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on Risk Management |
| By: | Abbas, Yasser; Daouia, Abdelaati; Nemouchi, Boutheina; Stupfler, Gilles |
| Abstract: | Expectiles have received increasing attention as coherent and elicitable market risk measure. Their estimation from heavy-tailed data in an extreme value framework has been studied using solely the Weissman extrapolation method. We challenge this dominance by developing the theory of two classes of semiparametric Generalized Pareto estimators that make more efficient use of tail observations by incorporating the location, scale and shape extreme value parameters: the first class relies on asymmetric least squares estimation, while the second is based on extreme quantile estimation. A comparison with simulated and real data shows the superiority of our proposals for real-valued profit-loss distributions. |
| Keywords: | Expectile, Extreme risk, Generalized Pareto model, Heavy tails, Semiparametric; extrapolation |
| JEL: | C13 C14 C18 C53 C58 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:tse:wpaper:130105 |
| By: | Viral V. Acharya; Heitor Almeida; Yakov Amihud; Ping Liu |
| Abstract: | We investigate how firms manage financial default risk (on debt) and operational default risk (on delivery obligations). Financially constrained firms reduce operational hedging through inventory and supply chain in favor of cash holdings. Our model predicts that firms’ markup increases with financial default risk as they cut operational hedging costs. Empirical analysis confirms this prediction and shows that the markup-credit risk relationship strengthens during adverse aggregate shocks, particularly for firms exposed to lending disruptions. Market power alone cannot explain this relationship, which reflects firms’ strategic adjustments in operational hedging practices. |
| JEL: | G31 G32 G33 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33340 |
| By: | Korobova, Elena; Fantazzini, Dean |
| Abstract: | Stablecoins are a pivotal and debated topic within decentralized finance (DeFi), attracting significant interest from researchers, investors, and crypto-enthusiasts. These digital assets are designed to offer stability in the volatile cryptocurrency market, addressing key challenges in traditional financial systems and DeFi, such as price volatility, transparency, and transaction efficiency. This paper contributes to the existing literature by estimating the credit risk associated with stablecoins, marking the first study to focus exclusively on this market. Our findings reveal that a substantial portion of stablecoins have failed, aligning with existing literature. Using Feder et al.'s (2018) methodology, we observed that 21% of stablecoins were "abandoned" at least once, with only 36% being later "resurrected, " and just 11% maintaining their "resurrected" status. These results support the hypothesis that stablecoins rarely recover once they break their peg, often due to technical issues or loss of user trust. We also found that the time between a statistically significant break in the stablecoin's peg and its subsequent collapse or stabilization averages approximately 10 days. We estimated probabilities of default (PDs) for stablecoins based on market capitalization using various forecasting models. A robustness check further indicated that stablecoins on the Ethereum blockchain are less prone to default, likely due to Ethereum's robust ecosystem and the established presence of older stablecoins. Despite the study's limitations, including a limited dataset of 121 stablecoins and missing market capitalization data, the findings offer practical applications for investors and traders. The techniques and models applied in this research provide tools for evaluating credit risks in the stable-coins market, aiding in portfolio management and investment strategies. |
| Keywords: | stablecoins; crypto-assets; cryptocurrencies; credit risk; default probability; probability of death; ZPP; Cox Proportional Hazards Model. |
| JEL: | C32 C35 C38 C51 C53 G12 G17 G32 G33 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122951 |
| By: | Arnone, Massimo; Costantiello, Alberto; Leogrande, Angelo |
