nep-ifn New Economics Papers
on International Finance
Issue of 2024‒03‒11
eleven papers chosen by
Jiachen Zhan, University of California,Irvine

  1. The transmission of bank liquidity shocks: Evidence from the Eurosystem collateral framework By Hüttl, Pia; Kaldorf, Matthias
  2. The lending implications of banks holding excess capital By Neryvia Pillay; Konstantin Makrelov
  3. What Drives U.S. Import Price Inflation? By Mary Amiti; Oleg Itskhoki; David Weinstein
  4. Pension Liquidity Risk By Kristy Jansen; Sven Klingler; Angelo Ranaldo; Patty Duijm
  5. Exorbitant Privilege and the Sustainability of US Public Debt By Jason Choi; Duong Q. Dang; Rishabh Kirpalani; Diego J. Perez
  6. Back to the roots of internal credit risk models: Does risk explain why banks' risk-weighted asset levels converge over time? By Böhnke, Victoria; Ongena, Steven; Paraschiv, Florentina; Reite, Endre J.
  7. Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk Management By Zhenglong Li; Vincent Tam; Kwan L. Yeung
  8. Customer Data Access and Fintech Entry: Early Evidence from Open Banking By Tania Babina; Saleem A. Bahaj; Greg Buchak; Filippo De Marco; Angus K. Foulis; Will Gornall; Francesco Mazzola; Tong Yu
  9. Global bank network connectedness revisited: What is common, idiosyncratic and when? By Jonas Krampe; Luca Margaritella
  10. Navigating Market Turbulence: Insights from Causal Network Contagion Value at Risk By Katerina Rigana; Ernst C. Wit; Samantha Cook
  11. RAGIC: Risk-Aware Generative Adversarial Model for Stock Interval Construction By Jingyi Gu; Wenlu Du; Guiling Wang

  1. By: Hüttl, Pia; Kaldorf, Matthias
    Abstract: How does a shock to the liquidity of bank assets affect credit supply, cross-border lending, and real activity at the firm level? We exploit that, in 2007, the European Central Bank replaced national collateral frameworks by a single list. This collateral framework shock added loans to non-domestic euro area firms to the pool of eligible assets. Using loan level data, we show that banks holding a large share of newly eligible cross-border loans increase loan supply by 14% and reduce spreads by 16 basis points, compared to banks with smaller holdings of such loans. The additional credit is mainly extended to (previously eligible) domestic borrowers, suggesting only a limited cross-border effect of the collateral framework shock. However, the shock had real effects: firms highly exposed to affected banks increase their total debt, employment, and investment.
    Keywords: Bank Liquidity Shocks, Bank Lending Channel, Financial Integration, Real Effects, Eligibility Premia
    JEL: E44 E58 G21
    Date: 2024
  2. By: Neryvia Pillay; Konstantin Makrelov
    Abstract: Banks hold capital above microprudential and macroprudential regulatory requirements for a variety of reasons, including as a risk mitigation measure. In this study, we assess how decisions around the size of excess capital as well as monetary and financial stability actions impact sectoral lending in South Africa. Using a unique set of micro data for the South African banking sector for the period 2008 to 2020, provided by South Africas Prudential Authority, our analysis controls for bank characteristics such as bank size, profitability and liquidity. Our results suggest that banks decisions around holding additional capital affect their lending. As expected, monetary policy actions have a strong impact on bank lending and so do regulatory changes to bank capital requirements. These impacts tend to be smaller for larger banks, in line with results published in the global literature. Our results highlight the difficulties of thinking about policy in a Tinbergen rule type of world. Fiscal, microprudential, macroprudential and monetary policy actions can affect price and financial stability goals through their impact on credit extension. When policies work at cross purposes, they can easily undermine each others goals.
    Date: 2024–01–16
  3. By: Mary Amiti; Oleg Itskhoki; David Weinstein
    Abstract: Inflation has risen sharply in many countries since the COVID-19 outbreak. Economists have debated the underlying causes. In this paper, we examine the drivers of the global import price inflation, which peaked at approximately 11 percent a year. We find that a common global component closely tracks movements in aggregate U.S. import prices until late 2022. Afterward, idiosyncratic U.S. demand shocks started to dominate.
