nep-rmg New Economics Papers
on Risk Management
Issue of 2021‒04‒05
25 papers chosen by
Stan Miles
Thompson Rivers University

  1. Efficient Computation of Portfolio Credit Risk with Chain Default By Ikeda, Yuki
  2. Countercyclical capital requirement reductions, state dependence and macroeconomic outcomes By Elif C. Arbatli-Saxegaard; Ragnar E. Juelsrud
  3. Opacity and risk-taking: Evidence from Norway By Jin Cao; Ragnar E. Juelsrud
  4. Valuing Exotic Options and Estimating Model Risk By Jay Cao; Jacky Chen; John Hull; Zissis Poulos
  5. On Capital Allocation for a Risk Measure Derived from Ruin Theory By Guusje Delsing; Michel Mandjes; Peter Spreij; Erik Winands
  6. Granular credit risk By Sigurd Galaasen; Rustam Jamilov; Hélène Rey; Ragnar Juelsrud
  7. Cyclical Patterns of Systemic Risk Metrics: Cross-Country Analysis By Plamen K Iossifov; Tomas Dutra Schmidt
  8. Unobserved components models with stochastic volatility for extracting trends and cycles in credit By O'Brien, Martin; Velasco, Sofia
  9. Foreign Exchange Intervention Rules for Central Banks: A Risk-based Framework By Romain Lafarguette; Romain M Veyrune
  10. Risk aggregation and capital allocation using a new generalized Archimedean copula By Fouad Marri; Khouzeima Moutanabbir
  11. Geopolitical Risk and Forecastability of Tail Risk in the Oil Market: Evidence from Over a Century of Monthly Data By Afees A. Salisu; Christian Pierdzioch; Rangan Gupta
  12. Analysis and management of credit risk in Morocco By Yousra El Hajel; Abdenbi El Marzouki; Hassane Zouiri
  13. Deep Hedging: Learning Risk-Neutral Implied Volatility Dynamics By Hans Buehler; Phillip Murray; Mikko S. Pakkanen; Ben Wood
  14. Limit Theorems for Default Contagion and Systemic Risk By Hamed Amini; Zhongyuan Cao; Agnes Sulem
  15. A survey of electricity spot and futures price models for risk management applications By Thomas Deschatre; Olivier F\'eron; Pierre Gruet
  16. Financial Conditions and Downside Risk to Economic Activity in Australia By Luke Hartigan; Michelle Wright
  17. Robustifying Conditional Portfolio Decisions via Optimal Transport By Viet Anh Nguyen; Fan Zhang; Jose Blanchet; Erick Delage; Yinyu Ye
  18. Optimal capital adequacy ratio: an investigation of Vietnamese commercial banks using two-stage DEA By Phuong Anh Nguyen; Bich Le Tran; Michel Simioni
  19. Global Financial Cycle and the Predictability of Oil Market Volatility: Evidence from a GARCH-MIDAS Model By Afees A. Salisu; Rangan Gupta; Riza Demirer
  20. Modeling of crisis periods in stock markets By Apostolos Chalkis; Emmanouil Christoforou; Theodore Dalamagkas; Ioannis Z. Emiris
  21. Regulatory and Bailout Decisions in a Banking Union By Andreas Haufler
  22. Concept of peer-to-peer lending and application of machine learning in credit scoring By Aleksy Klimowicz; Krzysztof Spirzewski
  23. Shape Constrained Kernel PDF and PMF Estimation By Pang Du; Christopher F. Parmeter; Jeffrey S. Racine
  24. Deep Hedging of Derivatives Using Reinforcement Learning By Jay Cao; Jacky Chen; John Hull; Zissis Poulos
  25. Alternative EU CAP Tools for Stabilising Farm Incomes in the Era of Climate Change By Ole Boysen; Kirsten Boysen-Urban; Alan Matthews

  1. By: Ikeda, Yuki
    Abstract: Many banks consider the chain default or bankruptcy when they compute the credit loss distribution. One way to consider the chain default is the good-old Monte Carlo simulation, however, it is typically time-consuming. In this paper, we extend the efficient Monte Carlo simulation using the importance sampling introduced by Glasserman and Li (2005) to realize an efficient Monte Carlo simulation of the Value at Risk (VaR) that allows the chain defaults. In addition, we see that another method, the saddle point approximation, can also be modified for the case of the chain defaults. Moreover, we give a simple method of shifting the means of the multivariate factors using the well-known EM-algorithm to further reduce the variance of the simulated VaR. Simulation studies show that these proposed methods have superior numerical performance.