| Abstract: | The paper deals only with the identification of the determinants of total risk exposure amount within the European banking system, while the importance of TREA within Basel III regulatory regimes is focused. The research provides the integration of an econometric investigation with high-end machine learning techniques for the identification of the influential financial variables of TREA. The most relevant financial determinants of TREA were identified as LCR, CRWEA, LA, and OREA. These also reflect complex interdependencies-for instance, the negative value of TREA and LCR would suggest that there were trade-offs made between risk-taking and liquidity management. Thus, the positive relationship with CRWEA, and even more so with derivatives over assets, underlines intrinsic risks from credit exposures and related to financial instruments' complexity. The report further iterates that there should be mechanisms for appropriate risk-weighting, adequate liquidity buffers, and proper operational controls so that the financial system can become significantly more stable and resilient. This work will put forward actionable recommendations to policy makers, regulators, and financial institutions on mitigating systemic vulnerabilities and further optimizing their strategies for compliance in view of an increasingly volatile financial landscape, leveraging from traditional econometric modeling insights with machine learning. |
| Keywords: | Total Risk Exposure Amount, European Banking System, Liquidity Coverage Ratio, Risk Management, Basel III Compliance. |
| JEL: | E58 G21 G28 G32 |
| Date: | 2025–01–05 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:123190 |
| By: | Xavier Gabaix; Ralph S. J. Koijen; Federico Mainardi; Sangmin Simon Oh; Motohiro Yogo |
| Abstract: | We define risk transfer as the percent change in the market risk exposure for a group of investors over a given period. We estimate risk transfer using novel data on U.S. investors' portfolio holdings, flows, and returns at the security level with comprehensive coverage across asset classes and broad coverage across the wealth distribution (including 400 billionaires). Our key finding is that risk transfer is small with a mean absolute value of 0.65% per quarter. Leading macro-finance models with heterogeneous investors predict risk transfer that exceeds our estimate by a factor greater than ten because investors react too much to the time-varying equity premium. Thus, the small risk transfer is a new moment to evaluate macro-finance models. We develop a model with inelastic demand, calibrated to the standard asset pricing moments on realized and expected stock returns, that explains the observed risk transfer. The model is adaptable to other macro-finance applications with heterogeneous households. |
| JEL: | E7 G1 G4 G5 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33336 |
| By: | Stark, Oded (University of Bonn); Balbus, Lukasz (University of Zielona Gora) |
| Abstract: | The purpose of this paper is to provide a general proposition of the relationship between altruism and risk taking. As explained in the body of the paper, we diverge from a result reported in Stark et al. (2022) and provide an expansion and a generalization of a preliminary result reported in Stark (2024). In a broad utility framework, we study the risk aversion of an altruistic person who is an active donor (benefactor) and the risk aversion of a beneficiary of an altruistic transfer. In both cases, we find that altruism lowers risk aversion. The specific case in which the utility functions of the benefactor and of the beneficiary are constant relative risk aversion (CRRA) functions constitutes a vivid example of lesser risk aversion characterization. We conclude that in terms of risk-taking behavior, a "population" endowed with altruism is uniformly more willing to take risks than a comparable "population" devoid of altruism. |
| Keywords: | altruism, altruistic transfers, the absolute risk aversion of a practicing altruistic person, intensity of altruism, variation in risk-taking preferences, the absolute risk aversion of a beneficiary of an altruistic transfer |
| JEL: | D01 D64 D81 G41 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17506 |
| By: | Jean-Gabriel Lauzier; Liyuan Lin; Ruodu Wang |
| Abstract: | We address the problem of sharing risk among agents with preferences modelled by a generalclass of comonotonic additive and law-invariant functionals that need not be either monotoneor convex. Such functionals are called distortion riskmetrics, which include many statisticalmeasures of risk and variability used in portfolio optimization and insurance. The set of Pareto Optimal allocations is characterized under various settings of general or comonotonic risk sharing problems. We solve explicitly Pareto-optimal allocations among agents using the Gini deviation, the mean-median deviation, or the inter-quantile difference as the relevant variability measures. The latter is of particular interest, as optimal allocations are not comonotonic in the presenceof inter-quantile difference agents; instead, the optimal allocation features a mixture of pairwise counter-monotonic structures, showing some patterns of extremal negative dependence. |
| Keywords: | Signed Choquet Integrals; Risk Sharing;Â Inter-Quantile Difference;Â Variability Measures; Pairwise Counter-Monotonicity |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:msh:ebswps:2024-18 |
