amount. We assess the ultimate ruin probability and present numerical results for the exponential, gamma, and mixed exponential claim distributions, demonstrating the high accuracy of these four methods. Analytical and numerical methods are used to highlight the practical implications of our findings.
    Date: 2021–07
  13. By: Andrew Ellul (Indiana University, Office of Financial Research, Centre for Economic Policy Research, Center for Studies of Economics and Finance, European Corporate Governance Institute); Dasol Kim (Office of Financial Research)
    Abstract: We provide evidence on how banks form network connections and endogenous risk-taking in their non-bank counterparty choices in the OTC derivatives markets. We use confidential regulatory data from the Capital Assessment and Stress Testing reports that provide counterparty-level data across a wide range of OTC markets for the most systemically important U.S. banks. We show that banks are more likely toeither establish or maintain a relationship, and increase their exposures within an existing relationship, with non-bank counterparties that are already heavily connected and exposed to other banks. Banks in such densely-connected networks are more likely to connect with riskier counterparties for their most material exposures. The effects are strongest in the case of (non-bank) financial counterparties. These findings suggest moral hazard behavior in counterparty choices. Finally, we demonstrate that these exposures are strongly linked to systemic risk. Overall, the results suggest a network formation process that amplifies risk propagation through non-bank linkages in opaque financial markets.
    Keywords: counterparty risk, financial networks, bank interconnectedness, over-the-counter markets, derivatives
    Date: 2021–07–12
  14. By: Howard Kunreuther; Jason Schupp
    Abstract: COVID-19 has demonstrated the challenges that policymakers, insurers, businesses, and employees face when disaster assistance programs are developed after the pandemic has already started. There is now an opportunity to design and implement effective and efficient solutions to manage the financial risks of a future pandemic. This paper suggests a practical framework, informed by the recent experience with COVID-19, for defining a meaningful role for insurance in managing business interruption (BI) and other risks from future pandemics. Policymakers, regulators, businesses, and other stakeholders interacting with representatives from the insurance industry can assist in defining its role in providing protection against the financial consequences of future pandemics. This framework, while designed for dealing with future pandemics, may be applied to other catastrophic and systemic risks.
    JEL: D81 D91 G22 H84
    Date: 2021–06
  15. By: Cousin Areski (IRMA - Institut de Recherche Mathématique Avancée - CNRS - Centre National de la Recherche Scientifique - UNISTRA - Université de Strasbourg); Gueye Djibril (IRMA - Institut de Recherche Mathématique Avancée - CNRS - Centre National de la Recherche Scientifique - UNISTRA - Université de Strasbourg)
    Abstract: Implied volatility surface is of crucial interest for risk management and exotic option pricing models. Its construction is usually carried out in accordance with the arbitrage-free principle. This condition leads to shape restrictions on the option prices such as monotonicity with respect to maturities and convexity with respect to strike prices. In this paper, we propose a new arbitrage-free construction method that extends classical spline techniques by additionally allowing for quantification of uncertainty. The proposed method extends the constrained kriging techniques developed in [MB16] and [CMR16] to the context of volatility surface construction. Assuming a Gaussian process prior, the posterior price surface becomes a truncated Gaussian field given shape constraints and market observations. Prices of illiquid instruments can also be incorporated when considered as noisy observations. Starting from a suitable finite-dimensional approximation of the Gaussian process prior, the no-arbitrage condition on the entire input domain is characterized by a finite number of linear inequality constraints. We define the most likely response surface and the most-likely noise values as the solution of a quadratic optimization problem. We use Hamiltonian Monte Carlo technics to simulate the posterior truncated Gaussian surface and build pointwise confidence bands. The Gaussian process hyper-parameters are estimated using maximum likelihood. The method is illustrated on Euro Stoxx 50 option prices by building no-arbitrage volatility surfaces and their corresponding confidence bands.
