Three Essays on Financial Stability
Thèse
Jean Armand Gnagne
Doctorat en économique
Philosophiæ doctor (Ph. D.)
Québec, Canada
Three Essays on Financial Stability
Thèse
Jean Armand Gnagne
Sous la direction de:
Kevin Moran, directeur de recherche Benoît Carmichael, codirecteur de recherche
Résumé
Cette thèse s’intéresse à la stabilité financière. Nous considérons plusieurs modèles écono-métriques visant à offrir une meilleure compréhension des perturbations pouvant affecter les systèmes bancaires et financiers. L’objectif ici est de doter les institutions publiques et régle-mentaires d’un éventail plus large d’instruments de surveillance.
Dans le premier chapitre, nous appliquons un modèle logit visant à identifier les principaux déterminants des crises financières. En plus des variables explicatives traditionnelles suggérées par la littérature, nous considérons une mesure des coûts de transactions (l’écart acheteur-vendeur) sur les marchés financiers. Nos estimations indiquent que des coûts de transactions élevés sont généralement associés à des risques accrus de crises financières. Dans un contexte où l’instauration d’une taxe sur les transactions financières (TTF) ferait augmenter les coûts de transactions, nos résultats suggèrent que l’instauration d’une telle taxe pourrait accroître les probabilités de crises financières.
Dans le second chapitre, nous analysons la formation des risques financiers dans un contexte où le nombre de données disponibles est de plus en plus élevé. Nous construisons des prédicteurs de faillites bancaires à partir d’un grand ensemble de variables macro-financières que nous incorporons dans un modèle à variable discrète. Nous établissons un lien robuste et significatif entre les variables issues du secteur immobilier et les faillites bancaires.
Le troisième chapitre met l’emphase sur la prévision des créances bancaires en souffrance (non-performing loans). Nous analysons plusieurs modèles proposés par la littérature et évaluons leur performance prédictive lorsque nous remplaçons les variables explicatives usuelles par des prédicteurs sectoriels construits à partir d’une grande base de données. Nous trouvons que les modèles basés sur ces composantes latentes prévoient les créances en souffrance mieux que les modèles traditionnels, et que le secteur immobilier joue à nouveau un rôle important.
Abstract
The primary focus of this thesis is on financial stability. More specifically, we investigate different issues related to the monitoring and forecasting of important underlying systemic financial vulnerabilities. We develop various econometric models aimed at providing a bet-ter assessment and early insights about the build-up of financial imbalances. Throughout this work, we consider complementary measures of financial (in)stability endowing hence the regulatory authorities with a deeper toolkit for achieving and maintaining financial stability. In the first Chapter, we apply a logit model to identify important determinants of financial crises. Along with the traditional explanatory variables suggested in the literature, we consider a measure of bid-ask spreads in the financial markets of each country as a proxy for the likely effect of a Securities Transaction Tax (STT) on transaction costs. One key contribution of this Chapter is to study the impact that a harmonized, area- wide tax, often referred to as Tobin Tax would have on the stability of financial markets. Our results confirm important findings uncovered in the literature, but also indicate that higher transaction costs are generally associated with a higher risk of crisis. We document the robustness of this key result to possible endogeneity effects and to the 2008 − 2009 global crisis episode. To the extent that a widely-based STT would increase transaction costs, our results therefore suggest that the establishment of this tax could increase the risk of financial crises.
In the second Chapter, we assess the build-up of financial imbalances in a data-rich envi-ronment. Concretely, we concentrate on one key dimension of a sound financial system by monitoring and forecasting the monthly aggregate commercial bank failures in the United States. We extract key sectoral predictors from a large set of macro-financial variables and incorporate them in a hurdle negative binomial model to predict the number of monthly com-mercial bank failures. We find a strong and robust relationship between the housing industry and bank failures. This evidence suggests that housing industry plays a key role in the build-up of vulnerability in the banking sector. Different specifications of our model confirm the robustness of our results.
In the third Chapter, we focus on the modeling of non-performing loans (NPLs), one other dimension along with, financial vulnerabilities are scrutinized. We apply different models proposed in the recent literature for fitting and forecasting U.S. banks non-performing loans (NPLs). We compare the performance of these models to those of similar models in which we replace traditional explanatory variables by key sectoral predictors all extracted from the large set of potential U.S. macro-financial variables. We uncover that the latent-component-based models all outperform the traditional models, suggesting then that practitioners and researchers could consider latent factors in their modeling of NPLs. Moreover, we also confirm that the housing sector greatly impacts the evolution of non-performing loans over time.
Table des matières
Résumé iii
Abstract iv
Table des matières vi
Liste des tableaux viii
Liste des figures x
Remerciements xiv
Avant-propos xvi
Introduction 1
1 Securities Transaction Taxes and Financial Crises 4
1.1 Résumé . . . 4
1.2 Abstract . . . 4
1.3 Introduction. . . 5
1.4 STT and transaction costs . . . 7
1.5 Methodology . . . 10
1.6 Data . . . 11
1.7 Results. . . 16
1.8 Robustness analyses . . . 19
1.9 Conclusion . . . 22
2 Monitoring Bank Failures in a Data-Rich Environment 23 2.1 Résumé . . . 23
2.2 Abstract . . . 23
2.3 Introduction. . . 24
2.4 Determinants of bank failures . . . 26
2.5 Data . . . 27
2.6 Econometric framework . . . 31
2.7 Results. . . 36
2.8 Conclusion . . . 45
3 On The Usefulness of Big Data in Modeling Non-Performing Loans 46 3.1 Résumé . . . 46
3.2 Abstract . . . 46
3.3 Introduction. . . 47
3.4 Recent empirical literature . . . 49
3.5 Econometric framework . . . 50
3.6 Data and preliminary analyses . . . 55
3.7 Estimation . . . 58
3.8 Out-of sample forecasting . . . 62
3.9 Conclusion . . . 64
Conclusion 65 A Monitoring Bank Failures in a Data-Rich Environment 67 A.1 Static HNB Model : additional analyses . . . 67
A.2 Dynamic HNB model : additionnal analyses . . . 69
A.3 Chapter 2 – list of explanatory Variables . . . 71
B On The Usefulness of Big Data in Modeling Non-Performing Loans 75 B.1 Preliminary analyses . . . 75
B.2 Chapter 3 – list of explanatory variables . . . 79
Liste des tableaux
1.1 Descriptive statistics for the variable “Financial Crisis" . . . 12
1.2 Definition and source of variables . . . 13
1.3 Index of transaction costs and adverse selection markers . . . 15
1.4 Index of transaction costs and tests of reverse causation . . . 16
1.5 Results from estimation of the likelihood (1.4). . . 17
1.6 Robustness I : lower threshold for asset price decline (20%) in crisis definition . 20 1.7 Robustness II : crisis defined by banking crises (Laeven and Valencia, 2012) only 20 1.8 Robustness III : sensitivity to country-specific financial openness (Chinn and Ito, 2008) . . . 21
1.9 Robustness IV : countries selection . . . 22