| By: | Sokhombela, Andiswa Luncedo Lwandile; Bonga-Bonga, Lumengo; Manguzvane, Mathias Mandla |
| Abstract: | This paper investigates the safe-haven characteristics of three assets, namely gold, crude oil and Bitcoin, and their ability to reduce downside risk of different portfolios during two severe financial crises: the 2008 global financial crisis (GFC) and the 2019 Coronavirus pandemic (COVID-2019). We examine the left-tail behaviour of portfolios consisting of 60/40 equity returns and bond yield from six G20 member nations by applying EVT, BMM in the context of portfolio optimisation and examine which selection of safe-haven assets between gold, crude oil and Bitcoin can be amalgamated to the stock/bond mix for an optimal portfolio during crises. The portfolios are from three developed countries: Canada, United States of America (USA) and United Kingdom (UK), while the three emerging countries are Russia, Brazil and South Korea. The sample data is from 2007 to 2009 for the GFC and 2019 to 2023 for COVID-19. The findings of the paper show that during the GFC, the addition of gold and crude oil and the combination of the two allowed the heavy Fréchet-type tails to transform into thin Weibull-type tails. This implies that the two assets acted as safe-haven assets during the crisis and gold being the best safe-haven option for all countries. Contrarily, COVID-19 yielded mixed results, all the assets including the digital cryptocurrency acted as a safe haven for only two emerging countries, namely Russia and Brazil, improving both tail behaviours to Weibull-type tails, with gold and Bitcoin serving safe-haven characteristics for both countries. |
| Keywords: | Safe-haven assets; Global Financial Crisis (GFC); COVID-19; Extreme Value Theory (EVT); block maxima method (BMM) |
| JEL: | C5 G11 G15 |
| Date: | 2024–12–22 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:123066 |
| By: | Aloisio Araujo; Juan Pablo Gama; Timothy J. Kehoe |
| Abstract: | We study the dynamic properties of the wealth distribution in an overlapping generations model with warm-glow bequests and heterogeneous attitudes towards risk. Some dynasties of agents are risk averters, and others are risk lovers. Agents can invest in two types of Lucas trees. The two types of trees are symmetric in the sense that one type has a high return in states where the other has a return of zero. This symmetry allows risk averters to perfectly ensure their future income and eliminates aggregate uncertainty in the model. Furthermore, risk lovers take extreme portfolio positions, which make it easy for us to characterize the evolution of their wealth holdings over time. We show that the model has an equilibrium in which the aggregate wealth distribution converges to a unique invariant distribution. The invariant distribution of wealth of the risk lovers has fat tails for high bequest rates. The existence of fat tails is endogenously generated by the behavior of risk lovers rather than by the exogenous existence of fat tails in the endowments or in the returns of the assets. |
| JEL: | C62 D51 D53 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33298 |
| By: | Alejandro Rodriguez Dominguez; Muhammad Shahzad; Xia Hong |
| Abstract: | A framework for portfolio allocation based on multiple hypotheses prediction using structured ensemble models is presented. Portfolio optimization is formulated as an ensemble learning problem, where each predictor focuses on a specific asset or hypothesis. The portfolio weights are determined by optimizing the ensemble's parameters, using an equal-weighted portfolio as the target, serving as a canonical basis for the hypotheses. Diversity in learning among predictors is parametrically controlled, and their predictions form a structured input for the ensemble optimization model. The proposed methodology establishes a link between this source of learning diversity and portfolio risk diversification, enabling parametric control of portfolio diversification prior to the decision-making process. Moreover, the methodology demonstrates that the diversity in asset or hypothesis selection, based on predictions of future returns, before and independently of the ensemble learning stage, also contributes to the out-of-sample portfolio diversification. The sets of assets with more diverse but lower average return predictions are preferred over less diverse selections. The methodology enables parametric control of diversity in both the asset selection and learning stages, providing users with significant control over out-of-sample portfolio diversification prior to decision-making. Experiments validate the hypotheses across one-step and multi-step decisions for all parameter configurations and the structured model variants using equity portfolios. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.03919 |