    Date: 2021–06–29
  16. By: Dupret, Jean-Loup (Université catholique de Louvain, LIDAM/ISBA, Belgium); Hainaut, Donatien (Université catholique de Louvain, LIDAM/ISBA, Belgium)
    Abstract: Affine Volterra processes have gained more and more interest in recent years. In particular, this class of processes generalizes the classical Heston model for which widely-used calibration techniques have long been known, as well as the rough Heston model which has garnered lots of attention from academicians and practitioners since 2014. The aim of this work is therefore to revisit and generalize the constant propotion portfolio insurance (CPPI) under the class of affine Volterra processes. Indeed, existing simulation-based methods for CPPI do not apply easily to affine Volterra processes, in particular when the variance process of the underlying risky asset is non-Markovian in the current variance state (as in the rough Heston model). We instead propose an approach based on the characteristic function of the log-cushion which appears to be more consistent, stable and particularly efficient in the case of affine Volterra processes compared with classical simulation techniques. Using such approach, we describe in this paper several properties of CPPI (moments, density and risk measures), which naturally result from the form of the log-cushion’s characteristic function under affine Volterra processes. This allows to consider different behaviors and more complex dynamics for the underlying risky asset in the context of CPPI and hence build portfolio strategies that are extremely tractable and consistent with financial data.
    Keywords: Finance ; Portfolio insurance ; CPPI ; Volterra processes ; Rough volatility
    Date: 2021–01–01
  17. By: Kyle Dempsey; Felicia Ionescu
    Abstract: Using administrative data from Y-14M and Equifax, we find evidence for large spreads in excess of those implied by default risk in the U.S. unsecured credit market. These borrowing premia vary widely by borrower risk and imply a nearly flat relationship between loan prices and repayment probabilities, at odds with existing theories. To close this gap, we incorporate supply frictions – a tractably specified form of lending standards – into a model of unsecured credit with aggregate shocks. Our model matches the empirical incidence of both risk and borrowing premia. Both the level and incidence of borrowing premia shape individual and aggregate outcomes. Our baseline model with empirically consistent borrowing premia features 45% less total credit balances and 30% more default than a model with no such premia. In terms of dynamics, we estimate that lending standards were unchanged for low risk borrowers but tightened for high risk borrowers at the outset of Covid-19. Borrowing premia imply a smaller increase in credit usage in response to a negative shock, which this tightening reduced further. Since spreads on loans of all risk levels are countercyclical, all consumers use less unsecured credit for insurance over the cycle, leading to 60% higher relative consumption volatility than in a model with no borrowing premia.
    Keywords: Bankruptcy; Borrowing premia; Consumer credit; Business cycles
    JEL: E21 E32 E44 E51 G12 G21 G22
    Date: 2021–06–24
  18. By: Can, S.U.; Einmahl, John (Tilburg University, School of Economics and Management); Laeven, Roger
    Date: 2021
  19. By: Can, S.U.; Einmahl, John (Tilburg University, Center For Economic Research); Laeven, Roger
    Keywords: Tail dependence; Tail copula; two-sample testing; financial crisis; distribution-free testing; martingale transformation
    Date: 2021
  20. By: Anastasiya Ivanova (National Bank of Ukraine); Alona Shmygel (National Bank of Ukraine); Ihor Lubchuk (National Bank of Ukraine)
    Abstract: Using data for the Ukrainian economy, we applied and adapted the growth-at-risk (GaR) framework to examine the association between financial conditions, credit and sectors' activity, and external conditions and the probability distribution of GDP growth in Ukraine. We applied CSA and PCA approaches to construct indices of these partitions. We further derived GDP growth distributions and explored their behavior under different scenarios. Results from the model with PCA indices suggest that the relationships between financial conditions as well as external conditions indices and economic activity are inverse regardless of quantile of GDP distribution. Moreover, we found that the financial conditions index has the largest effect on the GDP growth on the lower quantiles, which could generate significant downside risk to the economy.