    Keywords: Value-at-risk; Risk contributions; Importance sampling; Saddle point approximation; EM-algorithm
    JEL: C58 C63
    Date: 2021–03–16
  2. By: Elif C. Arbatli-Saxegaard; Ragnar E. Juelsrud
    Abstract: We use bank-, loan- and firm-level data together with a quasi-natural experiment to estimate the impact of capital requirement reductions on bank lending and real economic outcomes. We find that capital requirement reductions increase lending both to households and firms at the bank- and loan-level, and that the increased lending to firms translates into higher capital investment at the firm-level. Furthermore, the transmission of lower capital requirements to the real economy has a "double state-dependence". The first state-dependence relates to the characteristics of banks. Specifically, the transmission of lower capital requirements to lending is stronger for banks with lower capital ratios. We interpret this result as capital requirement reductions having a larger effect when they are more binding. The second state-dependence relates to the characteristics of the corporate sector. Specifically, the transmission of lower capital requirements to real economic outcomes - via bank lending - is weaker for firms with higher default risk or more leverage, suggesting that capital requirement reductions is most effective in terms of boosting real economic outcomes when firms are financially sound.
    Keywords: banking, capital requirements, macroprudential regulation
    JEL: E51 G21 G28
    Date: 2020–08
  3. By: Jin Cao; Ragnar E. Juelsrud
    Abstract: This paper investigates how balance sheet opacity affects banks' risk-taking behavior. We measure bank balance sheet opacity according to two metrics: the ratio of available-for-sale (AFS) securities and the ratio of off-balance sheet items. We show that balance sheet opacity is positively correlated with realized bank risk. Specifically, banks with more AFS securities have lower realized risk, while banks with more off-balance sheet items have higher realized risk. The correlation between opacity and risk depends on both macroeconomic variables and bank characteristics. The positive relationship between bank opacity and bank risk is weaker for better capitalized banks and banks that are subject to more market discipline. The relationship is also weaker during periods of favorable market conditions. Motivated by this analysis, we then investigate how regulation affects bank opacity. We show that higher capital requirements reduce bank opacity and bank risk through a portfolio rebalancing channel.
    Keywords: opacity, transparency, available-for-sale securities, off-balance sheet items, risktaking
    JEL: G21 G23 G28
    Date: 2020–10–07
  4. By: Jay Cao; Jacky Chen; John Hull; Zissis Poulos
    Abstract: A common approach to valuing exotic options involves choosing a model and then determining its parameters to fit the volatility surface as closely as possible. We refer to this as the model calibration approach (MCA). This paper considers an alternative approach where the points on the volatility surface are features input to a neural network. We refer to this as the volatility feature approach (VFA). We conduct experiments showing that VFA can be expected to outperform MCA for the volatility surfaces encountered in practice. Once the upfront computational time has been invested in developing the neural network, the valuation of exotic options using VFA is very fast. VFA is a useful tool for the estimation of model risk. We illustrate this using S&P 500 data for the 2001 to 2019 period.
    Date: 2021–03
  5. By: Guusje Delsing; Michel Mandjes; Peter Spreij; Erik Winands
    Abstract: This paper addresses allocation methodologies for a risk measure inherited from ruin theory. Specifically, we consider a dynamic value-at-risk (VaR) measure defined as the smallest initial capital needed to ensure that the ultimate ruin probability is less than a given threshold. We introduce an intuitively appealing, novel allocation method, with a focus on its application to capital reserves which are determined through the dynamic value-at-risk (VaR) measure. Various desirable properties of the presented approach are derived including a limit result when considering a large time horizon and the comparison with the frequently used gradient allocation method. In passing, we introduce a second allocation method and discuss its relation to the other allocation approaches. A number of examples illustrate the applicability and performance of the allocation approaches.
    Date: 2021–03
  6. By: Sigurd Galaasen; Rustam Jamilov; Hélène Rey; Ragnar Juelsrud
    Abstract: What is the impact of granular credit risk on banks and on the economy? We provide the ?rst causal identi?cation of single-name counterparty exposure risk in bank portfolios by applying a new empirical approach on an administrative matched bank-?rm dataset from Norway. Exploiting the fat tail properties of the loan share distribution we use a Gabaix and Koijen (2020a,b) granular instrumental variable strategy to show that idiosyncratic borrower risk survives aggregation in banks portfolios. We also ?nd that this granular credit risk spills over from affected banks to ?rms, decreases investment, and increases the probability of default of non-granular borrowers, thereby sizably affecting the macroeconomy.
