nep-ban New Economics Papers
on Banking
Issue of 2021‒08‒30
29 papers chosen by
Christian Calmès, Université du Québec en Outaouais

  1. Central Bank Digital Currency in Historical Perspective: Another Crossroad in Monetary History By Michael D. Bordo
  2. The Fed Explained By Raul Anibal Feliz
  3. A Time-Varying Network for Cryptocurrencies By Li Guo; Wolfgang Karl H\"ardle; Yubo Tao
  4. Competition and Selection in Credit Markets By Constantine Yannelis; Anthony Lee Zhang
  5. How do banks propagate economic shocks? By Yusuf Emre Akgunduz; Seyit Mumin Cilasun; H. Ozlem Dursun-de Neef; Yavuz Selim Hacihasanoglu; Ibrahim Yarba
  6. Does financial inclusion reduce non-performing loans and loan loss provisions? By Ozili, Peterson Kitakogelu; Adamu, Ahmed
  7. The Pandemic's Impact on Credit Risk: Averted or Delayed? By SungJe Byun; Aaron L. Game; Alexander Jiron; Pavel Kapinos; Kelly Klemme; Bert Loudis
  8. Loan-to-Value Caps, Bank Lending, and Spillover to General-Purpose Loans By Selva Bahar Baziki; Tanju Capacioglu
  9. Global lending conditions and international coordination of financial regulation policies By Enisse Kharroubi
  10. Fragmentation, Price Formation, and Cross-Impact in Bitcoin Markets By Jakob Albers; Mihai Cucuringu; Sam Howison; Alexander Y. Shestopaloff
  11. The currency that came in from the cold - Capital controls and the information content of order flow By Francis Breedon; Thórarinn G. Pétursson; Paolo Vitale
  12. Analysis of the Impact of Borrower-Based Measures By Martin Cesnak; Jan Klacso; Roman Vasil
  13. The Joint Dynamics of Money and Credit Multipliers Since the Gold Standard Era By Luca Benati
  14. Whatever it takes to understand a central banker - Embedding their words using neural networks. By Martin Baumgaertner; Johannes Zahner
  15. The impact of the Covid-19 lockdown on the i-banking use: An empirical inquiry from Greece By Bechlioulis, Alexandros P.; Karamanis, Dimitrios
  16. U.S. agricultural banks’ efficiency under COVID-19 Pandemic conditions: A two-stage DEA analysis By Gao, Penghui; Secor, William; Escalante, Cesar L.
  17. Foundations of system-wide financial stress testing with heterogeneous institutions By Farmer, J. Doyne; Kleinnijenhuis, Alissa; Nahai-Williamson, Paul; Wetzer, Thom
  18. Анализ рисков потребительских кредитов с помощью алгоритмов машинного обучения // Consumer credit risk analysis via machine learning algorithms By Байкулаков Шалкар // Baikulakov Shalkar; Белгибаев Зангар // Belgibayev Zanggar
  19. Comparing minds and machines: implications for financial stability By Buckmann, Marcus; Haldane, Andy; Hüser, Anne-Caroline
  20. Canal d’incertitude de la COVID-19 : Quelles stratégies et tactiques pour la politique monétaire ? By PINSHI, Christian P.; MALATA, Alain
  21. Liquidity Shocks: Lessons Learned from the Global Financial Crisis and the Pandemic By Lorie Logan
  22. Money Creation in Decentralized Finance: A Dynamic Model of Stablecoin and Crypto Shadow Banking By Ye Li; Simon Mayer
  23. Mortgage pricing and monetary policy By Benetton, Matteo; Gavazza, Alessandro; Surico, Paolo
  24. Corporate stress and bank nonperforming loans: Evidence from Pakistan By M. Ali Choudhary; Anil K. Jain
  25. Trust and Financial Development: Forms of Trust and Ethnic Fractionalization Matter By Ali Recayi Ogcem; Ruth Tacneng; Amine Tarazi
  26. Macroeconomic and Financial Risks: A Tale of Mean and Volatility By Dario Caldara; Chiara Scotti; Molin Zhong
  27. Crypto Wash Trading By Lin William Cong; Xi Li; Ke Tang; Yang Yang
  28. Global Banking and Firm Financing: A Double Adverse Selection Channel of International Transmission By Leslie Sheng Shen
  29. Corrective Regulation with Imperfect Instruments By Eduardo Dávila; Ansgar Walther

  1. By: Michael D. Bordo
    Abstract: Digitalization of Money is a crossroad in monetary history. Advances in technology has led to the development of new forms of money: virtual (crypto) currencies like bitcoin; stable coins like libra/diem; and central bank digital currencies (CBDC) like the Bahamian sand dollar. These innovations in money and finance have resonance to earlier shifts in monetary history: 1) The shift in the eighteenth and nineteenth century from commodity money (gold and silver coins) to convertible fiduciary money and inconvertible fiat money; 2) the shift in the nineteenth and twentieth centuries from central bank notes to a central bank monopoly; 3) Then evolution since the seventeenth century of central banks and the tools of monetary policy. This paper analyzes the arguments for a CBDC through the lens of monetary history. The bottom line is that the history of transformations in monetary systems suggests that technical change in money is inevitably driven by the financial incentives of a market economy. Government has always had a key role in the provision of outside money, which is a public good. Government has also regulated inside money provided by the private sector. This held for fiduciary money and will likely hold for digital money. CBDC could make monetary policy more efficient, and it could transform the international monetary and payments systems.