| By: | Bradley, Richard |
| Abstract: | Insurers draw on sophisticated models for the probability distributions over losses associated with catastrophic events that are required to price insurance policies. But prevailing pricing methods don’t factor in the ambiguity around model-based projections that derive from the relative paucity of data about extreme events. I argue however that most current theories of decision making under ambiguity only partially support a solution to the challenge that insurance decision makers face and propose an alternative approach that allows for decision making that is responsive to both the evidential situation of the insurance decision maker and their attitude to ambiguity. |
| Keywords: | ambiguity; insurance decision making; reinsurance; natural catastrophes; catastrophe modelling; Ambiguity |
| JEL: | J1 |
| Date: | 2024–09–11 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:122339 |
| By: | Dyakonova, Ludmila; Konstantinov, Alexey |
| Abstract: | The article studies approaches to improving the forecasting quality of machine learning models in finance. An overview of studies devoted to the application of machine learning models and artificial intelligence in the banking sector is given, both from the point of view of risk management and considering in more detail the applied methods of credit scoring and fraud detection. Aspects of applying explainable artificial intelligence (XAI) methods in financial organizations are considered. To identify the most effective machine learning models, the authors conducted experiments to compare 8 classification models used in the financial sector. The gradient boosting model CatboostClassifier was chosen as the base model. A comparison was carried out for the results obtained on the CatboostClassifier model with the characteristics of the other models: IsolationForest, feature ranking model using Recursive Feature Elimination (RFE), XAI Shapley values method, positive class weight increase models wrapper model. All models were applied to 5 open financial data sets. 1 dataset contains transaction data of credit card transactions, 3 datasets contain data on retail lending, and 1 dataset contains data on consumer lending. Our calculations revealed slight improvement for the models IsolationForest and wrapper model in comparison with the base CatboostClassifier model in terms of ROC_AUC for loan defaults data. |
| Keywords: | financial risks, credit scoring, fraud detection, machine learning, explainable artificial intelligence methods, Catboost, SHAP |
| JEL: | C63 |
| Date: | 2024–12–10 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122941 |
| By: | Matthias Fengler; Winfried Koeniger; Stephan Minger |
| Abstract: | We analyze the transmission of monetary policy to the costs of hedging using options order book data. Monetary policy transmits to hedging costs both by changing the relevant state variables, such as the value of the underlying, its volatility and tail risk, and by affecting option market liquidity, including the bid-ask spread and market depth. Our estimates suggest that during the peak of the pandemic crisis in March 2020, monetary policy decisions resulted in substantial changes in hedging costs even within short intraday time windows around the decisions, amounting approximately to the annual expenses of a typical equity mutual fund. |
| Keywords: | liquidity, monetary policy, option order books, option markets, Covid-19 pandemic |
| JEL: | G13 G14 D52 E52 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11556 |
| By: | Jessen, Robin (RWI); König, Johannes (DIW Berlin) |
| Abstract: | We decompose earnings risk into contributions from hours and wage shocks. To distinguish between hours shocks, modeled as innovations to the marginal disutility of work, and labor supply reactions to wage shocks we formulate a life-cycle model of consumption and labor supply. For estimation we use data on married American men from the PSID. Permanent wage shocks explain 31% of total risk, permanent hours shocks 21%. Progressive taxation attenuates cross-sectional earnings risk, but its life-cycle insurance impact is much smaller. At the mean, a one standard deviation hours shock raises life-time income by 11%, a wage shock by 13%. |
| Keywords: | consumption insurance, labor supply, earnings risk, structural estimation, progressive taxation |
| JEL: | D31 J22 J31 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17498 |
| By: | Gaganis, Chrysovalantis; Leledakis, George N.; Pasiouras, Fotios; Pyrgiotakis, Emmanouil G. |