2.1 U.S. bank failures and assistances : descriptive statistics . . . 30
2.2 Data description . . . 31
2.3 Estimation of the number of commercial bank failures . . . 37
2.4 Actual and fitted cumulative frequencies . . . 38
2.5 Bank failures prediction with the HNB model : three-months-ahead horizon . . 41
2.6 Bank failures prediction with the dynamic HNB model : four-months-ahead horizon . . . 43
2.7 Bank failures prediction with the HNB model : sensitivity analysis . . . 45
3.1 Descriptive statistics for non-performing loans ratios (%). . . 55
3.2 Data description . . . 57
3.3 Static OLS estimation results . . . 58
3.4 Dynamic OLS estimation results . . . 59
3.5 VAR estimation. . . 60
3.6 VAR-X estimation . . . 61
3.7 Forecast error variance decomposition . . . 62
A.1 Static HNB predictors summary statistics . . . 68
A.2 Correlation across predictors in the static HNB model . . . 68
A.3 Dynamic HNB model grid search . . . 69
A.4 Dynamic HNB predictors summary statistics . . . 70
A.5 Correlation across predictors in the dynamic HNB model. . . 70
A.6 Chapter 2 – list of explanatory variables . . . 71
B.1 Descriptive statistics of the real estate loans proportion . . . 75
B.2 Descriptive statistics of the explanatory variables . . . 75
B.4 Correlation across estimated predictors - Factor model . . . 76
B.5 Unit root tests . . . 77
B.6 Granger causality tests . . . 77
B.7 Lag length criteria Test – VAR Benchmark model. . . 77
B.8 Lag length criteria test – VAR Factor model . . . 78
B.9 Lag length criteria test – VAR-X Benchmark model . . . 78
B.10 Lag length criteria test – VAR-X Factor model . . . 78
B.11 Explanatory variables (before transformation) - Benchmark model . . . 78
Liste des figures
1.1 Probability of crises according to the model . . . 19
2.1 Evolution of the U.S. banking industry : 1975 - 2013 . . . 28
2.2 U.S. bank failures and assistances (in levels and in proportion of total) . . . 29
2.3 Histogram of the U.S. monthly bank failures and assistances . . . 30
2.4 Predicted number of bank failures by model . . . 39
2.5 Bank failures prediction with the HNB model : various forecasting horizons . . 40
2.6 Bank failures prediction with the dynamic HNB model : four-months-ahead horizon . . . 44
3.1 Non-performing loans in the US banking sector . . . 56
3.2 IRF of the VAR model . . . 62
3.3 IRF of the VARX model . . . 63
3.4 Forecasting performance . . . 64
To the One who strengthens me, To my family.
To the loving memory of my brother Jean Hugues Gnagne.
Research is an organized method for keeping you reasonably dissatisfied with what you have.
Remerciements
Cette thèse constitue l’aboutissement d’un travail plus collectif qu’individuel. Remercier de manière exhaustive toutes ces personnes physiques et morales qui auront contribué de près ou de loin à la réalisation de ce travail s’avère pour moi une démarche bien périlleuse que je me garde d’entreprendre. De fait, je tiens à exprimer ici ma plus profonde gratitude à tous et tiens particulièrement à m’excuser pour toute omission qui relèverait sans doute de la nature humaine.
J’adresse d’entrée de jeu mes remerciements les plus sincères à mon directeur de thèse, le Professeur Kevin Moran pour l’excellent travail d’encadrement, ses précieux conseils, sa grande disponibilité et surtout son optimisme à toute épreuve. Son sens de la rigueur, sa grande compréhension des sujets macro-économiques, et son perpétuel souci de clarté constituent pour moi des enseignements que je retiendrai tout au long de ma carrière professionnelle. De ces années de collaboration, je garde un excellent souvenir. Je remercie également le Professeur Benoît Carmichael d’avoir accepté de codiriger ma thèse. Je le remercie pour sa disponibilité, son encadrement, sa bonne humeur constante et nos longues heures de discussions qui auront été déterminants lors des moments plus difficiles.
J’exprime également ma reconnaissance à tout le département d’économique de l’Université Laval, et particulièrement au Professeur Sylvain Dessy pour avoir cru en moi en m’acceptant au programme de doctorat. Son regard bienveillant et ses nombreux conseils m’ont grandement aidé. Je remercie aussi l’honorable Professeur Jean-Yves Duclos qui, en tant que directeur du département d’économie, m’a témoigné une grande confiance en m’octroyant mes premiers contrats de chargé de cours, et après lui, le Professeur Guy Lacroix. Je ne peux oublier de citer le Professeur Philippe Barla qui fut décisif dans notre cheminement. À travers lui, je remercie également le Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques (CRREP) pour toutes les bourses dont j’ai été le bénéficiaire.
Je souhaite de plus, marquer toute mon appréciation aux autres professeurs du département d’économique de l’Université Laval, ainsi qu’à tout le personnel. Merci spécial à Ginette Ther-rien pour la dose de bonne humeur distillée quotidiennement durant ces années. Nos précieuses discussions existentielles me manqueront. Merci également à Jocelyne Turgeon et Josée Des-gagnés pour l’excellent travail.
Merci à mes collègues, docteur(e)s et candidat(e)s au doctorat en économique, Gilles Koumou, Ali Yedan, Setou Diarra, Simplice Aimé Nono, Mbea Bell, Isaora, Ghislaine Sandra, Elfried, Bodel, Carolle et Marie Albertine. Je chérirai ces années de franche camaraderie et d’entraide. Soyez assurés de mes sentiments les plus affectueux.
Aussi, aimerais-je ici saluer mes collègues de la Direction de la Gestion de la Dette et de la Modélisation Financière du ministère des Finances du Québec. Je porte une mention spé-ciale à notre Directeur, M. Benjamin Calixte pour sa grande disponibilité, son ouverture et sa compréhension à mon égard. Son attitude bienveillante et ses encouragements m’ont permis de rapidement achever cette thèse. À mes autres collègues, Jean-David, Martin, Mireille et Jean-Philippe, merci de m’avoir rappelé quasi quotidiennement que j’avais une thèse à ter-miner. Sans vous, peut-être, l’aurais-je oublié. Également, aux autres collègues et directeurs du ministère des Finances du Québec, grand merci : spécialement au Directeur Général de l’Analyse et de la Prévision Économique, M. Daniel Floréa, au Directeur Raymond Fournier, au Directeur Francis Hébert, à la Directrice Debbie Gendron, merci d’avoir cru en moi. À mon collègue et ami, Jonathan Morneau-Couture, tu as auras été plus décisif et déterminant dans cette thèse que tu ne le penses.
Enfin, à ma famille, ma mère Albertine, mes frères et soeurs, Marcellin, Marina et Alice, mon épouse, May-Astrid et ma fille, Kayla, inutile de me répandre ici sur votre indéfectible soutien et amour. Vous le savez, je vous dois cette thèse.
Avant-propos
Les chapitres de la présente thèse constituent des articles soumis ou à soumettre à des revues scientifiques avec comité de lecture pour publication.
Le premier chapitre de cette thèse est un article réalisé avec mon directeur de recherche, Kevin Moran, et mon co-directeur Benoît Carmichael. Cet article, dont je suis l’auteur principal, fait l’objet de quelques révisions pour être soumis à une revue scientifique.
Le deuxième chapitre est un article réalisé avec mon directeur Kevin Moran, et mon co-directeur Benoît Carmichael. Cet article, dont je suis l’auteur principal, fait l’objet de quelques révisions pour être soumis à une revue scientifique.
Le dernier chapitre de cette thèse est un article réalisé avec mon directeur Kevin Moran, et mon co-directeur Benoît Carmichael. Cet article, dont je suis l’auteur principal, fait l’objet de quelques révisions pour être soumis à une revue scientifique.
