nep-fmk New Economics Papers
on Financial Markets
Issue of 2019‒11‒18
five papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. The CDS-bond Basis: Negativity Persistence and Limits to Arbitrage By Guesmi, Sahar; Ben-Abdallah, Ramzi; Breton, Michèle; Dionne, Georges
  2. The Universe of Leveraged Bank Loan and High Yield Bond U.S. Mutual Funds By Ayelen Banegas; Jessica Goldenring
  3. A structural model of interbank network formation and contagion By Coen, Patrick; Coen, Jamie
  4. Predicting bank distress in the UK with machine learning By Suss, Joel; Treitel, Henry
  5. Making a difference: European mutual funds distinctiveness and peers’ performance By BEREAU Sophie,; GNABO Jean-Yves,; VANHOMWEGEN Henri,

  1. By: Guesmi, Sahar (HEC Montreal, Canada Research Chair in Risk Management); Ben-Abdallah, Ramzi (Université du Québec à Montréal (UQAM)); Breton, Michèle (HEC Montreal, Department of Decision science); Dionne, Georges (HEC Montreal, Canada Research Chair in Risk Management)
    Abstract: We reinvestigate the CDS-bond basis negativity puzzle after the financial crisis. This puzzle is defined as the unexpected persistence of the dislocation between bond and derivative credit markets. We show that the first two moments of the basis are described by three distinct Markov regimes identified with periods related to the 2008 financial crisis. We observe that the post-crisis regime differs significantly from the crisis and the pre-crisis regimes. We then explore the cross-sectional variation of the CDS-bond basis in each regime. Using a model with several limit-to-arbitrage factors, we validate that the negative basis can be explained by liquidity risk in both the bond and CDS markets, together with counterparty risk, collateral quality, and funding constraints. Finally, we propose a model to empirically affirm that the basis negativity persistence during the post-crisis period is mainly related to a significant decrease in basis arbitrage activity, which is partly explained by the post-crisis regulatory reforms.
    Keywords: CDS-bond basis; Markov regime; arbitrage; liquidity; financial crisis; Basel regulation; Dodd-Frank Act.
    JEL: G12 G13 G14 G18 G28
    Date: 2019–11–04
  2. By: Ayelen Banegas; Jessica Goldenring
    Abstract: This note aims to characterize the universe of Bank Loan (BL) mutual funds (MFs) and compare it against that of High Yield Bond (HYB) MFs on several dimensions.
    Date: 2019–08–02
  3. By: Coen, Patrick (London School of Economics); Coen, Jamie (London School of Economics and Bank of England)
    Abstract: The interbank network, in which banks compete with each other to supply and demand differentiated financial products, fulfils an important function but may also result in risk propagation. We examine this trade-off by setting out a model in which banks form interbank network links endogenously, taking into account the effect of links on default risk. We estimate this model based on novel, granular data on aggregate exposures between banks. We find that the decentralised interbank market is not efficient: a social planner would be able to increase surplus on the interbank market by 13% without increasing mean bank default risk or decrease mean bank default risk by 4% without decreasing interbank surplus. We then propose two novel regulatory interventions (caps on aggregate exposures and pairwise capital requirements) that result in efficiency gains.
    Keywords: Contagion; systemic risk; interbank network; network formation
    JEL: G18 G28 L13 L51
    Date: 2019–10–11
  4. By: Suss, Joel (Bank of England); Treitel, Henry (Bank of England)
    Abstract: Using novel data and machine learning techniques, we develop an early warning system for bank distress. The main input variables come from confidential regulatory returns, and our measure of distress is derived from supervisory assessments of bank riskiness from 2006 through to 2012. We contribute to a nascent academic literature utilising new methodologies to anticipate negative firm outcomes, comparing and contrasting classic linear regression techniques with modern machine learning approaches that are able to capture complex non-linearities and interactions. We find the random forest algorithm significantly and substantively outperforms other models when utilising the AUC and Brier Score as performance metrics. We go on to vary the relative cost of false negatives (missing actual cases of distress) and false positives (wrongly predicting distress) for discrete decision thresholds, finding that the random forest again outperforms the other models. We also contribute to the literature examining drivers of bank distress, using state of the art machine learning interpretability techniques, and demonstrate the benefits of ensembling techniques in gaining additional performance benefits. Overall, this paper makes important contributions, not least of which is practical: bank supervisors can utilise our findings to anticipate firm weaknesses and take appropriate mitigating action ahead of time.
    Keywords: Machine learning; bank distress; early warning system
    JEL: C14 C33 C52 C53 G21
    Date: 2019–10–04
  5. By: BEREAU Sophie, (Université catholique de Louvain et Université de Namur); GNABO Jean-Yves, (Université de Namur et Université Paris-Nanterre); VANHOMWEGEN Henri, (Université de Namur)
    Abstract: Skilled managers of equity mumtual funds can develop innovoative strategies to outsmart their style peers. We unveil various causes of distinct investment strategies and test whether they materialize into outperformance of peer competitors. We frame our paper on European funds and propose a novel procedure to measure and test the impact of strategy distinctiveness while dealing with endogenous style classification and sample noise in peers’ comparisons of performance. We find a strong, robust and positive impact of ostrategy distinctiveness on financial performance. Yet, the marginal effect decreases with the level of distinctiveness.
    Keywords: European equity mutual funds, distinctiveness, commonality, peer performance, adaptivei clustering
    JEL: G11 G12 G23
    Date: 2019–07–10

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