    Keywords: granular credit risk, credit concentration, granular borrowers, large exposures regulation, granular instrumental variable, granular hypothesis
    Date: 2020–10–15
  7. By: Plamen K Iossifov; Tomas Dutra Schmidt
    Abstract: We analyze a range of macrofinancial indicators to extract signals about cyclical systemic risk across 107 economies over 1995–2020. We construct composite indices of underlying liquidity, solvency and mispricing risks and analyze their patterns over the financial cycle. We find that liquidity and solvency risk indicators tend to be counter-cyclical, whereas mispricing risk ones are procyclical, and they all lead the credit cycle. Our results lend support to high-level accounts that risks were underestimated by stress indicators in the run-up to the 2008 global financial crisis. The policy implications of conflicting risk signals would depend on the phase of the credit cycle.
    Keywords: Credit cycles;Liquidity risk;Solvency;Systemic risk;Private debt;credit cycle.,WP,risk metrics,risk index,risk indices,solvency risk,interest rate,mispricing risk
    Date: 2021–02–05
  8. By: O'Brien, Martin (Central Bank of Ireland); Velasco, Sofia (Central Bank of Ireland)
    Abstract: This paper develops a multivariate filter based on an unobserved component trend-cycle model. It incorporates stochastic volatility and relies on specific formulations for the cycle component. We test the performance of this algorithm within a Monte-Carlo experiment and apply this decomposition tool to study the evolution of the financial cycle (estimated as the cycle of the credit-to-GDP ratio) for the United States, the United Kingdom and Ireland. We compare our credit cycle measure to the Basel III credit-to- GDP gap, prominent for its role informing the setting of countercyclical capital buffers. The Basel-gap employs the Hodrick-Prescott filter for trend extraction. Filtering methods reliant on similar-duration assumptions suffer from endpoint-bias or spurious cycles. These shortcomings might bias the shape of the credit cycle and thereby limit the precision of the policy assessment reliant on its evolution to target financial distress. Allowing for a flexible law of motion of the variance covariance matrix and informing the estimation of the cycle via economic fundamentalsweare able to improve the statistical properties and to find a more economically meaningful measure of the build-up of cyclical systemic risks. Additionally, we find a large heterogeneity in the drivers of the credit cycles across time and countries. This result stresses the relevance in macro prudential policy of considering flexible approaches that can be tailored to country characteristics in contrast to standardized indicators.
    Keywords: Credit imbalances, cyclical systemic risk, financial cycle, macroprudential analysis, multivariate unobserved-components models, stochastic volatility .
    JEL: C32 E32 E58 G01 G28
    Date: 2020–12
  9. By: Romain Lafarguette; Romain M Veyrune
    Abstract: This paper presents a rule for foreign exchange interventions (FXI), designed to preserve financial stability in floating exchange rate arrangements. The FXI rule addresses a market failure: the absence of hedging solution for tail exchange rate risk in the market (i.e. high volatility). Market impairment or overshoot of exchange rate between two equilibria could generate high volatility and threaten financial stability due to unhedged exposure to exchange rate risk in the economy. The rule uses the concept of Value at Risk (VaR) to define FXI triggers. While it provides to the market a hedge against tail risk, the rule allows the exchange rate to smoothly adjust to new equilibria. In addition, the rule is budget neutral over the medium term, encourages a prudent risk management in the market, and is more resilient to speculative attacks than other rules, such as fixed-volatility rules. The empirical methodology is backtested on Banco Mexico’s FXIs data between 2008 and 2016.
    Keywords: Exchange rates;Vector autoregression;Exchange rate risk;Foreign exchange;Currency markets;Foreign Exchange Interventions,Value at Risk,GARCH,WP,var FX intervention rule,intervention region,central bank intervention frequency,FXI risk mitigation,market participant
    Date: 2021–02–12
  10. By: Fouad Marri; Khouzeima Moutanabbir
    Abstract: In this paper, we address risk aggregation and capital allocation problems in the presence of dependence between risks. The dependence structure is defined by a mixed Bernstein copula which represents a generalization of the well-known Archimedean copulas. Using this new copula, the probability density function and the cumulative distribution function of the aggregate risk are obtained. Then, closed-form expressions for basic risk measures, such as tail value-at-risk(TVaR) and TVaR-based allocations, are derived.