    Keywords: Quantile regression; economic growth; GDP; principal component analysis; GDP growth distribution
    JEL: C31 C53 E17
    Date: 2021–07–01
  21. By: Mathias S. Kruttli; Phillip J. Monin; Lubomir Petrasek; Sumudu W. Watugala
    Abstract: Hedge fund gross U.S. Treasury (UST) exposures doubled from 2018 to February 2020 to $2.4 trillion, primarily driven by relative value arbitrage trading and supported by corresponding increases in repo borrowing. In March 2020, amid unprecedented UST market turmoil, the average UST trading hedge fund had a return of -7% and reduced its UST exposure by close to 20%, despite relatively unchanged bilateral repo volumes and haircuts. Analyzing hedge fund-creditor borrowing data, we find the large, more regulated dealers provided disproportionately more funding during the crisis than other creditors. Overall, the step back in hedge fund UST activity was primarily driven by fund-specific liquidity management rather than dealer regulatory constraints. Hedge funds exited the turmoil with 20% higher cash holdings and smaller, more liquid portfolios, despite low contemporaneous outflows. This precautionary flight to cash was more pronounced among funds exposed to greater redemption risk through shorter share restrictions. Hedge funds predominantly trading the cash-futures basis faced greater margin pressure and reduced UST exposures and repo borrowing the most. After the market turmoil subsided following Fed intervention, hedge fund returns recovered quickly, but UST exposures did not revert to pre-shock levels over the subsequent months.
    Keywords: Hedge funds; Treasury markets; Relative value; Arbitrage; Liquidity; Redemption risk; Creditor constraints
    JEL: G11 G23 G24 G01
    Date: 2021–06–24
  22. By: Russo, Marianna; Kraft, Emil; Bertsch, Valentin; Keles, Dogan
    Abstract: Electricity retailers face increasing uncertainty due to the ongoing expansion of unpredictable, distributed generation in the residential sector. We analyze how increasing levels of households' solar PV self-generation affect the short-term decisionmaking and associated risk exposure of electricity retailers in day-ahead and intraday markets. First, we develop a stochastic model accounting for correlations between solar load, residual load and price in sequentially nested wholesale spot markets across seasons and type of day. Second, we develop a computationally tractable twostage stochastic mixed-integer optimization model to investigate the trading portfolio and risk optimization problem faced by retailers. Through conditional value-at-risk we assess retailers' profitability and risk exposure to different levels of PV self-generation by assuming different retail tariff schemes. We find risk-hedging trading strategies and tariffs to have greater impact in Summer and with low levels of residual load in the system, i.e. when the solar generation uncertainty affect more the households demand to be served and the wholesale spot prices. The study is innovative in unveiling the potential of dynamic electricity tariffs, which are indexed to spot prices, to sustain a high penetration of renewable energy source while promoting risk sharing between customer and retailer. Our findings have implications for electricity retailers facing load and revenue risks in wholesale spot markets, likewise for regulators and policy-makers interested in electricity market design.
    Keywords: Electricity markets,Stochastic model,Stochastic programming,Retailer uncertainty modeling,Riskmanagement
    JEL: C10 C50 G10 Q42 Q48
    Date: 2021
  23. By: Terri van der Zwan (Erasmus University Rotterdam); Erik Hennink (Ortec Finance); Patrick Tuijp (Ortec Finance)
    Abstract: We find that the outperformance for Fama-French factors compared to macroeconomic factors in terms of fitting the cross-section of expected returns disappears when accounting for horizon effects. In addition, we obtain novel empirical relations between macroeconomic factors and Fama-French factors at longer horizons. To obtain our results, we introduce a general linear multifactor asset pricing methodology that integrates systematic risk measured at different frequencies into a single pricing equation. Our setup allows for a setting where investors with different investment horizons may experience different levels of systematic risk, which could arise from delayed stock price reaction to systematic factor news.