Introduction
Over the last decades, achieving and maintaining financial stability has risen to prominence. The sequence of severe financial crises1of the years 1980’s and 1990’s put the need for a soun-der financial system at the top of the regulatory authorities priorities. Since, significant actions have been undertaken to bolster financial regulation. With, for example, the implementation of the Basel Committee, important achievements have been made. More integrated channels for the exchange of information between countries on developments in the banking sector and the build-up of imbalances have been set up. New global standards for the regulation and su-pervision of banks have been established, and a better cooperation with other financial sectors standard setters and international bodies have been fostered.2 However, as a reminder, the brutality of the subprime crisis unveiled significant loopholes in the financial regulation and renewed the interest of the regulatory authorities for a tighter regulation framework.
A functional definition of financial stability represents a key step towards a suitable regulation framework as it helps identify the set of policies to develop and implement. Defining financial stability or conversely financial instability has been one of the main focus of the macro-financial literature. Many authors have sought to provide a comprehensible definition covering all the principal aspects along with a sound financial system can be achieved and maintained. Still, defining financial stability proves a thorny issue since the literature lacks a clear and consensual definition of financial stability. As underlined by Schinasi (2004), does financial stability mean the soundness of institutions, the stability of markets, the absence of turbulence, low volatility, or something else more fundamental ? Should defining financial stability be the main focus or rather, defining financial instability ? The literature diverges on this standpoint.
Oosterloo et al. (2007), in a survey of central banks of the Organisation for Economic Co-operation and Development (OECD) countries, found that there is no unambiguous definition of financial stability. One strand of the literature, largely dominated by central bankers favors the definition of financial stability. The preeminent view put forward is that achieving financial stability is to ensure the financial system is capable of playing its role of facilitating the
1. Some of the major financial crises were the Latin America sovereign debt crisis, the Savings and Loans in the United States, the Russian financial crisis, the Asian financial crises.
2. For more details about the Basel Committee activities, we refer the readers to the Basel Committee Charter available at https ://www.bis.org/bcbs/charter.htm.
functioning of the economy, by channeling funds from depositors to investors, and impeding build-up of imbalances. For most central banks, defining financial stability rather than its absence is likely to be the more useful and avoid biased policy decisions. Another strand of the literature, largely dominated by academics (Mishkin,1999;Ferguson,2003;Allen and Wood,
2006; Goodhart, 2006; Borio and Drehmann, 2009) rather prefer to view financial stability through the lens of the absence of financial instability. These authors therefore focus on a list of potential characterizations of financial instability such as the incapacity of the financial system to perform its usual roles, the divergence of an important set of financial assets prices, the domestically or internationally rationing of credit, the emergence of financial distress in response to normal-sized shocks. As one example,Borio and Drehmann(2009) define financial distress as an event in which financial institutions experience substantial losses leading to serious dislocations to the economy. To the extent that, we focus on several financial distress characterizations, it seems reasonable to relate this thesis to the latter strand of the literature. This thesis adresses the issue of financial stability in several important ways. First, we consider different financial instability characterizations. The first Chapter analyzes financial distress episodes defined as a profound disruption of financial markets whose symptoms include sharp declines in asset prices and the failure of financial firms (Eichengreen and Portes,1987). The second Chapter examines the aggregate failures of the commercial banks and the third Chapter investigates the evolution of bank non-performing loans. Second, this thesis contributes to a better and early identification of forthcoming financial distress episodes by proposing various forecasting models. These models can be used by the regulatory authorities to monitor the build-up of financial imbalances. Third, throughout our work, we adopt an empirical approach which is aimed at providing regulatory authorities with workable tools to spot and address underlying financial vulnerabilities.
In the first Chapter, we focus on the impact that a harmonized, area-wide tax, often referred to as Tobin Tax could have on the stability of financial markets. We use the framework developed by Demirgüç-Kunt and Detragiache (1998) to identify the determinants of financial crises to a panel dataset of OECD countries over the sample 1973 − 2012. We add to the traditional explanatory variables suggested in the literature, a measure of bid-ask spreads in the financial markets of each country as a proxy for the likely effect of a securities transaction tax (STT) on transaction costs. We find that higher transaction costs are associated with a higher risk of crisis and we document the robustness of this key result to possible endogeneity effects and to the 2008 − 2009 global crisis episode. To the extent that an STT would increase transaction costs, the establishment of an STT could increase the risk of financial crises.
In the second Chapter, we model and forecast aggregate commercial bank failures. We construct key sectoral predictors from the large set of macro-financial variables developed byMcCracken and Ng(2016) for the United States and incorporate them in a hurdle negative binomial model to predict the number of monthly commercial U.S. bank failures. Our results indicate a strong
and robust relationship between the factor synthesizing housing industry variables and bank failures. This suggests a link between the housing sector and the vulnerability of commercial banks to non-performing loans increases and asset deterioration.
In the third Chapter, we review different models applied in the recent literature for fitting and forecasting U.S. banks non-performing loans (NPLs). We compare the performance of these models to those of similar models that we develop in a data-rich environment. We replace traditional explanatory variables by key sectoral predictors, all extracted from a large set of potential U.S. macro-financial predictors suggested by McCracken and Ng (2016) for big data analysis, that we supplement with additional banking variables. We uncover that data-rich-models all outperform the traditional models. Our results suggest that practitioners and researchers could consider latent factors in their modeling of NPLs. More specifically, for the U.S. case, we also point out that housing sector, which accounts only for almost 10% of the U.S. banks total loans in average, greatly impacts the evolution of NPLs over time.
Chapitre 1
Securities Transaction Taxes and
Financial Crises
1.1
Résumé
Ce chapitre étudie l’impact qu’une taxe sur les transactions financières (TTF), comme celle envisagée par la Commission Européenne, peut avoir sur la probabilité de crises financières. Nous appliquons la méthodologie développée par Demirgüç-Kunt and Detragiache(1998) aux données de pays de l’OCDE, de 1973 à 2012, auxquelles nous ajoutons une mesure du cours acheteur-vendeur, comme proxy de l’impact probable d’une TTF sur les coûts de transac-tions. Nos résultats indiquent que des coûts de transactions élevés sont associés à un risque accru de crises financières. Nous montrons la robustesse de ce résultat important aux possibles effets d’endogénéité et à la crise de 2008 − 2009. Dans la mesure où une TTF pourrait ac-croître les coûts de transactions, ce résultat suggère donc que l’établissement d’une telle taxe augmenterait les risques de crises financières.
1.2
Abstract
This Chapter studies the impact that a harmonized Securities Transaction Tax (STT), like the one considered by the European Commission, could have on the likelihood of systemic financial crises. We apply the methodology developed by Demirgüç-Kunt and Detragiache
(1998) to identify the determinants of financial crises to a panel dataset of OECD countries over the sample 1973 − 2012, adding a measure of bid-ask spreads in the financial markets of each country as a proxy for the likely effect of an STT on transaction costs. Our results indicate that higher transaction costs are associated with a higher risk of crisis and we document the robustness of this key result to possible endogeneity effects and to the 2008 − 2009 global crisis episode. To the extent that a widely-based STT would increase transaction costs, our results therefore suggest that the establishment of this tax could increase the risk of financial crises.