    Date: 2021–03
  11. By: Afees A. Salisu (Centre for Econometric and Allied Research, University of Ibadan, Ibadan, Nigeria); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield, 0028, South Africa)
    Abstract: Using monthly data for the period from 1916 to 2020, we report that geopolitical risk, when decomposed into threats and actual risk, has predictive value for tail risk in the oil market. When we study the full sample of data, we find that threats increase tail risk in the oil market, while actual acts related risk reduces tail risk at longer forecast horizons. While the findings of the full-sample analysis show that the effect of threats and acts on tail risk in the oil market is quantitatively small, results of an out-of-sample analysis show that, for several model configurations, geopolitical risks associated with threats are statistically significant predictors of tail risk in the oil market, even after controlling for a factor capturing global equity-market tail-risk spillovers. Our results have important investment implications.
    Keywords: Oil price, Tail risks, Geopolitical risks, Forecasting
    JEL: C22 C32 C53 G15 Q02
    Date: 2021–03
  12. By: Yousra El Hajel (Université Mohammed V); Abdenbi El Marzouki (Université Mohammed V); Hassane Zouiri (Université Mohammed V)
    Abstract: Risk is inherent in all human activity, especially when doing business. In banking, risk is an element that we experience on a daily basis. Indeed, the bank's main activity being to distribute credit, the risk of non-repayment is omnipresent. Credit is a recurring operation, especially in our environment where liquidity is almost non-existent among customers (companies, individuals). Indeed, they always have needs to satisfy such as financing their operations, their consumption, payment of salaries and taxes, etc.. Therefore, the bank-client relationship is better expressed in the facilities, which is why the bank must set limits to counteract the excesses and defaults that may occur during the relationship. In general, the main default borne by the bank is the credit risk that it must circumscribe by a good definition and a good analysis in order to have a correct measurement when it lends to such or such customer (individual or company). Thus, the purpose of this paper is to analyze the evolution of credit risk management in Morocco. It is, first of all, to make a credit progression in a fragile banking system in Morocco, then, The management of credit risk in Morocco and the conclusion
    Abstract: Le risque est inhérent à toute activité humaine, notamment lorsqu'on fait des affaires. Dans les métiers de la banque, le risque est un élément que l'on vit au quotidien. En effet l'activité principale de la banque étant de distribuer du crédit, le risque de non remboursement est omniprésent. Le crédit est une opération récurrente surtout dans notre environnement ou la liquidité est presque chose inexistante chez les clients (entreprise, particulier). En effet, ceux-ci ont toujours des besoins à satisfaire comme le financement de leur exploitation ; de leur consommation le paiement des salaires et impôts ; etc. Par conséquent la relation banque client s'exprime mieux dans les facilités c'est pourquoi la banque doit fixer des limites pour contrecarrer les excès et le défaut pouvant survenir durant la relation. En général, le principal défaut supporté par la banque est le risque de crédit qu'il doit circonscrire par une bonne définition et une bonne analyse à fin d'en avoir une mesure assez correcte lorsqu'elle prête à tel ou tel client (particulier ou entreprise) Ainsi, l'objet de ce papier est d'analyser L'évolution de la gestion du risque de crédit au Maroc. Il s'agit, tout d'abord, de faire une progression de crédit dans un système bancaire fragile au Maroc, ensuite, La gestion du risque crédit au Maroc et la conclusion
    Keywords: Risk Management,Credit Risk,Compliance with Prudential Regulations,Moroccan Banks
    Date: 2020–10–22
  13. By: Hans Buehler; Phillip Murray; Mikko S. Pakkanen; Ben Wood
    Abstract: We present a numerically efficient approach for learning a risk-neutral measure for paths of simulated spot and option prices up to a finite horizon under convex transaction costs and convex trading constraints. This approach can then be used to implement a stochastic implied volatility model in the following two steps: 1. Train a market simulator for option prices, as discussed for example in our recent; 2. Find a risk-neutral density, specifically the minimal entropy martingale measure. The resulting model can be used for risk-neutral pricing, or for Deep Hedging in the case of transaction costs or trading constraints. To motivate the proposed approach, we also show that market dynamics are free from "statistical arbitrage" in the absence of transaction costs if and only if they follow a risk-neutral measure. We additionally provide a more general characterization in the presence of convex transaction costs and trading constraints. These results can be seen as an analogue of the fundamental theorem of asset pricing for statistical arbitrage under trading frictions and are of independent interest.