    JEL: E31 F14 F42
    Date: 2024–02
  4. By: Kristy Jansen; Sven Klingler; Angelo Ranaldo; Patty Duijm
    Abstract: Pension funds rely on interest rate swaps to hedge the interest rate risk arising from their liabilities. Analyzing unique data on Dutch pension funds, we show that this hedging behavior exposes pension funds to liquidity risk due to margin calls, which can be as large as 15% of their total assets. Our analysis uncovers three key findings: (i) pension funds with tighter regulatory constraints use swaps more aggressively; (ii) in response to rising interest rates, triggering margin calls, pension funds predominantly sell safe and short-term government bonds; (iii) we demonstrate that this procyclical selling adversely affects the prices of these bonds.
    Keywords: Pension funds; fixed income; interest rate swaps; liability hedging; liquidity risk; margin calls; price impact
    JEL: E43 G12 G18
    Date: 2024–02
  5. By: Jason Choi; Duong Q. Dang; Rishabh Kirpalani; Diego J. Perez
    Abstract: We study the extent to which the perceived cost of losing the exorbitant privilege the US holds in global safe asset markets sustains the safety of its public debt. Our findings indicate that the loss of this special status in the event of a default significantly augments the debt capacity for the US. Debt levels would be up to 30% lower if the US did not have this special status. Most of this extra debt capacity arises from the loss of the convenience yield on US Treasuries, which makes debt more expensive following its loss and provides strong incentives to repay debt.
    JEL: F0 F34
    Date: 2024–02
  6. By: Böhnke, Victoria; Ongena, Steven; Paraschiv, Florentina; Reite, Endre J.
    Abstract: The internal ratings-based (IRB) approach maps bank risk profiles more adequately than the standardized approach. After switching to IRB, banks' risk-weighted asset (RWA) densities are thus expected to diverge, especially across countries with different supervisory strictness and risk levels. However, when examining 52 listed banks headquartered in 14 European countries that adopted the IRB approach, we observe a downward convergence of their RWA densities over time. We test whether this convergence can be entirely explained by differences in the size of the banks, loss levels, country risk, and/or time of IRB implementation. Our findings indicate that this is not the case. Whereas banks in high-risk countries with less strict regulation and/or supervision, reduce their RWA densities, banks elsewhere increase theirs. Especially for banks in high-risk countries, RWA densities seem to underestimate banks' economic risk. Hence, the IRB approach enables regulatory arbitrage, whereby authorities may only enforce strict supervision on capital requirements if they do not jeopardize bank existence.
    Keywords: Capital regulation, credit risk, internal ratings-based approach, regulatory arbitrage, risk-weighted assets
    JEL: G21 G28
    Date: 2024
  7. By: Zhenglong Li; Vincent Tam; Kwan L. Yeung
    Abstract: Deep or reinforcement learning (RL) approaches have been adapted as reactive agents to quickly learn and respond with new investment strategies for portfolio management under the highly turbulent financial market environments in recent years. In many cases, due to the very complex correlations among various financial sectors, and the fluctuating trends in different financial markets, a deep or reinforcement learning based agent can be biased in maximising the total returns of the newly formulated investment portfolio while neglecting its potential risks under the turmoil of various market conditions in the global or regional sectors. Accordingly, a multi-agent and self-adaptive framework namely the MASA is proposed in which a sophisticated multi-agent reinforcement learning (RL) approach is adopted through two cooperating and reactive agents to carefully and dynamically balance the trade-off between the overall portfolio returns and their potential risks. Besides, a very flexible and proactive agent as the market observer is integrated into the MASA framework to provide some additional information on the estimated market trends as valuable feedbacks for multi-agent RL approach to quickly adapt to the ever-changing market conditions. The obtained empirical results clearly reveal the potential strengths of our proposed MASA framework based on the multi-agent RL approach against many well-known RL-based approaches on the challenging data sets of the CSI 300, Dow Jones Industrial Average and S&P 500 indexes over the past 10 years. More importantly, our proposed MASA framework shed lights on many possible directions for future investigation.
    Date: 2024–02
  8. By: Tania Babina; Saleem A. Bahaj; Greg Buchak; Filippo De Marco; Angus K. Foulis; Will Gornall; Francesco Mazzola; Tong Yu
    Abstract: Open banking (OB) empowers bank customers to share transaction data with fintechs and other banks. 49 countries have adopted OB policies. Consumer trust in fintechs predicts OB policy adoption and adoption spurs investment in fintechs. UK microdata shows that OB enables: i) consumers to access both financial advice and credit; ii) SMEs to establish new fintech lending relationships. In a calibrated model, OB universally improves welfare through entry and product improvements when used for advice. When used for credit, OB promotes entry and competition by reducing adverse selection, but higher prices for costlier or privacy-conscious consumers partially offset these benefits.