1.3
Introduction
Important changes to the global environment for regulating financial markets and institutions have been undertaken in recent years. These changes, motivated by the 2008-2009 financial crisis, aim to make financial markets more resilient and lessen the likelihood of systemic crises.1 In this context, the establishment of a Securities Transaction Tax (STT), an ad-valorem tax on financial transactions, has generated renewed interest. An ongoing policy effort initiated by the European Commission aims to introduce an area-wide, harmonized version of such a tax across the European Union that would have two stated goals : (i) increase the resilience of European financial markets by complementing other regulatory policies aimed at avoiding future crises, and (ii) generate revenue to help share the burden of future support to troubled financial institutions (European Commission,2013). The implementation of the EU STT has not proceeded in an orderly fashion, however, because important differences of opinion persist about its scope, magnitude and general appeal. As a result, 11 EU members have agreed to continue discussing a near-future implementation of the tax in their jurisdiction, while others have not joined these efforts.2
The present paper contributes to this debate by analyzing the impact that a harmonized, area-wide tax like the one envisaged would have on the stability of European financial markets. Our approach follows the framework introduced by Demirgüç-Kunt and Detragiache (1998) to study the determinants of financial crises and studies a significant panel of countries over a long, consistent historical sample, in order to filter out country and time-specific factors. The approach in Demirgüç-Kunt and Detragiache (1998) is related to an important body of work analyzing banking and financial crises in order to identify “early warning” variables – key factors associated with heightened crisis probabilities– signaling developing vulnerabilities (Kaminsky and Reinhart,1999;Borio and Lowe,2002;Bussiere and Fratzscher,2006;Barrell et al.,2010;Schularick and Taylor,2012;Duca and Peltonen,2013;Betz et al.,2014).
The extension of Demirgüç-Kunt and Detragiache (1998) that we develop is structured as follows. First, a binary financial crisis variable is constructed and an empirical logit model for this variable is formulated. As in the literature, this model includes a wide range of explanatory variables potentially associated with the likelihood of crisis. Next, we construct and incorporate to the model a country-specific, time-series measure of transaction costs in financial markets ; this index is meant to proxy for the likely impact of an harmonized STT on transaction costs.3 We show this proxy to be unrelated to other characteristics of financial markets, like turnover or volatility, and provide evidence that reverse causation from financial crises to transaction
1. These regulatory changes include increased capital requirements for banks, tighter limits on loan-to-value ratios, and macroprudential policies.
2. Some individual EU members have chosen to introduce their own, country-level version of the tax (France, 2012 ; Italy, 2013) even as planning for the harmonized, area-wide one continues.
3. Aliber et al.(2003) andLanne and Vesala(2010) adopt a similar approach and assess the relationship between a measure of transaction costs and the markets volatility to investigate the likely impact of an STT.
costs is unlikely. The complete model is then estimated using a panel dataset for the 34 OECD countries over the sample 1973 − 2012.
Our results uncover a positive, statistically and economically significant link between transac-tion costs and the likelihood of financial crisis ; said otherwise, higher transactransac-tion costs are associated with a higher risk of crisis. Benchmark results show that the odds of experiencing a crisis increase by 50 percent following one-standard deviation increase in these costs. We show this result to be robust to alternative measures of the crisis, estimation subsamples and the occurrence of the 2008-2009 global crisis.
This main finding has two important implications : first, it suggests that the “early warning” literature associated with Demirgüç-Kunt and Detragiache (1998), Kaminsky and Reinhart
(1999) and Borio and Lowe(2002,2009) might benefit from adding variables related to tran-saction costs to signal developing or increased vulnerabilities. Second, this finding also suggests that to the extent it would induce a general rise in transaction costs for financial trades, the implementation of a EU-wide STT could increase the likelihood of financial crisis, a result distinctly at odds with the effect envisaged by the framers of the EU proposal.
This intriguing result might be interpreted by noting that the establishment of an STT in-creases trading costs for all traders, both informed (rational) investors whose trades serve to stabilize markets and noise traders following ‘positive-feedback strategies’ (DeLong et al.,
1990) chasing momentum. If the tax leads more of the former to exit markets than the latter, the tax could lead to the building of financial imbalances (Borio and Lowe,2002,2009) that are precursors of crises.4
Previous work analyzing the impacts of STTs has most often focused on specific countries, historical episodes, or markets where such taxes were present. In addition, it has concentrated on aspects of financial markets’ performance different from the occurrence of systemic financial crises, such as trading volumes, individual asset volatility and market liquidity (Jackson and O’Donnell,1985;Roll,1989;Umlauf,1993;Saporta and Kan,1997;Pomeranets and Weaver,
2011;Capelle-Blancard and Havrylchyk,2016;Becchetti et al.,2014). The present paper the-refore contributes a novel set of results to the literature on the impact of STTs, by using a long, historically-consistent and area-wide approach and examining the impact of STTs on the likelihood of systemic financial crises.
The remainder of this paper is organized as follows. Section 1.4 discusses the theoretical underpinnings and available empirical results about STTs and their impact on transaction costs. It also discusses how they might affect the resilience of financial markets. Section 1.5 presents the econometric strategy we employ and Section 1.6 describes the data, providing
4. Relatedly,Friedman (1953) emphasizes the important stabilizing influence of informed traders for ex-change rate markets andLanne and Vesala(2010) provide empirical evidence that the actions of these traders might be reduced by the establishment of a ‘Tobin tax’ in these markets.
extensive details on how we construct the crisis variable and the proxy for the impact of STTs on transaction costs. Section 1.7 presents our results while Section 1.8 documents their robustness. Finally, Section 1.9 concludes.
1.4
STT and transaction costs
1.4.1 Securities transaction taxes
Establishing an ad-valorem tax on financial transactions was originally proposed by Keynes to reduce what he considered excess volatility and disruptive speculation in financial markets. The likely macroeconomic and financial impacts of such a tax has been the subject of an important literature ever since.5
Proponents of STTs (Tobin, 1978; Stiglitz, 1989; Summers and Summers, 1989) argue that these taxes can stabilize financial markets and increase their resilience. These authors suggest that when a significant fraction of trades in a given financial market reflect non-informed views or short-term (speculative) investing horizons, excess volatility obtains and leads prices to diverge from fundamentals. The environment developed by DeLong et al. (1990) reflects that view, and assumes the presence of “noise traders” basing their investment decisions on momentum rather than fundamentals, which amplifies movements in asset prices and increases volatility. By discouraging such trades, an STT could therefore stabilize financial markets without affecting long-term investors, whose trades reflect fundamentals and thus help better allocate capital.6
More skeptical views about the merits of STTs are advanced in Schwert and Seguin (1993),
Kupiec (1996), Amihud and Mendelson (2003) and Song and Zhang (2005), among others. These authors remark that an STT increases trading costs and the cost of capital for all investors and may have adverse effects when trades beneficial to liquidity and stability are thus discouraged. Kupiec (1996) for example, shows that discouraging trades by informed traders will accentuate the price-impact of non-informed trading, thus removing a stabilizing influence on financial markets. He further shows that return volatility unambiguously increases following the establishment of an STT because it causes a level-decrease in the average price of securities that trumps any decrease in their volatility. In addition, the environment developed in Song and Zhang (2005) emphasizes the positive impact of fundamentalists on deepening overall liquidity and the associated decreases in this liquidity from the establishment of an STT. Bloomfield et al. (2009) argue that the literature’s conflicted views of what constitutes “noise” trading may explain the lack of clear conclusions. They describe an experimental set-up where two types of “noise” traders co-exist -liquidity traders and uninformed traders–
5. Pomeranets(2012) provides a review of this literature.
6. The Commission proposal for the EU-STT reflects this opinion and states that one of the goal of the tax is to “create appropriate disincentives for transactions that do not enhance the efficiency of financial markets” (European Commission,2013).
and report that the establishment of STTs is unlikely to deliver the reductions in volatility envisioned by their proponents.