    Date: 2021–03
  14. By: Hamed Amini; Zhongyuan Cao; Agnes Sulem
    Abstract: We consider a general tractable model for default contagion and systemic risk in a heterogeneous financial network, subject to an exogenous macroeconomic shock. We show that, under some regularity assumptions, the default cascade model could be transferred to a death process problem represented by balls-and-bins model. We also reduce the dimension of the problem by classifying banks according to different types, in an appropriate type space. These types may be calibrated to real-world data by using machine learning techniques. We then state various limit theorems regarding the final size of default cascade over different types. In particular, under suitable assumptions on the degree and threshold distributions, we show that the final size of default cascade has asymptotically Gaussian fluctuations. We next state limit theorems for different system-wide wealth aggregation functions and show how the systemic risk measure, in a given stress test scenario, could be related to the structure and heterogeneity of financial networks. We finally show how these results could be used by a social planner to optimally target interventions during a financial crisis, with a budget constraint and under partial information of the financial network.
    Date: 2021–04
  15. By: Thomas Deschatre; Olivier F\'eron; Pierre Gruet
    Abstract: This review presents the set of electricity price models proposed in the literature since the opening of power markets. We focus on price models applied to financial pricing and risk management. We classify these models according to their ability to represent the random behavior of prices and some of their characteristics. In particular, this classification helps users to choose among the most suitable models for their risk management problems.
    Date: 2021–03
  16. By: Luke Hartigan (Reserve Bank of Australia); Michelle Wright (Reserve Bank of Australia)
    Abstract: We apply the growth-at-risk framework to the Australian economy. This allows us to estimate how important current financial conditions are in explaining future downside risk to key macroeconomic variables. As such, it provides a way to quantify the economic costs of financial instability. In order to implement this framework, we develop a new financial conditions index for Australia and show that it correlates closely with previous episodes of financial instability. We find that more restrictive financial conditions play an important role in explaining downside risk to growth in both GDP and employment and upside risk to changes in the unemployment rate. Our measure of financial conditions is, however, less useful for explaining risks to growth in household consumption and business investment. Overall, the framework provides a useful characterisation of the relationship between financial stability and economic activity in Australia.
    Keywords: downside risk; dynamic factor model; financial conditions; quantile regression
    JEL: C32 C53 C55 E27 E32 E44
    Date: 2021–03
  17. By: Viet Anh Nguyen; Fan Zhang; Jose Blanchet; Erick Delage; Yinyu Ye
    Abstract: We propose a data-driven portfolio selection model that integrates side information, conditional estimation and robustness using the framework of distributionally robust optimization. Conditioning on the observed side information, the portfolio manager solves an allocation problem that minimizes the worst-case conditional risk-return trade-off, subject to all possible perturbations of the covariate-return probability distribution in an optimal transport ambiguity set. Despite the non-linearity of the objective function in the probability measure, we show that the distributionally robust portfolio allocation with side information problem can be reformulated as a finite-dimensional optimization problem. If portfolio decisions are made based on either the mean-variance or the mean-Conditional Value-at-Risk criterion, the resulting reformulation can be further simplified to second-order or semi-definite cone programs. Empirical studies in the US and Chinese equity markets demonstrate the advantage of our integrative framework against other benchmarks.
    Date: 2021–03
  18. By: Phuong Anh Nguyen (VNU-HCM - Vietnam National University - Ho Chi Minh City); Bich Le Tran (VNU-HCM - Vietnam National University - Ho Chi Minh City); Michel Simioni (UMR MoISA - Montpellier Interdisciplinary center on Sustainable Agri-food systems (Social and nutritional sciences) - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - CIHEAM-IAMM - Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier - CIHEAM - Centre International de Hautes Études Agronomiques Méditerranéennes - Montpellier SupAgro - Institut national d’études supérieures agronomiques de Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)
    Abstract: Over the last years the Vietnamese banking system has been struggling to restructure, reform governance, consolidate financial statements and build up merge and acquisition, in line with international standards. The Bank for International Settlements (BIS) proposed BASEL III in 2010, whereby banks must increase their minimum Capital Adequacy Ratios (CAR) year by year with a goal of 10.5% by 2019. The objective of this paper is to address the questions: (1) what are the optimal CAR levels for Vietnamese Commercial Banks (2) whether the minimum required CARs stipulated in the Basel II and III are reasonable for Vietnam banking system? The data set consists of a sample of Vietnamese commercial banks over the six-year period from 2010 to 2015. The optimal CARs of banks are calculated using the nonparametric two-stage Data Envelopment Analysis (DEA) model, with two inputs: fixed assets, employee expense and two outputs: interest income, non-interest income. The findings indicate that 92.4% of the banks have the optimal CAR higher than the minimum ratio 10.5% defined in BASEL III. Moreover, 57.98% of the banks should raise their current level of CAR to reach their optimal ones. To conclude, this paper will provide a guideline for Vietnamese banks to decide their optimal CAR to reach the efficiency frontier.