    JEL: G21 G23 G24 G28 G5 G50 K21 L10 L51 O31 O36 O38 O50
    Date: 2024–01
  9. By: Jonas Krampe; Luca Margaritella
    Abstract: We revisit the problem of estimating high-dimensional global bank network connectedness. Instead of directly regularizing the high-dimensional vector of realized volatilities as in Demirer et al. (2018), we estimate a dynamic factor model with sparse VAR idiosyncratic components. This allows to disentangle: (I) the part of system-wide connectedness (SWC) due to the common component shocks (what we call the "banking market"), and (II) the part due to the idiosyncratic shocks (the single banks). We employ both the original dataset as in Demirer et al. (2018) (daily data, 2003-2013), as well as a more recent vintage (2014-2023). For both, we compute SWC due to (I), (II), (I+II) and provide bootstrap confidence bands. In accordance with the literature, we find SWC to spike during global crises. However, our method minimizes the risk of SWC underestimation in high-dimensional datasets where episodes of systemic risk can be both pervasive and idiosyncratic. In fact, we are able to disentangle how in normal times $\approx$60-80% of SWC is due to idiosyncratic variation and only $\approx$20-40% to market variation. However, in crises periods such as the 2008 financial crisis and the Covid19 outbreak in 2019, the situation is completely reversed: SWC is comparatively more driven by a market dynamic and less by an idiosyncratic one.
    Date: 2024–02
  10. By: Katerina Rigana; Ernst C. Wit; Samantha Cook
    Abstract: Accurately defining, measuring and mitigating risk is a cornerstone of financial risk management, especially in the presence of financial contagion. Traditional correlation-based risk assessment methods often struggle under volatile market conditions, particularly in the face of external shocks, highlighting the need for a more robust and invariant predictive approach. This paper introduces the Causal Network Contagion Value at Risk (Causal-NECO VaR), a novel methodology that significantly advances causal inference in financial risk analysis. Embracing a causal network framework, this method adeptly captures and analyses volatility and spillover effects, effectively setting it apart from conventional contagion-based VaR models. Causal-NECO VaR's key innovation lies in its ability to derive directional influences among assets from observational data, thereby offering robust risk predictions that remain invariant to market shocks and systemic changes. A comprehensive simulation study and the application to the Forex market show the robustness of the method. Causal-NECO VaR not only demonstrates predictive accuracy, but also maintains its reliability in unstable financial environments, offering clearer risk assessments even amidst unforeseen market disturbances. This research makes a significant contribution to the field of risk management and financial stability, presenting a causal approach to the computation of VaR. It emphasises the model's superior resilience and invariant predictive power, essential for navigating the complexities of today's ever-evolving financial markets.
    Date: 2024–02
  11. By: Jingyi Gu; Wenlu Du; Guiling Wang
    Abstract: Efforts to predict stock market outcomes have yielded limited success due to the inherently stochastic nature of the market, influenced by numerous unpredictable factors. Many existing prediction approaches focus on single-point predictions, lacking the depth needed for effective decision-making and often overlooking market risk. To bridge this gap, we propose a novel model, RAGIC, which introduces sequence generation for stock interval prediction to quantify uncertainty more effectively. Our approach leverages a Generative Adversarial Network (GAN) to produce future price sequences infused with randomness inherent in financial markets. RAGIC's generator includes a risk module, capturing the risk perception of informed investors, and a temporal module, accounting for historical price trends and seasonality. This multi-faceted generator informs the creation of risk-sensitive intervals through statistical inference, incorporating horizon-wise insights. The interval's width is carefully adjusted to reflect market volatility. Importantly, our approach relies solely on publicly available data and incurs only low computational overhead. RAGIC's evaluation across globally recognized broad-based indices demonstrates its balanced performance, offering both accuracy and informativeness. Achieving a consistent 95% coverage, RAGIC maintains a narrow interval width. This promising outcome suggests that our approach effectively addresses the challenges of stock market prediction while incorporating vital risk considerations.
    Date: 2024–02

This nep-ifn issue is ©2024 by Jiachen Zhan. 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.
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