The impact of STTs on financial markets has also generated an important empirical literature. This literature has commonly used difference-in-difference frameworks contrasting the behavior of assets traded on an exchange subjected to an STT to that of similar assets traded elsewhere and not subjected to the tax. Umlauf (1993), analyzing the STT present in Sweden between 1984 and 1986, Pomeranets and Weaver (2011) (the New York state tax between 1932 and 1981) and Saporta and Kan (1997) (the UK Stamp Tax, 1963 − 1986) are examples of this strategy ; although they all report that these STTs depressed trading volumes significantly, they fail to reach a consensus about their impact on volatility. More recently, the 2012 decision by France to establish its own, country-specific STT has lead to renewed contributions to this literature (Becchetti et al.,2014;Capelle-Blancard and Havrylchyk,2016;Gomber et al.,2016). Again, these studies uncovered no significant impact of the French STT on volatility, liquidity, or general market quality.
This lack of consensus, and the fact that the empirical literature has not focused on the impact of STTs on systemic vulnerabilities, makes it challenging to judge whether the planned EU-STT can help avoid future crises. Our paper makes a contribution towards this assessment, by explicitly considering whether STTs affect the likelihood of systemic crises, using a well-established methodology to study the determinants of systemic crises (Demirgüç-Kunt and Detragiache,1998) and verifying if our proxy for the effects of an STT on transaction costs is significantly related to the occurrence of crises.
1.4.2 Securities transaction costs
Since no harmonized STT currently applies to a large group of countries, a proxy for its effect is developed to test this proposition. One likely effect of the EU-STT would be to increase the gap between prices paid by buyers and those received by sellers of financial assets. We therefore analyze the impact of an STT on the probability of crisis by using an index of transaction costs. We discuss below the conditions under which our index of the naturally dispersion in transaction costs in our data can be linked to the likely impacts of an STT on transaction costs.
Suppose τ , a round-trip ad valorem tax as the one envisaged by the European Union. After introduction of such a tax, an additional cost of τ χ is charged to transfer titles ownership of any asset i valued at χ. The change in the transacting costs is thus :
∆Costsi = τ χ, (1.1)
The literature provides various definition for securities transaction costs (STC) depending on the transaction activities included, as pointed out by Demsetz (1968) in his seminal paper
on transaction costs. Transaction costs can be viewed as the total amount paid by agents to acquire ownership of assets and these costs fall into two components : the brokerage commis-sion (BC) and the bid-ask spread (SPRD). The brokerage commiscommis-sion broadly represents fees paid to intermediaries to convey money from investors to market-makers which may be flat or proportionate to transaction volumes.7 The bid-ask spread is the difference between the ask (P a) and bid (P b) prices of the market-makers and represents the markup charged for immediate transaction. As an ad valorem tax, the change in transaction costs induced by the establishment of an STT is more likely to pass through the bid-ask spread than the brokerage commission. Thus, from the market-maker side, after the establishment of an STT, an extra cost of τ P bit will be required to purchase an asset i at time t and a supplement of τ P ait will be charged to sell the same asset. Consequently, the bid-ask spread is expected to increase by τ SP RDit.8
In order for our proxy to replicate as closely as possible the likely impact of an STT on tran-saction costs, it should ideally load heavily on the asset’s price (its value), and not be affected by other components affecting the bid-ask spread. It seems then worth to present the main components of bid-ask spread according to theoretical and empirical analyses. The seminal contribution of Demsetz (1968) identifies inventory costs and the market-maker markup for predictable immediacy of exchange as the main component of the bid-ask spread. Many theo-retical and empirical contributions following his research (Tinic,1972;Branch and Freed,1977;
Stoll, 1978; Easley and O’hara, 1987; Glosten and Harris, 1988; Harris, 1994; Bollen et al.,
2004) also discuss the importance of informational asymmetry in the bid-ask spread. Bollen et al. (2004) summarizes this research and write the bid-ask spread in the general form :
SP RDit= f (OP Cit, IHCit, ASCit, COM Pit), (1.2)
where OP C represents order-processing costs, IHC, the inventory-holding costs, ASC, the adverse selection costs and COM P , the competition level. Most empirical works on bid-ask determinants posit a linear function. Hereafter, some evidence on these bid-bid-ask spread determinants on which we will base our assumptions and construct our STC index are reviewed.
Bollen et al.(2004) point out that order-processing costs (OP C) are irrelevant for competitive markets.9 They also show, in a theoretical model, that IHC is proportional to the asset’s transaction value. This finding is empirically confirmed by Tinic and West (1972), Benston and Hagerman(1974),Tinic and West(1974) andGrant and Whaley(1978). Further, research on adverse selection costs (ASC) in stock market uncovered important evidence about “small firm" and “trade size" effects (Banz,1981; Reinganum,1981; Stoll and Whaley,1983;Easley 7. For some developed stock markets, brokerage commissions may quantitatively be negligible relative to other components of total transaction costs.
8. We assume that all the burden of this tax is transferred to customers. Assuming a tax burden shared between customers, intermediaries and market-makers does not invalidate our strategy, as the bid-ask spread should still increase by τ0SP RD, with τ0 representing the share of the STT incurred by the market-maker.
and O’hara,1987;Amihud,2002). Small firms are more likely to experience high transactions costs due to a perception of adverse selection and the trade size is sometimes interpreted as a signal of informational asymmetries. This “trade size" effect is heightened for small firms whose stocks are infrequently traded. Finally,Tinic and West(1972),Benston and Hagerman(1974),
Tinic and West (1974) and Grant and Whaley (1978) found a negative relationship between bid-ask spread and market competition level. As the level of competition increases, bid-ask spread decreases. Our approach to measure markets’ transaction costs will aim at mitigating the impacts of all components affecting the bid-ask spread often than those proportionate to asset values.
1.5
Methodology
We adopt the approach developed by Demirgüç-Kunt and Detragiache(1998) and first denote Yit as a binary variable indicating whether country i at time t experiences a crisis (Yit = 1) or not (Yit= 0). A logistic approach is used whereby
P rob(Yit= 1) = F (β0Xit) =
eβ0Xit
1 + eβ0Xit, (1.3)
where Xit is a (k · 1) vector of explanatory variables for country i at time t, and β is the (k · 1) vector of associated coefficients. The vector of explanatory variables Xit includes our constructed measure of transaction costs (described below) as well as other control variables used in the literature. Parameters of the model are estimated by maximizing the sample likelihood LogL =X i X t YitlogF (β0Xit) + (1 − Yit)log(1 − F (β0Xit)) . (1.4) In the data described below, financial crises constitute relatively rare events and some countries are considered not to have experienced any such crisis during the period reflected by our sample (1973 − 2012). We therefore abstain from including fixed-effects in our logistic approach, to prevent one variable (the fixed effect) to perfectly predict the dependent variable for the countries with no crisis.10
In a logistic model like (1.3), the magnitude of a parameter is not the variable’s marginal impact on probabilities, although the parameter’s sign correctly indicates the direction of probability change. Instead, a change in a given explanatory variable has a non-linear impact that is a function of all other variables and will tend to be smallest for country-period pairs with very low, or very high, initial crisis probabilities. In this context, the economic significance of our results is assessed by measuring how much a one-standard deviation change in a given
10. This data characteristic is also present in Demirgüç-Kunt and Detragiache(1998), who motivate abs-tracting from fixed effects by a desire to avoid the bias caused if only countries having experienced at least one crisis are included. They discuss using a probit model with random effects as an alternative, but note that doing so would require making the strong assumption that such random effects are uncorrelated with other regressors.
explanatory variable Xk modifies the odds of observing a crisis.11 This is expressed by the
log-change in the odds ratio ∆OR, which is computed as
log∆OR = logP rob(Yit= 1|Xitk+ 1) P rob(Yit= 0|Xitk+ 1)
/P rob(Yit= 1|Xitk) P rob(Yit= 0|Xitk)
= βk, (1.5)
where βk is the coefficient associated with the explanatory variable Xk. A reported value of 0.5, say, for βk indicates that a one-standard deviation increase in Xk causes a 50% increase in the odds of experiencing a crisis.