    JEL: E42 E52 E58
    Date: 2021–08
  2. By: Raul Anibal Feliz
    Abstract: The 11th edition of The Fed Explained: What the Central Bank Does (formerly The Federal Reserve System Purposes & Functions) details the structure, responsibilities, and work of the U.S. central banking system. The Federal Reserve System performs five functions to promote the effective operation of the U.S. economy and, more generally, to serve the public interest. It includes three key entities: the Board of Governors, 12 Federal Reserve Banks, and the Federal Open Market Committee.
    Date: 2021–08–11
  3. By: Li Guo; Wolfgang Karl H\"ardle; Yubo Tao
    Abstract: Cryptocurrencies return cross-predictability and technological similarity yield information on risk propagation and market segmentation. To investigate these effects, we build a time-varying network for cryptocurrencies, based on the evolution of return cross-predictability and technological similarities. We develop a dynamic covariate-assisted spectral clustering method to consistently estimate the latent community structure of cryptocurrencies network that accounts for both sets of information. We demonstrate that investors can achieve better risk diversification by investing in cryptocurrencies from different communities. A cross-sectional portfolio that implements an inter-crypto momentum trading strategy earns a 1.08% daily return. By dissecting the portfolio returns on behavioral factors, we confirm that our results are not driven by behavioral mechanisms.
    Date: 2021–08
  4. By: Constantine Yannelis; Anthony Lee Zhang
    Abstract: We present both theory and evidence that increased competition may decrease rather than increase consumer welfare in subprime credit markets. We present a model of lending markets with imperfect competition, adverse selection and costly lender screening. In more competitive markets, lenders have lower market shares, and thus lower incentives to monitor borrowers. Thus, when markets are competitive, all lenders face a riskier pool of borrowers, which can lead interest rates to be higher, and consumer welfare to be lower. We provide evidence for the model’s predictions in the auto loan market using administrative credit panel data.
    JEL: D14 D4 G20 G21 G5 L62
    Date: 2021–08
  5. By: Yusuf Emre Akgunduz; Seyit Mumin Cilasun; H. Ozlem Dursun-de Neef; Yavuz Selim Hacihasanoglu; Ibrahim Yarba
    Abstract: This paper exploits the COVID-19 pandemic as a negative shock on firm revenues in affected industries and studies the transmission of this shock via banks. We use the ex-ante heterogeneity in the amount of loans issued to affected industries to measure the variation in banks' exposure to the negative shock. Using bank-firm level credit register data from Turkey, we show that banks transmitted the negative shock with a reduction in their loan supply not only to affected but also unaffected industries. The effect persists at the firm level, but is reduced for large firms and firms with existing relationships to state-owned banks.
    Keywords: Bank loan supply, Economic shocks propagation, COVID-19 pandemic, Bank lending channel, Firm borrowing channel
    JEL: G01 G21 G28 G32
    Date: 2021
  6. By: Ozili, Peterson Kitakogelu; Adamu, Ahmed
    Abstract: We examine whether countries that have high levels of financial inclusion have fewer non-performing loans and loan loss provisions in their banking sectors. The fixed effect panel regression methodology was used to analyse the effect of financial inclusion on bank non-performing loans and loan loss provisions. Using data from 48 countries, we find that greater formal account ownership is associated with high non-performing loans. Bank loan loss provisions are fewer in countries that have high levels of financial inclusion only when financial inclusion is achieved through the combined use of formal account ownership, bank branch supply and ATM supply. Also, non-performing loans are fewer in countries that experience economic boom and high levels of financial inclusion.