    Keywords: Cross-Section of Stock Returns, Factors, Frequency Decomposition, Horizon Effects, Investment Horizon
    JEL: G12 C58 G11
    Date: 2021–07–04
  24. By: Arthur Charpentier; Lariosse Kouakou; Matthias L\"owe; Philipp Ratz; Franck Vermet
    Abstract: The peer-to-peer (P2P) economy has been growing with the advent of the Internet, with well known brands such as Uber or Airbnb being examples thereof. In the insurance sector the approach is still in its infancy, but some companies have started to explore P2P-based collaborative insurance products (eg. Lemonade in the U.S. or Inspeer in France). The actuarial literature only recently started to consider those risk sharing mechanisms, as in Denuit and Robert (2021) or Feng et al. (2021). In this paper, describe and analyse such a P2P product, with some reciprocal risk sharing contracts. Here, we consider the case where policyholders still have an insurance contract, but the first self-insurance layer, below the deductible, can be shared with friends. We study the impact of the shape of the network (through the distribution of degrees) on the risk reduction. We consider also some optimal setting of the reciprocal commitments, and discuss the introduction of contracts with friends of friends to mitigate some possible drawbacks of having people without enough connections to exchange risks.
    Date: 2021–07
  25. By: Maximilian Blesch (Berlin School of Economics); Philipp Eisenhauer (University of Bonn)
    Abstract: Economists often estimate a subset of their model parameters outside the model and let the decision-makers inside the model treat these point estimates as-if they are correct. This practice ignores model ambiguity, opens the door for misspecification of the decision problem, and leads to post-decision disappointment. We develop a framework to explore, evaluate, and optimize decision rules that explicitly account for the uncertainty in the first step estimation using statistical decision theory. We show how to operationalize our analysis by studying a stochastic dynamic investment model where the decision-makers take ambiguity about the model's transition dynamics directly into account.
    Keywords: decision-making under uncertainty, robust Markov decision process
    JEL: D81 C44 D25
    Date: 2021–07
  26. By: Hainaut, Donatien (Université catholique de Louvain, LIDAM/ISBA, Belgium)
    Abstract: A common approach for pricing insurance contracts consists to represent the insured's health status by a Markov chain. This article extends this framework by observing this chain on a random scale of time, defined as the inverse of an α-stable process. This stochastic clock induces sub-exponential waiting times spent in each state. We first review and extend the properties of this time-change to a conditional filtration at time t > 0. Next we evaluate a general type of insurance contract from inception to expiry.
    Date: 2021–01–01
  27. By: Osadchiy, Maksim
    Abstract: The IRB approach underlies Basel II and Basel III. The approach is based on the Vasicek distribution. The main advantage of the distribution is simplicity and accounting for default correlation. But the distribution substantially underestimates probability of default due to ignoring of premature defaults. Besides, the IRB approach uses the maturity adjustment, which is a kind of a black box, since there is no clear information about the econometric model and calibration of its parameters. If maturity exceeds one year, the IRB formula leads to negativity and even discontinuity of capital in the neighborhood of zero default probability. The paper suggests the Vasicek-Black-Cox (VBC) model, which is constructed to fix drawbacks of the IRB approach. The VBC model is constructed on the base of the Vasicek model and the Black-Cox model. The Vasicek model is a special case of the VBC model, designed to evaluate the default distribution taking into account premature defaults. The VBC model was constructed using the Method of Images, since the firm in the framework of the Black-Cox model is treated as the barrier binary option.
    Keywords: IRB; Vasicek; Merton; Black-Cox; barrier options; default distribution
    JEL: G21 G32 G33
    Date: 2021–07–07
  28. By: Felipe Aldunate; Dirk Jenter; Arthur Korteweg; Peter Koudijs
    Abstract: Does enhanced shareholder liability reduce bank failure? We compare the performance of around 4,200 state-regulated banks of similar size in neighboring U.S. states with different liability regimes during the Great Depression. The distress rate of limited liability banks was 29% higher than that of banks with enhanced liability. Results are robust to a diff-in-diff analysis incorporating nationally-regulated banks (which faced the same regulations everywhere) and are not driven by other differences in state regulations, Fed membership, local characteristics, or differential selection into state-regulated banks. Our results suggest that exposing shareholders to more downside risk can successfully reduce bank failure.