Once a crisis occurs, it may produce an endogenous economic response that includes an in-verse causality loop between our dependent and explanatory variables. To limit the extent of this problem, we follow Demirgüç-Kunt and Detragiache (1998) and estimate our model by excluding all observations from a country that are subsequent to the onset of the first crisis having affected that country ; this strategy eliminates potential endogeneity and crisis memory effects convincingly. Note that our work does not aim at analyzing the severity or duration of crises but only to determine factors associated with the eruption of crisis. Consequently, we purposely consider only contemporaneous variables (Demirgüç-Kunt and Detragiache,1998).
1.6
Data
Our dataset covers the 34 OECD countries over the period 1973 − 2012 and the data are at an annual frequency.12 The dependent variable – the occurrence of a financial crisis – is binary
and takes value 1 if a crisis is experienced and 0 otherwise. The main variable of interest is the likely impact of an STT on transaction costs. We construct a proxy for this using annual averages of the bid-ask spreads of large firms in the financial markets for all countries and all period-years in our sample. Explanatory variables are from the financial and macroeconomic realm and are similar to those used in related literature.
1.6.1 Dependent Variable
One influential view for what constitutes a financial crisis characterizes it as a profound dis-ruption of financial markets whose symptoms include sharp declines in asset prices and the failure of financial firms (Eichengreen and Portes,1987). To operationalize this definition, we use historical data on banking crises constructed byLaeven and Valencia(2012), a widely-used benchmark measure of banking crises, as well as data on OECD countries’ stock market indexes
11. Since our index of transaction costs, our explanatory variable of interest, is reported in normalized terms, so that a one-unit change reflects a one standard-deviation modification to the underlying variable.
12. Restricting the analysis to the relatively homogenous countries comprising the OECD is an appropriate strategy to assess the EU STT tax, which would apply to a relatively homogenous group of countries similar to the OECD members. In addition, data availability limitations, notably on the bid-ask spreads underlying our proxy for the STT’s effect on transaction costs, limits our capacity to extend the analysis to a larger set of countries.
Table 1.1 – Descriptive statistics for the variable “Financial Crisis" Number of Financial Crises when Defined with Decline in Asset Prices of :
Period 20% 50% 0% (Only Banking Crises)
USA Canada OECD USA Canada OECD USA Canada OECD
1973 − 1980 0 0 0 0 0 0 0 0 3
1981 − 1990 0 0 2 0 0 0 0 0 3
1991 − 2000 0 0 7 0 0 3 0 0 13
2001 − 2011 1 0 19 0 0 12 2 0 19
Total 1 0 28 0 0 15 2 0 38
Source : Authors’ calculations based onLaeven and Valencia(2012) and Datastream data on major stock indexes
from Datastream. Specifically, we define the presence of a financial crisis in a country-year ob-servation when a banking crisis occurs in the country according toLaeven and Valencia(2012) and when the country’s stock markets experience a year-over-year decline at a pre-determined threshold (we experiment with declines of 20% and 50%). Considering that an STT would arguably affect asset markets as much as the banking sector, we consider it important to use a crisis definition that records disruptions in both sectors.
Requiring that both the banking sector and asset markets experience major disruptions to measure financial crises makes them relatively rare events in the OECD data. Table 1.1shows that some countries (e.g. Canada) are not considered to have experienced a financial crisis in the 1973 − 2012 period, whereas others (like the United States) are considered to have experienced only one. In total, 28 crisis events are recorded for the 34 OECD countries in our sample when the threshold decline in assets prices is 20%, and 15 when a decline of 50% in asset prices is required to define a crisis.13 To assess the robustness of our results, we also estimate our model using only the banking crisis indicator from Laeven and Valencia(2012) (38 crisis events are then recorded, see Table 1.1). Importantly, Section 1.7 shows that our key result – a positive association between our STT proxy and the likelihood of crisis – is not affected by these changes in the measurement of crises.
1.6.2 Explanatory variables
Three explanatory variables from the macroeconomic realm are added : GDP growth, the inflation rate, and a measure of real short-term interest rates. These variables have been shown to be significantly associated to the occurrence of crisis by the previous literature (Demirgüç-Kunt and Detragiache, 1998; Davis and Karim, 2008; Duca and Peltonen, 2013).
Demirgüç-Kunt and Detragiache (1998) provides an intuitive discussion to motivate the link between these variables and banking crises, noting for example that lower levels of economic activity depress the capital position of banking institutions and high real interest rates hurt them by affecting the maturity mismatch of bank’ balance sheets.14
13. The data analyzed by Demirgüç-Kunt and Detragiache (1998) and others similarly feature few crisis events, even if they study samples of countries not limited to OECD members or cover longer time periods.
14. They include other macroeconomic explanatory variables in their analysis (exchange rates, terms-of-trade, and the fiscal health of governments) but these variables exhibit no statistically significant relation to
Next, we add three variables controlling for the strength and the depth of a country’s banking sector : the banks’ operating costs as a ratio of the sector’s income, total bank deposits and total liquid bank assets (both expressed in ratios to GDP). These variables have also been studied in the literature analyzing the determinants of financial crises ; for instance,Davis and Karim (2008) show that high values of liquid bank assets reduce these vulnerabilities whereas rapid expansions in the bank deposits (money) to GDP ratio increases them.
Finally, the quality and depth of a country’s assets markets are represented by four variables : total stock market capitalization and total value traded (both relative to the country’s GDP), turnover, and intra-period volatility. Once again these variables have been used previously to provide early warning to future crises, and Borio and Lowe (2002, 2009) notably single out rapid increases in prices as important variables for such purposes. Table 1.2 summarizes all variables used in the analysis and their source.15
Table 1.2 – Definition and source of variables
Variable Definition Source
Dependent Variables
Banking Crisis Significant banking system distresses IMF
Asset prices declines Annual variation in stock market indexes Datastream Securities Transactions Tax
STT Index Weighted Average of bid-ask spreads of 30 largest firms Datastream Macroeconomic Variables
GDP Growth Real GDP growth rate OECD
Inflation Consumer Price Index growth rate OECD
Real Interest Rate Real 3-Month Treasury Bill Rate OECD
Banking Variables
Bank Costs Bank Costs as a Ratio of Sector’s Income OECD
Total deposits Deposits in banks relative to GDP OECD
Liquid Assets Bank Cash and Liquid Securities to GDP OECD
Stock Market Variables
Capitalization Stock market Capitalization to GDP Datastream
Value Traded Value of Stock Market Transactions to GDP OECD
Stock Turnover Value of Transactions as a Ratio of Capitalization OECD Price Volatility Within-Year Standard Deviation of Stock Prices Datastream the probability of crisis.