    Keywords: financial inclusion, non-performing loans, loan loss provisions, financial stability, bank stability, ATM, formal account ownership, credit risk, access to finance.
    JEL: G00 G20 G21 G23 G28 G29 O31
    Date: 2021–08
  7. By: SungJe Byun; Aaron L. Game; Alexander Jiron; Pavel Kapinos; Kelly Klemme; Bert Loudis
    Abstract: The COVID-19 recession resulted in historic unemployment and a significant shock to much of the service sector. Despite these macroeconomic challenges, banks' risk-based capital buffers remain high and the number of bank failures remains low. Government relief programs, including the Coronavirus Aid, Relief, and Economic Security (CARES) Act, both directly and indirectly helped stabilize bank balance sheets during the crisis.
    Date: 2021–07–30
  8. By: Selva Bahar Baziki; Tanju Capacioglu
    Abstract: This paper studies the effect of the introduction of and a subsequent easing in residential credit loan-to-value (LTV) ratio caps on bank lending and borrowers' loan usage with a unique and comprehensive bank-linked individual credit data set in a large emerging economy. We first show that following the introduction of an LTV cap, banks that were previously lending at rates above the limit have reduced residential lending, as targeted by the policy. We find that banks change their balance sheet composition as a response, replacing the reduction in residential lending with higher commercial loans and general-purpose loans issued to new residential borrowers.
    Keywords: Loan to value ratio, Credit risk, Housing loans, General-purpose loans, Credit spillover
    JEL: G21 G28 E51 E58 G20
    Date: 2021
  9. By: Enisse Kharroubi
    Abstract: Using a model of strategic interactions between two countries, I investigate the gains to international coordination of financial regulation policies, and how these gains depend on global lending conditions. When global lending conditions are determined non-cooperatively, I show that coordinating regulatory policies leads to a Pareto improvement relative to the case of no cooperation. In the non-cooperative equilibrium, one region - the core - determines global lending conditions, leaving the other region - the periphery - in a sub-optimal situation. The periphery then tightens regulatory policy to reduce the cost of sub-optimal lending conditions. Yet, in doing so, it fails to internalise a cross-border externality: tightening regulatory policy in one region limits ex ante borrowing in the other region, which increases the cost of sub-optimal lending conditions for the periphery. The equilibrium with cooperative regulatory policies can then improve on this outcome as both regions take into account the cross-border externality and allow for larger ex ante borrowing, ending in a lower cost of suboptimal lending conditions for the periphery.
    Keywords: regulatory policy, global financial conditions, international coordination
    JEL: D53 D62 F38 F42 G18
    Date: 2021–08
  10. By: Jakob Albers; Mihai Cucuringu; Sam Howison; Alexander Y. Shestopaloff
    Abstract: In light of micro-scale inefficiencies induced by the high degree of fragmentation of the Bitcoin trading landscape, we utilize a granular data set comprised of orderbook and trades data from the most liquid Bitcoin markets, in order to understand the price formation process at sub-1 second time scales. To achieve this goal, we construct a set of features that encapsulate relevant microstructural information over short lookback windows. These features are subsequently leveraged first to generate a leader-lagger network that quantifies how markets impact one another, and then to train linear models capable of explaining between 10% and 37% of total variation in $500$ms future returns (depending on which market is the prediction target). The results are then compared with those of various PnL calculations that take trading realities, such as transaction costs, into account. The PnL calculations are based on natural $\textit{taker}$ strategies (meaning they employ market orders) that we associate to each model. Our findings emphasize the role of a market's fee regime in determining its propensity to being a leader or a lagger, as well as the profitability of our taker strategy. Taking our analysis further, we also derive a natural $\textit{maker}$ strategy (i.e., one that uses only passive limit orders), which, due to the difficulties associated with backtesting maker strategies, we test in a real-world live trading experiment, in which we turned over 1.5 million USD in notional volume. Lending additional confidence to our models, and by extension to the features they are based on, the results indicate a significant improvement over a naive benchmark strategy, which we also deploy in a live trading environment with real capital, for the sake of comparison.