    Keywords: Vietnam banking system,Two-stage DEA,BASEL II,BASEL III,Capital adequacy ratios
    Date: 2021–01–01
  19. By: Afees A. Salisu (Centre for Econometric and Allied Research, University of Ibadan, Ibadan, Nigeria); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield, 0028, South Africa); Riza Demirer (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA)
    Abstract: This study examines the predictive power of the global financial cycle (GFCy) over oil market volatility using the GARCH-MIDAS framework. The GARCH-MIDAS model provides an appropriate setting to forecast high frequency oil market volatility using global predictors that are only available at low frequency. We show that the global financial cycle carries significant predictive information over both oil market volatility proxies, both in- and out-of-sample. The predictive relationship is found to be positive, more strongly during the pre-GFC period, suggesting that rising global asset prices coupled with improved cross-border capital flows are associated with rising volatility in the oil market. While the GARCH-MIDAS model incorporating GFCy or any other proxy of global financial/economic conditions yields economic gains compared to the conventional GARCH-MIDAS-RV specification, especially in the pre-GFC period; the stance is found to be robust to risk aversion and leverage ratio. The economic gains observed from the GFCy-based model particularly during the pre-GFC period when world markets experienced a steady rise in global asset prices and cross-border capital flows underline the potential role of risk appetite (or behavioural factors) in forecasting applications. Overall, our results suggest that incorporating low frequency proxies of global asset market conditions can provide significant forecasting gains for energy market models, with significant implications for both investors and policymakers.
    Keywords: Global Financial Cycle, Oil Volatility, Predictability, MIDAS models
    JEL: C32 C53 G15 Q02
    Date: 2021–03
  20. By: Apostolos Chalkis; Emmanouil Christoforou; Theodore Dalamagkas; Ioannis Z. Emiris
    Abstract: We exploit a recent computational framework to model and detect financial crises in stock markets, as well as shock events in cryptocurrency markets, which are characterized by a sudden or severe drop in prices. Our method manages to detect all past crises in the French industrial stock market starting with the crash of 1929, including financial crises after 1990 (e.g. dot-com bubble burst of 2000, stock market downturn of 2002), and all past crashes in the cryptocurrency market, namely in 2018, and also in 2020 due to covid-19. We leverage copulae clustering, based on the distance between probability distributions, in order to validate the reliability of the framework; we show that clusters contain copulae from similar market states such as normal states, or crises. Moreover, we propose a novel regression model that can detect successfully all past events using less than 10% of the information that the previous framework requires. We train our model by historical data on the industry assets, and we are able to detect all past shock events in the cryptocurrency market. Our tools provide the essential components of our software framework that offers fast and reliable detection, or even prediction, of shock events in stock and cryptocurrency markets of hundreds of assets.
    Date: 2021–03
  21. By: Andreas Haufler
    Abstract: We model a banking union of two countries whose banking sectors differ in their average probability of failure and externalities between the two countries arise from cross-border bank ownership. The two countries face (i) a regulatory decision of which banks are to be shut down before they can go bankrupt, and (ii) a loss allocation – or bailout – decision of who pays for banks that have failed despite regulatory oversight. Each of these choices can either be taken in a centralized or in a decentralized way. In our benchmark model the two countries always agree on a centralized regulation policy. In contrast, bailout policies are centralized only when international spillovers from cross-border bank ownership are strong, and banking sectors are highly profitable.