15. Intra-period volatility refers to the intra-year, monthly standard deviation of the stock market index and is therefore conceptually different from the year-over-year change we use to define a financial crisis. A stock market’s index could be volatile during a given year even though its year-over-year change is null.
1.6.3 STT index
Assumptions
As a result of the emergence over the last decades of the Internet and technological advances like real-time stock market investing phone applications, the intermediation costs and broker commissions components of intermediation costs have sharply decreased. We therefore assume BCit to be negligible for all i, t. Given that the establishment of a securities transaction tax is not aimed at affecting the broker commission (as the tax does not affect the broker commis-sion), abstracting from BCit in the design of our index appears natural. In our analysis, we regard OECD countries stock market as sufficiently developed to be able to assume, following
Bollen et al. (2004), that order-processing costs and competition do not affect the bid-ask spread, ie. ∂SP RDit ∂OP Cit = ∂SP RDit ∂COM Pit ≈ 0. (1.6)
In other words, OECD countries’ stock markets are sufficiently competitive to eliminate in-efficiency and operate at marginal costs. At this stage and after having eliminated BC, OPC and COMP, we can rewrite the securities transaction costs from (1.2) as
ST Cit= a2IHCit+ a3ASCit+ it. (1.7)
As argued above, IHCit closely reflects asset values χ and ASCit is the premium charged for informational asymmetry, particularly relevant for small firms. To ensure that our STT index is not contaminated by such asymmetry, we therefore consider only large firms in the creation of our proxy so that our data reflects the following formula
ST Cit= a2χ, (1.8)
where χ again represents assets values. Comparing (1.8) and (1.1) shows that under our maintained assumptions, the dispersion in transactions costs present in our data is similar in spirit to the changes in such costs that the establishment of an STT would entail. As such, the impact of our transaction cost index on the likelihood of crisis can be interpreted as reflecting the potential impact of an STT on this likelihood.
Construction
We compute an average of the bid-ask spreads on the thirty largest publicly-traded companies of every country in our sample, weighted by capitalization, for each period-year in our sample. Our proxy for country i at time t is thus constructed as
ST Cit = X j Djit Dit (P Ajit− P Bjit P Bjit ), (1.9)
where Djitis firm j’s capitalization and Ditis the aggregate market capitalization for all firms considered of country i in year t.16 Over the complete sample, average computed transaction costs stand at 1.7% while the within-country but across time average is 2.4%. Considerable heterogeneity exists in the index : the average figures for the United States and Canada are relatively low (0.2%) while corresponding figures for countries like Sweden and Denmark are significantly higher. Before proceeding with our estimation, we verify our STC index is not affected by adverse selection, reverse causation (running from crisis to the STC index) and not driven by the 2009 global crisis episode.
Robustness of our measure Adverse selection
As discussed above, bid-ask spreads typically encompass a premium for adverse selection, which we have aimed to mitigate in the construction of our index. Stoll and Whaley (1983),
Easley and O’hara (1987) found significant relationships between adverse selection and va-riables like price volatility, transaction turnover, value traded and market capitalization. We empirically verify that adverse selection does not affect our transaction costs measure by re-gressing our index on these markers of adverse selection. Table 1.3shows that no significant relationship is present, which suggests that our index is exempt from influences owing to adverse selection.
Table 1.3 – Index of transaction costs and adverse selection markers
Marker of adverse selection Adj. R2(%) Coef. p-value
Price Volatility 0.00 0.00 0.94
Transaction Turnover 0.54 -0.09 0.12
Value Traded/Mkt Cap. 0.00 0.00 0.84
Note : the table reports the results of regressing our index of transaction costs on three markers of informational asymmetry like adverse selection.
Reverse causation
We now investigate further our index of transaction costs by considering whether reverse causation, by which the occurrence of a financial crisis would cause higher transaction costs, may affect the index. Following Furceri and Mourougane(2012), we estimate the equation
ST Ci,t= αi+ 4 X j=1 βjST Ci,t−j+ 4 X k=1 δkDi,t−k+ ei,t, (1.10)
where Di,t−k indicates whether country i has experienced a crisis or not in the past years. We consider an unbalanced panel data from 1987 to 2012 for 20 OECD countries. We include four lags of each explanatory variables as in Furceri and Mourougane(2012).17 As Table 1.4 16. Companies are added or deleted from the index for each country as their market capitalization evolves through time.
indicates, we uncover no significant relationship between past occurrences of financial crises and current values of STT index. Our index of transactions costs thus does not appear to be statistically linked to our financial crises measure, once the influence of their own past values is taken into account.
Table 1.4 – Index of transaction costs and tests of reverse causation
(1) (2) (3) STT Index (t − 1) −0.37 0.10 *** −0.36 0.10 *** −0.35 0.10 *** STT Index (t − 2) −0.38 0.10 *** −0.37 0.10 *** −0.37 0.10 *** STT Index (t − 3) −0.34 0.11 *** −0.33 0.10 *** −0.33 0.10 *** STT Index (t − 4) −0.27 0.10 ** −0.27 0.10 *** −0.27 0.10 *** Crisis (t − 1) 0.01 0.01 0.00 0.02 −0.00 0.01 Crisis (t − 2) 0.00 0.01 0.00 0.01 0.00 0.01 Crisis (t − 3) 0.00 0.01 0.00 0.01 0.00 0.01 Crisis (t − 4) 0.00 0.01 0.00 0.01 0.00 0.01 Adj. R-Sq.(%) 13.87 13.49 13.46
Note : Regression of our index of transaction costs on its past values and past crises (Furceri and Mourougane,2012). Standard errors are in italic. Symbols∗,∗∗and∗∗∗indicate statistical significance at 10%, 5% and 1% level. Results (1), (2) and (3) are arrived at with the different measures of the dependent variables described in Section 1.4.1.
As a synthesis, we consider both theoretical and empirical evidence to construct an index aimed at reflecting the likely impact of an STT on transaction costs. The assumptions we make, may in fact underestimate the true incidence of an STT.18 Indeed, although small, brokerage commissions and order-processing costs do apply and increase transaction costs. Furthermore, STTs may also amplify adverse selection premia, especially in periods of turmoil. Insofar as we purposely purged our index from these components, the evidence we uncover may downplay the effective impact of the imposition of an STT on transaction costs.
1.7
Results
Table 1.5 presents our estimates of the likelihood (1.4) according to several specifications. Recall that in our sample, all data subsequent to the first crisis experienced by a country are deleted. The specifications analyzed incorporate first the STT index (column 1), then the explanatory variables (macroeconomic, banking or financial) one block at the time (columns 2-4) or simultaneously (columns 5-7). Table1.5reflects results arrived at with the benchmark crisis measure (a crisis is defined by the presence of a systemic banking crisis and a decline of 50% in asset prices), but we verify the robustness of our results to this assumption below. The key results in Table 1.5concern the impact of the STT proxy on the likelihood of crises.