| Abstract: | This study examines the association between the culturally endorsed charismatic leadership style in a society and stock price crash risk. The results reveal a positive and statistically significant association, providing support to the arguments about the dark-side view of charismatic leadership. This finding remains robust to the inclusion of various control variables, instrumental variable estimations that account for endogeneity, the use of sub-samples, and when considering the societal endorsement in the country of origin of the CEO rather than the country of the corporate headquarters. Further analysis reveals that the impact of the charismatic leadership style is channeled through two firm-level managerial actions, namely overinvestment and reporting opacity associated with accruals management. Finally, the results show that the impact of the culturally endorsed charismatic leadership style is moderated by the country-level minority investors’ protection rights and the strength of law and order. |
| Keywords: | Charismatic leadership style; stock price crash risk; CEO; culture; |
| JEL: | G00 G12 G14 G15 M12 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122898 |
| By: | Katsafados, Apostolos G.; Leledakis, George N.; Panagiotou, Nikolaos P.; Pyrgiotakis, Emmanouil G. |
| Abstract: | We combine machine learning algorithms (ML) with textual analysis techniques to forecast bank stock returns. Our textual features are derived from press releases of the Federal Open Market Committee (FOMC). We show that ML models produce more accurate out-of-sample predictions than OLS regressions, and that textual features can be more informative inputs than traditional financial variables. However, we achieve the highest predictive accuracy by training ML models on a combination of both financial variables and textual data. Importantly, portfolios constructed using the predictions of our best performing ML model consistently outperform their benchmarks. Our findings add to the scarce literature on bank return predictability and have important implications for investors. |
| Keywords: | Bank stock prediction; Trading strategies; Machine learning; Press conferences; Natural language processing; Banks |
| JEL: | C53 C88 G00 G11 G12 G14 G17 G21 |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122899 |
| By: | Liu, Xueying (Capital University of Economics and Business, Beijing); Zhao, Zhong (Renmin University of China) |
| Abstract: | This study investigates the impact of social pension insurance on the efficiency of household financial portfolios, utilizing data from the 2019 wave of the China Household Finance Survey. Our findings indicate that social pension insurance significantly enhances the efficiency of household financial portfolios, partly through the channels of risk attitude and precautionary savings. |
| Keywords: | social pension insurance, household portfolios, sharpe ratio, efficiency |
| JEL: | G59 J24 I28 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17492 |
| By: | Luís Martins (Universidade de Coimbra, CeBER, Faculdade de Economia); Diogo Pinheiro (Department of Mathematics, Brooklyn College of the City University of New York, and Department of Mathematics, Graduate Center of the City University of New York); Alberto A. Pinto (LIAAD–INESC TEC, Departmento de Matemática, Faculdade de Ciências, Universidade do Porto,) |
| Abstract: | We employ a duality approach and martingale techniques to characterize the solutions to a stochastic optimal control problem modeling the choices available to an economic agent seeking to maximize the expected utility derived from consumption, terminal wealth, and life insurance coverage, while facing both investment risks and mortality uncertainty. The agent dynamically allocates wealth between a financial market composed of one risk-free asset and multiple risky assets, and term life insurance premiums subject, respectively, to uncertainty associated with the market conditions and the agent uncertain lifespan. Our results provide insights into the trade-offs between consumption, wealth accumulation, and life insurance demand in the presence of financial and mortality risks. |
| Keywords: | Stochastic optimal control; Convex duality; Martingale methods; Consumption-investment problem; Life-insurance.; Stochastic optimal control; Convex duality; Martingale methods; Consumption-investment problem; Life-insurance. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:gmf:papers:2024-05 |
| By: | Wojciech Grabowski; Jakub Janus |