    Date: 2021–08
  11. By: Francis Breedon; Thórarinn G. Pétursson; Paolo Vitale
    Abstract: We analyse how capital controls affect FX microstructure, using as a case study the introduction and subsequent removal of controls in Iceland. We use a VAR of private order flow, Central Bank order flow and EURISK that allows for contemporaneous feedback effects to analyse the impact and information content of trades and find that controls have profound effects. When controls were introduced, volume plummeted, the information content of trading activity declined and became less responsive to macro news. While there was no recovery of trading volume after controls were abolished, the information content and responsiveness of trading activity increased sharply.
    JEL: C32 F31 F32 G14 G15
    Date: 2021–06
  12. By: Martin Cesnak (National Bank of Slovakia); Jan Klacso (National Bank of Slovakia); Roman Vasil (National Bank of Slovakia)
    Abstract: The National Bank of Slovakia has been actively implementing borrower-based measures since 2014. In this paper we provide a cost-benefit analysis of these measures. DSTI measures affected mainly the riskiest borrowers with at most secondary education and lower income. Exemptions from DTI limits are provided mainly to borrowers with a higher volume of loans and higher education. LTV limits affected mainly younger borrowers up to 35 years old. The impact of respective measures was affected by front-loading, by the gradual tightening of the limits and by other legislative changes. The highest impact is estimated in 2019, when the volume of newly granted loans was lowered by 17% due to the measures. The estimated impact on residential real estate prices is relatively mild. The current coronavirus pandemic is the first period when systemic risks could have materialized after the implementation of the measures. Due to the possible loan payment deferral the number of loans defaulted has remained relatively low, therefore LTV measures have not been able to limit credit losses. On the other hand, DSTI measures have helped to mitigate credit risk. Households affected the most by the pandemic were those with an already high debt burden even before the outbreak of the crisis. These households have used loan payment deferral to a larger extent.
    JEL: C58 D61 G21 G28
    Date: 2021–08
  13. By: Luca Benati
    Abstract: Since the XIX century, technological progress has allowed commercial banks to create ever greater amounts of broad money and credit starting from a unit of monetary base. Crucially, however, at the very low frequencies the relative amounts of the two aggregates created out of a unit of base money have remained unchanged over time in each of the 42 countries I analyze. This finding questions the widespread notion that, since WWII, credit has become disconnected from broad money, and suggests that, except for their greater productivity at creating broad money and credit out of base money, today’s commercial banks are not fundamentally different from their XIX century’s counterparts. The implication is that only the ascent of shadow banks has introduced a disconnect between broad money and credit.
    Keywords: Money; credit; Lucas critique; financial crises.
    Date: 2021–08
  14. By: Martin Baumgaertner (THM Business School); Johannes Zahner (Philipps-Universitaet Marburg)
    Abstract: Dictionary approaches are at the forefront of current techniques for quantifying central bank communication. This paper proposes embeddings – a language model trained using machine learning techniques – to locate words and documents in a multidimensional vector space. To accomplish this, we gather a text corpus that is unparalleled in size and diversity in the central bank communication literature, as well as introduce a novel approach to text quantification from computational linguistics. Utilizing this novel text corpus of over 23,000 documents from over 130 central banks we are able to provide high quality text-representations –embeddings– for central banks. Finally, we demonstrate the applicability of embeddings in this paper by several examples in the fields of monetary policy surprises, financial uncertainty, and gender bias.
    Keywords: Word Embedding, Neural Network, Central Bank Communication, Natural Language Processing, Transfer Learning
    JEL: C45 C53 E52 Z13
    Date: 2021
  15. By: Bechlioulis, Alexandros P.; Karamanis, Dimitrios
    Abstract: This paper studies the impact of Covid-19 lockdown on the i-banking use. During the first lockdown period in Greece, between April 13th and May 3rd, 2020, we conducted a survey of 4,807 respondents between 18 and 64 years old who participated in the labor force and used internet. The sample was appropriately weighted to accurately reflect the real population. The main result is straightforward: more days in a lockdown is associated with an increased possibility for further i-banking use. We also provide important insights to financial services’ providers by pointing out female gender, increasing age, living in a metropolitan area, and job security status as the most crucial predictors for shaping changing i-banking use.