    Keywords: limited liability, bank risk taking, financial crises, Great Depression
    JEL: G21 G28 G32 N22
    Date: 2021
  29. By: Rian Dolphin; Barry Smyth; Yang Xu; Ruihai Dong
    Abstract: Forecasting stock returns is a challenging problem due to the highly stochastic nature of the market and the vast array of factors and events that can influence trading volume and prices. Nevertheless it has proven to be an attractive target for machine learning research because of the potential for even modest levels of prediction accuracy to deliver significant benefits. In this paper, we describe a case-based reasoning approach to predicting stock market returns using only historical pricing data. We argue that one of the impediments for case-based stock prediction has been the lack of a suitable similarity metric when it comes to identifying similar pricing histories as the basis for a future prediction -- traditional Euclidean and correlation based approaches are not effective for a variety of reasons -- and in this regard, a key contribution of this work is the development of a novel similarity metric for comparing historical pricing data. We demonstrate the benefits of this metric and the case-based approach in a real-world application in comparison to a variety of conventional benchmarks.
    Date: 2021–07
  30. By: Knut Anton Mork (Department of Economics, Norwegian University of Science and Technology); Vegard Skonseng Bjerketvedt (Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology)
    Abstract: Models of habit formation in consumption typically specify utility over the excess of consumption above some habit level. This specification is unsatisfactory in settings where agents occasionally have to tolerate consumption below the habit level. More importantly, they often imply infeasible solutions with realistically low riskless rates. We propose an alternative specification, where the curvature of the utility function rises steeply for consumption below the habit level, but without utility falling abruptly to minus infinity. We explore analytically the key features of the implied behavior and present representative numerical solutions of the model in continuous time. We then simulate investor-consumer behavior with these preferences and compare this behavior to simpler rules of thumb. We find that soft habits, like hard habits, imply procyclical risk taking. Soft habits also allow some smoothing, especially in the downward direction. However, its most distinguishing feature takes the form of deliberate efforts to build sufficient capital to limit the probability of consumption having to fall below habits. Because the priority given to the buildup of wealth, the question of smoothing remains mostly moot in practice. The simpler rules of thumb tend to smooth more and save less that the base case and thus lead to insufficient buildup of capital over time. Of the simpler rules, the relatively best result is found for behavior as if preferences were CRRA with risk aversion somewhere between the soft-habit risk aversion for consumption above and below the habit level.
    Keywords: Habit formation; Withdrawal smoothing; Risk taking; Long-term fund-preservation
    JEL: C63 E21 G11
    Date: 2021–06–29
  31. By: Sebastian Barnes; Robert Hillman; George Wharf; Duncan MacDonald
    Abstract: Covid-19 and the associated restrictions on interaction have led to an unprecedented shock to activity and firms’ balance sheets. To assess the impact, this paper applies a new large-scale firm-level simulation model calibrated to the United Kingdom (UK). The paper specifically examines the Coronavirus Job Retention Scheme (CJRS) furlough program and a credit guarantee.The Corporate Sector Agent-Based (CAB) Model (Hillman, Barnes, Wharf and MacDonald, 2021) takes into account: heterogeneity across firms; interactions between firms across a realistic customer-supplier network; and rule-of-thumb behaviour by firms and bankruptcy constraints. The model amplifies the effect of shocks and generates substantial persistence and overshooting, as well as displaying a number of non-linearities. The CAB uses a data-rich approach based on ORBIS firm-level data and the OECD Input-Output tables. Simulations in this paper are calibrated to the observed path of UK output in 2020.
    Keywords: agent-based modelling, bankruptcy, Covid-19, credit guarantees, financial stability, firm dynamics, firm-level data, input-output analysis, network analysis, short-time working schemes
    JEL: D21 D22 D57 D85 E27 G33
    Date: 2021–07–13

General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.