| Abstract: | This study investigates the impact of the Russo-Ukrainian war on stock market connectedness in 24 European economies. Using a framework based on Clayton copulas, we identify changes in the left-tail dependence of stock market returns between the war and pre-war periods and explore their determinants through limited dependent variable models. We find that the war-induced shifts in the market connectedness are significant but not uniform, involving both elevated left-tail linkages (financial contagion) and instances of diminished connectedness and increased market resilience. Such diverse changes can be attributed not only to cross-country differences in stock market volatilities and trade dynamics but also to countries' proximity to the warzone and their reliance on fossil-fuel imports, particularly their pre-war energy dependence on Russia. Our results highlight the need to consider these vulnerabilities in portfolio diversification strategies of international investors, as well as in financial stability policies. |
| Keywords: | stock markets; tail dependence; international market connectedness; financial contagion; Russia-Ukraine war. |
| JEL: | E44 F36 G11 G15 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:ise:remwps:wp03602024 |
| By: | Bryzgalova, Svetlana; Huang, Jiantao; Julliard, Christian |
| Abstract: | We propose a novel framework for analyzing linear asset pricing models: simple, robust, and applicable to high-dimensional problems. For a (potentially misspecified) stand-alone model, it provides reliable price of risk estimates for both tradable and nontradable factors, and detects those weakly identified. For competing factors and (possibly nonnested) models, the method automatically selects the best specification—if a dominant one exists—or provides a Bayesian model averaging–stochastic discount factor (BMA-SDF), if there is no clear winner. We analyze 2.25 quadrillion models generated by a large set of factors and find that the BMA-SDF outperforms existing models in- and out-of-sample. |
| JEL: | C11 C52 G12 C50 |
| Date: | 2023–02–28 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:126151 |
| By: | Saadaoui, Jamel; Smyth, Russell; Vespignani, Joaquin |
| Abstract: | Ensuring a stable supply of critical minerals at reasonable prices is essential for the clean energy transition. The security of supply of critical minerals is particularly susceptible to geopolitical risk. In this paper, we use constant and time-varying parameter local projection (TVP-LP) regression models to examine the effect of geopolitical risk on prices of six critical minerals: aluminium, copper, nickel, platinum, tin and zinc. We propose a conceptual framework in which we make two predictions. The first is that the responsiveness of prices for critical minerals to geopolitical risk will depend on the non-technical risk associated with procuring each critical mineral, which will be reflected in the elasticity of supply. The second is that geopolitical threats will have a bigger effect on critical mineral prices than geopolitical acts. With the exception of platinum prices, which have suffered a downward structural demand side shock associated with the growth of the electric vehicle market, we find empirical support for the first prediction. Our results are also consistent with the second prediction. We find considerable evidence that the effect of geopolitical risk on the prices of critical minerals are time varying with time-varying effects of geopolitical shocks observed during the Gulf War, following the 9/11 terrorist attacks and during the COVID-19 pandemic with the time varying effects generally being stronger for geopolitical threats than geopolitical acts. |
| Keywords: | Critical Minerals; Energy Security; Geopolitical Risk |
| JEL: | E00 Q40 Q43 |
| Date: | 2024–11–01 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122858 |
| By: | Sanroman Graciela; Bertoletti Lucía; Borraz Fernando |
| Abstract: | This paper examines the disparity in default risk between vulnerable and non-vulnerable populations in consumer lending. We merge an exhaustive registry of loans granted in the financial system with microdata on vulnerable individuals applying for social programs. We estimate the sources of this disparity and how loan and individual characteristics influence the probability of default. We find that vulnerable individuals have a higher risk than non-vulnerable individuals. However, this difference is reduced when individual debt characteristics, particularly the interest rate, are considered. Specifically, interest rates explain at least 30 percent of the risk gap. We also find that the default probabilities faced by lending firms are higher than those faced by banks, but we show that this effect is partly due to interest rate divergences. Our study underscores the importance of considering individual characteristics, loan characteristics, and interest rates when assessing default risk. While recognizing their limitations, these results suggest the need for policy interventions to promote financial inclusion, fair interest rate practices, and financial education, especially for vulnerable populations. |
| JEL: | G21 G51 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:aep:anales:4765 |