    Keywords: Covid-19 health crisis; lockdown; i-banking use; respondents’ sentiments
    JEL: C83 G0
    Date: 2020–11–05
  16. By: Gao, Penghui; Secor, William; Escalante, Cesar L.
    Keywords: Agricultural Finance, Agribusiness, Productivity Analysis
    Date: 2021–08
  17. By: Farmer, J. Doyne; Kleinnijenhuis, Alissa; Nahai-Williamson, Paul; Wetzer, Thom
    Abstract: We propose a structural framework for the development of system-wide financial stress tests with multiple interacting contagion, amplification channels and heterogeneous financial institutions. This framework conceptualises financial systems through the lens of five building blocks: financial institutions, contracts, markets, constraints, and behaviour. Using this framework, we implement a system-wide stress test for the European financial system. We obtain three key findings. First, the financial system may be stable or unstable for a given microprudential stress test outcome, depending on the system's shock-amplifying tendency. Second, the 'usability' of banks' capital buffers (the willingness of banks to use buffers to absorb losses) is of great consequence to systemic resilience. Third, there is a risk that the size of capital buffers needed to limit systemic risk could be severely underestimated if calibrated in the absence of system-wide approaches.
    Keywords: Systemic risk, stress testing, financial contagion, financial institutions, capital requirements, macroprudential policy
    JEL: G17 G21 G23 G28 C63
    Date: 2020–05
  18. By: Байкулаков Шалкар // Baikulakov Shalkar (Center for the Development of Payment and Financial Technologies); Белгибаев Зангар // Belgibayev Zanggar (National Bank of Kazakhstan)
    Abstract: Данное исследование представляет собой попытку оценки кредитоспособности физических лиц с помощью алгоритмов машинного обучения на основе данных, предоставляемых банками второго уровня Национальному Банку Республики Казахстан. Оценка кредитоспособности заемщиков позволяет НБРК исследовать качество выданных кредитов банками второго уровня и прогнозировать потенциальные системные риски. В данном исследовании были применены два линейных и шесть нелинейных методов классификации (линейные модели - логистическая регрессия, стохастический градиентный спуск, и нелинейные - нейронные сети, k-ближайшие соседи (kNN), дерево решений (decision tree), случайный лес (random tree), XGBoost, наивный Байесовский классификатор (Naïve Bayes)) и сравнивались алгоритмы, основанные на правильности классификации (accuracy), точности (precision) и ряде других показателей. Нелинейные модели показывают более точные прогнозы по сравнению с линейными моделями. В частности, нелинейные модели, такие как случайный лес (random forest) и k-ближайшие соседи (kNN) на передискредитированных данных (oversampled data) продемонстрировали наиболее многообещающие результаты. // This project is an attempt to assess the creditworthiness of individuals through machine learning algorithms and based on regulatory data provided by second-tier banks to the central bank. The assessment of the creditworthiness of borrowers can allow the central bank to investigate the accuracy of issued loans by second-tier banks, and predict potential systematic risks. In this project, two linear and six nonlinear classification methods were developed (linear models – Logistic Regression, Stochastic Gradient Descent, and nonlinear - Neural Networks, kNN, Decision tree, Random forest, XGBoost, Naïve Bayes), and the algorithms were compared based on accuracy, precision, and several other metrics. The non-linear models illustrate more accurate predictions in comparison with the linear models. In particular, the non-linear models such as the Random Forest and kNN classifiers on oversampled data demonstrated promising outcomes.
    Keywords: потребительские кредиты, машинное обучение, банковское регулирование, стохастический градиентный спуск, логистическая регрессия, k-ближайшие соседи, классификатор случайных лесов, дерево решений, gaussian NB (Гауссовский наивный Байесовский классификатор), XGBoost, нейронные сети (многослойный персептрон), consumer credits, machine learning, bank regulation, stochastic gradient descent (linear model), logistic regression (linear model), kNN (neighbors), random forest classifier (ensemble), decision tree (tree), gaussian NB (naïve bayes), XGBoost, Neural network (MLP classifier)
    JEL: G21 G28 E37 E51
    Date: 2021
  19. By: Buckmann, Marcus (Bank of England); Haldane, Andy (Bank of England); Hüser, Anne-Caroline (Bank of England)
    Abstract: Is human or artificial intelligence more conducive to a stable financial system? To answer this question, we compare human and artificial intelligence with respect to several facets of their decision-making behaviour. On that basis, we characterise possibilities and challenges in designing partnerships that combine the strengths of both minds and machines. Leveraging on those insights, we explain how the differences in human and artificial intelligence have driven the usage of new techniques in financial markets, regulation, supervision, and policy making and discuss their potential impact on financial stability. Finally, we describe how effective mind-machine partnerships might be able to reduce systemic risks.