    Keywords: banking union, bank regulation, bailout policies
    JEL: G28 F33 H87
    Date: 2021
  22. By: Aleksy Klimowicz (Faculty of Economic Sciences, University of Warsaw); Krzysztof Spirzewski (Faculty of Economic Sciences, University of Warsaw)
    Abstract: Numerous applications of AI are found in the banking sector. Starting from front-office, enhancing customer recognition and personalized services, continuing in middle-office with automated fraud-detection systems, ending with back-office and internal processes automatization. In this paper we provide comprehensive information on the phenomenon of peer-to-peer lending in the modern view of alternative finance and crowdfunding from several perspectives. The aim of this research is to explore the phenomenon of peer-to-peer lending market model. We apply and check the suitability and effectiveness of credit scorecards in the marketplace lending along with determining the appropriate cut-off point. We conducted this research by exploring recent studies and open-source data on marketplace lending. The scorecard development is based on the P2P loans open dataset that contains repayments record along with both hard and soft features of each loan. The quantitative part consists of applying a machine learning algorithm in building a credit scorecard, namely logistic regression.
    Keywords: artificial intelligence, peer-to-peer lending, credit risk assessment, credit scorecards, logistic regression, machine learning
    JEL: G21 C25
    Date: 2021
  23. By: Pang Du; Christopher F. Parmeter; Jeffrey S. Racine
    Abstract: We consider shape constrained kernel-based probability density function (PDF) and probability mass function (PMF) estimation. Our approach is of widespread potential applicability and includes, separately or simultaneously, constraints on the PDF (PMF) function itself, its integral (sum), and derivatives (finite-differences) of any order. We also allow for pointwise upper and lower bounds (i.e., inequality constraints) on the PDF and PMF in addition to more popular equality constraints, and the approach handles a range of transformations of the PDF and PMF including, for example, logarithmic transformations (which allows for the imposition of log-concave or log-convex constraints that are popular with practitioners). Theoretical underpinnings for the procedures are provided. A simulation-based comparison of our proposed approach with those obtained using Grenander-type methods is favourable to our approach when the DGP is itself smooth. As far as we know, ours is also the only smooth framework that handles PDFs and PMFs in the presence of inequality bounds, equality constraints, and other popular constraints such as those mentioned above. An implementation in R exists that incorporates constraints such as monotonicity (both increasing and decreasing), convexity and concavity, and log-convexity and log-concavity, among others, while respecting finite-support boundaries via explicit use of boundary kernel functions.
    Keywords: nonparametric; density; restricted estimation
    JEL: C14
    Date: 2021–03
  24. By: Jay Cao; Jacky Chen; John Hull; Zissis Poulos
    Abstract: This paper shows how reinforcement learning can be used to derive optimal hedging strategies for derivatives when there are transaction costs. The paper illustrates the approach by showing the difference between using delta hedging and optimal hedging for a short position in a call option when the objective is to minimize a function equal to the mean hedging cost plus a constant times the standard deviation of the hedging cost. Two situations are considered. In the first, the asset price follows a geometric Brownian motion. In the second, the asset price follows a stochastic volatility process. The paper extends the basic reinforcement learning approach in a number of ways. First, it uses two different Q-functions so that both the expected value of the cost and the expected value of the square of the cost are tracked for different state/action combinations. This approach increases the range of objective functions that can be used. Second, it uses a learning algorithm that allows for continuous state and action space. Third, it compares the accounting P&L approach (where the hedged position is valued at each step) and the cash flow approach (where cash inflows and outflows are used). We find that a hybrid approach involving the use of an accounting P&L approach that incorporates a relatively simple valuation model works well. The valuation model does not have to correspond to the process assumed for the underlying asset price.
    Date: 2021–03
  25. By: Ole Boysen (School of Agriculture & Food Science and Geary Institute for Public Policy, University College Dublin); Kirsten Boysen-Urban (Department of International Agricultural Trade & Food Security, University of Hohenheim); Alan Matthews (Department of Economics, Trinity College Dublin)
    Abstract: No reliable supports protect EU farmers from the catastrophic risks which are expected to increase in frequency and severity due to climate change. We propose three transparent, predictable, and fair safety net policies which operate with indices on the Member State level. Simulations with a tailored global model of a series of historic yield shocks as observed over past decades serve as a test bed to quantify the costs and benefits of these policies in EU Member States using various risk metrics. The results highlight properties of and rankings among these polices useful for guiding future policy design and assessment.
    Keywords: Safety nets, risk management, income stabilisation, climate change, EU Common Agricultural Policy
    JEL: Q18 Q54
    Date: 2021–03–12

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