    Keywords: Artificial intelligence; machine learning; financial stability; innovation; systemic risk
    JEL: C45 C55 C63 C81
    Date: 2021–08–20
  20. By: PINSHI, Christian P.; MALATA, Alain
    Abstract: The Central Bank of Congo (BCC) lowered policy rate in response to uncertainty surrounding COVID-19. The impact of the pandemic on the economy is uncertain and depends on several factors. This cut in the policy rate would not help the economy to limit the fallout from COVID-19, so we should rethink other tactics and strategies, such as a good communication strategy and the deployment of unconventional measures. However, coordination with fiscal policy would be a determining factor in blurring the uncertain effects of the coronavirus crisis.
    Keywords: Monetary policy, Covid-19, uncertainty
    JEL: C32 E32 E44 E52 E63
    Date: 2020–09
  21. By: Lorie Logan
    Abstract: Remarks at the 2021 Financial Crisis Forum, Panel on Lessons for Emergency Lending (delivered via videoconference).
    Keywords: liquidity; markets; repo; facilities; financial; conditions; shocks; pandemic; COVID-19; announcements
    Date: 2021–08–11
  22. By: Ye Li; Simon Mayer
    Abstract: Stablecoins rise to meet the demand for safe assets in decentralized finance. Stablecoin issuers transform risky reserve assets into tokens of stable values, deploying a variety of tactics. To address the questions on the viability of stablecoins, regulations, and the initiatives led by large platforms, we develop a dynamic model of optimal stablecoin management and characterize an instability trap. The system is bimodal: stability can last for a long time, but once stablecoins break the buck following negative shocks, volatility persists. Debasement triggers a vicious cycle but is unavoidable as it allows efficient risk sharing between the issuer and stablecoin users.
    Date: 2021
  23. By: Benetton, Matteo (Haas School of Business, University of California); Gavazza, Alessandro (London School of Economics); Surico, Paolo (London Business School)
    Abstract: This paper provides novel evidence on lenders’ mortgage pricing and on how central bank operations affected it. Using the universe of mortgages originated in the UK, we show that lenders seek to segment the market by offering two-part tariffs composed of interest rates and origination fees, and that during recent periods of unconventional monetary policy, such as UK’s Funding for Lending Scheme, lenders decreased interest rates and increased origination fees. To understand lenders’ pricing strategies and their effects on market equilibrium, we develop and estimate a structural discrete-continuous model of mortgage demand and lender competition in which borrowers may have different sensitivities to rates and fees. We use the estimated model to decompose the effects of central bank unconventional monetary policy on mortgage pricing and lending, finding that central bank operations increased borrower surplus and lender profits. Moreover, although origination fees allow lender to price discriminate and capture surplus, banning fees would lower borrower surplus and aggregate welfare.
    Keywords: origination fees; mortgage demand; heterogeneity; structural estimation; unconventional monetary policy
    JEL: E52 G21
    Date: 2021–08–13
  24. By: M. Ali Choudhary; Anil K. Jain
    Abstract: Using detailed administrative Pakistani credit registry data, we show that banks with low leverage ratios are both significantly slower and less likely to recognize a loan as nonperforming than other banks that lend to the same firm. Moreover, we find suggestive evidence that this lack of recognition impedes loan curing, with banks with low leverage ratios reporting significantly higher final default rates than other banks for the same borrower (even after controlling for differences in loan terms). Our empirical findings are consistent with the theoretical prediction that classifying a nonperforming loan is more expensive for banks with less capital.
    Keywords: Credit markets; Banks; Corporate debt; Evergreening; Nonperforming loans
    JEL: G21 G33
    Date: 2021–08–20
  25. By: Ali Recayi Ogcem (LAPE - Laboratoire d'Analyse et de Prospective Economique - GIO - Gouvernance des Institutions et des Organisations - UNILIM - Université de Limoges); Ruth Tacneng (LAPE - Laboratoire d'Analyse et de Prospective Economique - GIO - Gouvernance des Institutions et des Organisations - UNILIM - Université de Limoges); Amine Tarazi (LAPE - Laboratoire d'Analyse et de Prospective Economique - GIO - Gouvernance des Institutions et des Organisations - UNILIM - Université de Limoges)
    Abstract: We examine the relationship between trust and financial development using detailed regional data in Turkey. We distinguish different forms of trust (i.e., generalized, narrow, and wide) and investigate whether varying degrees of generalized and narrow trust, as well as wide and narrow trust imply different financial development outcomes. Moreover, we assess how different forms of trust and their combination affect financial development in the presence of ethnically fragmented populations. We use instrumental variable (IV) estimations to address endogeneity issues and the potential reverse causality between trust and financial development. Our main results indicate that wide trust has a significantly positive impact on financial development. Moreover, in regions where narrow trust is relatively high, we find financial development benefits from increasing generalized trust. Our findings also highlight that whereas wide trust leads to more developed financial markets in more ethnically fragmented regions, generalized trust plays a stronger role in less fragmented ones. Further, we also analyze the impact of trust on the proportion of credit backed by stable funds such as deposits. Our findings show that generalized trust plays an important role in mitigating the adverse effects that ethnic fractionalization have on the availability of deposits or stable sources to fund loans. On the whole, our study highlights the importance of distinguishing the impact of different forms and combinations of trust. Generalized trust, which is the focus of most studies, is not an all-encompassing one-size-fits-all solution to enhance economic performance.
    Keywords: Trust,Financial development,Regional development,Ethnic fractionalization
    Date: 2021–08–19
  26. By: Dario Caldara; Chiara Scotti; Molin Zhong
    Abstract: We study the joint conditional distribution of GDP growth and corporate credit spreads using a stochastic volatility VAR. Our estimates display significant cyclical co-movement in uncertainty (the volatility implied by the conditional distributions), and risk (the probability of tail events) between the two variables. We also find that the interaction between two shocks--a main business cycle shock as in Angeletos et al. (2020) and a main financial shock--is crucial to account for the variation in uncertainty and risk, especially around crises. Our results highlight the importance of using multivariate nonlinear models to understand the determinants of uncertainty and risk.
    Keywords: Uncertainty; Tail risk; Joint conditional distributions; Main shocks
    JEL: C53 E23 E32 E44
    Date: 2021–08–19
  27. By: Lin William Cong; Xi Li; Ke Tang; Yang Yang
    Abstract: We introduce systematic tests exploiting robust statistical and behavioral patterns in trading to detect fake transactions on 29 cryptocurrency exchanges. Regulated exchanges feature patterns consistently observed in financial markets and nature; abnormal first-significant-digit distributions, size rounding, and transaction tail distributions on unregulated exchanges reveal rampant manipulations unlikely driven by strategy or exchange heterogeneity. We quantify the wash trading on each unregulated exchange, which averaged over 70% of the reported volume. We further document how these fabricated volumes (trillions of dollars annually) improve exchange ranking, temporarily distort prices, and relate to exchange characteristics (e.g., age and userbase), market conditions, and regulation.
    Date: 2021–08
  28. By: Leslie Sheng Shen
    Abstract: This paper proposes a "double adverse selection channel" of international transmission. It shows, theoretically and empirically, that financial systems with both global and local banks exhibit double adverse selection in credit allocation across firms. Global (local) banks have a comparative advantage in extracting information on global (local) risk, and this double information asymmetry creates a segmented credit market where each bank lends to the worst firms in terms of the unobserved risk factor. Given a bank funding (e.g., monetary policy) shock, double adverse selection affects firm financing at the extensive and price margins, generating spillover and amplification effects across countries.
    Keywords: Adverse selection; Global banking; Information asymmetry; International transmission; Monetary policy
    JEL: G21 F30
    Date: 2021–08–10
  29. By: Eduardo Dávila; Ansgar Walther
    Abstract: This paper studies the optimal design of second-best corrective regulation, when some agents or activities cannot be perfectly regulated. We show that policy elasticities and Pigouvian wedges are sufficient statistics to characterize the marginal welfare impact of regulatory policies in a large class of environments. We show that the optimal second-best policy is determined by a subset of policy elasticities: leakage elasticities, and characterize the marginal value of relaxing regulatory constraints. We apply our results to scenarios with unregulated agents/activities and with uniform regulation across agents/activities. We illustrate our results in applications to shadow banking, scale-invariant regulation, asset substitution, and fire sales.
    JEL: D62 G18 G28 H21
    Date: 2021–08

This nep-ban issue is ©2021 by Christian Calmès. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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.