18. Note that the design of our index is in line with a strand of the literature favoring a measure of transaction costs based on bid-ask spreads (Schultz,1983;Stoll and Whaley,1983;Glassman,1987;Aliber et al.,2003;
Table 1.5 – Results from estimation of the likelihood (1.4) (1) (2) (3) (4) (5) (6) (7) Trans. Costs STT Index 0.4 0.1 ** 0.4 0.2 ** 0.5 0.2 ** 0.4 0.2 *** 0.4 0.2 ** 0.5 0.3 * 0.5 0.2 ** Macro. Var. GDP Growth — — — −0.9 0.4 *** — — — — — — −0.2 0.4 −1.0 0.4 ** 0.3 0.6 Inflation — — — −0.4 4.9 — — — — — — −6.4 7.7 0.1 5.8 −4.7 8.7
Real Interest Rate — — — 0.1 0.4 — — — — — — −0.2 0.6 0.7 0.5 −0.7 0.8 Bank. Var. Bank Costs — — — — — — 0.0 0.4 — — — — — — −0.1 0.4 — — — Total Depostis — — — — — — −3.3 3.5 — — — — — — −3.6 3.6 −10.2 5.9 * Liquid Assets — — — — — — 3.1 3.3 — — — — — — 3.3 3.4 11.7 5.8 ** Stck. Mkt. Var. Capitalization — — — — — — — — — −2.3 1.1 ** −2.5 1.2 ** — — — −6.2 2.7 ** Value Traded — — — — — — — — — 1.9 1.1 * 1.9 1.1 * — — — 4.1 2.1 * Stock Turnover — — — — — — — — — −1.1 1.0 -1.2 1.0 — — — −2.8 1.8 Price Volatility — — — — — — — — — 0.7 0.2 *** 0.7 0.2 *** — — — 0.8 0.3 *** N. Obs 287 266 188 262 246 179 217 Dev. Expl.(%) 5.8 14.8 12.5 41.5 43.2 24.8 51.4 Success Rate (%) 97.2 97.0 96.8 97.3 97.2 96.7 96.7 Accuracy (%) 100 100 100 66.7 66.7 100 60 Sensibility (%) 11.1 11.1 14.3 44.4 44.4 14.3 37.5
Notes : Standard errors are in italic. Symbols∗,∗∗ and∗∗∗indicate statistical significance at 10%, 5% and 1% level.
The estimated coefficients are positive, have stable magnitude across the table and are statisti-cally significant. They indicate that a rise in the proxy for transaction costs is associated with a higher risk of financial crisis. In addition, the results are economically significant : column (7) of the table, our benchmark for discussion below, notably reports that a one standard deviation change in the transaction cost index leads to a 50 percent increase in the odds ratio. These results suggest that the EU-STT could reduce the resilience of European financial mar-kets and increase their vulnerability to financial crises, an outcome at odds with the intent of the policy.
This intriguing result may be interpreted as follows. As discussed in Section 1.4.2, the esta-blishment of an STT increases transactions costs for all types of traders, both noise traders whose transactions may favour the emergence of financial vulnerabilities and rational (funda-mental) traders that stabilise markets by buying when prices are low and selling when they are high (Friedman,1953). If the STT entails a shift in the composition of these traders, perhaps because more rational (fundamental) traders exit relative to noise traders, the price impact of noise-traders’ transactions might increase and mean-reverting mechanisms guiding prices back to fundamentals might decline. In addition, transaction cost increases following the introduc-tion of an STT may trigger a shift in noise traders’ demand, towards cheaper and riskier ones. As discussed in Black(1986), some noise traders base transactions on mistaken information (noise) but others may trade because of its consumption (“fun”) value. The former may be discouraged by the establishment of an STT but not the latter for whom, sound but expensive assets might just become less attractive. This shift from sound assets towards cheaper and
weaker ones may increase vulnerabilities.19
The results in Table 1.5 also indicate that other macroeconomic and financial variables im-portantly affect the probability of crisis. Among them, GDP growth is estimated to reduce the probability of crisis in a sizeable and statistically significant fashion, a result that confirms those in previous contributions to the literature on crisis prediction (Demirgüç-Kunt and De-tragiache, 1998; Kaminsky and Reinhart,1999;Davis and Karim,2008). Similarly, high real interest rates are also associated with increased vulnerability to crises. Interestingly, the es-timated impact of the rate of inflation is not significant, in contrast to Demirgüç-Kunt and Detragiache(1998). This might be related to the fact that our dataset covers the relatively ho-mogenous countries forming the OECD and inflation was well-anchored in most such countries during a large proportion of the years covered by our sample.20 Estimates for the banking variables have the signs predicted by the literature. For instance, higher bank costs are asso-ciated with poorer resilience of financial markets. However, these estimates most often lack the statistical significance exhibited by the macroeconomic variables discussed above. This might stem from the relatively wide scope of our definition for financial crises, which requires both banking and asset markets to experience challenging conditions before a crisis is defined. Finally, the impact of some asset and stock markets variables is significant, notably market capitalization as a ratio of GDP and value traded, again as a ratio to GDP : increased ca-pitalization is linked to a lower probability of crisis, perhaps reflecting depth and liquidity, whereas large values for value traded indicate vulnerabilities to crises, possibly resulting from excessive growth in prices. Intra-period price volatility is also very significant and its posi-tive association with systemic crisis confirms theoretical and empirical work that focuses on a strong relation between higher volatility and financial turmoil (Umlauf, 1993; Jones and Seguin,1997;Pomeranets and Weaver,2011)
Four criteria measure model performance. First, “Deviance explained” is a goodness-of-fit measure assessing the value-added of the model in explaining crises relative to a constant-only specification. The next three measures, – the “Success”, “Accuracy” and “Sensibility” rates – index the model’s capacity to correctly classify events. To construct them, for each country-period pair, we consider the model’s in-sample prediction to be cYit = 1 (a crisis event) if F ( bβ0Xit) ≥ 0.5 and cYit = 0 (a non-crisis event) if F ( bβ0Xit) < 0.5. Using this prediction, the “Success Rate” is the fraction of times the model correctly predicts realized events, the “Accuracy” rate is the fraction of times the model was correct in predicting a crisis and the “Sensibility” rate is the proportion of realized crises correctly predicted by the model ; as such, “Accuracy” controls for type-II errors while “Sensibility” encompasses errors of type-I. The four criteria all indicate a good in-sample fit of our model : our baseline result in column 7 of the table notably indicates high goodness of fit (52%) and strikes a good balance between
19. Liu et al.(2016) point out the sensitivity of investors to transaction costs in their stock selection. 20. Using data from developed countries, Schularick and Taylor (2012) also fail to statistically associate inflation to financial crises.
Figure 1.1 – Probability of crises according to the model 0.00 0.25 0.50 0.75 1.00 DE 08 GR 08 HU 91 HU 08 IS 08 IL 77 IT 08 KR 97 LU 08 MX 81 MX 91 NL 08 NO 91 NO 08 Crisis Probability
DE – Germany, GR – Greece, HU – Hungary, IS – Iceland, IL – Israel, IT – Italy, KR – Republic of Korea, LU – Luxembourg, MX – Mexico, NL – Netherlands, NO – Norway
accuracy and sensibility. Figure 1.1 evaluates this performance graphically, by depicting the in-sample prediction of crises probability for some of the crisis episodes in our sample : it shows that our model captures well the majority of crises depicted.
Overall we interpret our results as confirming previous literature on the impact of various macroeconomic and financial variables on the risk of crisis, on the one hand, while putting forward the new result that our index of transaction costs, proxying for the likely impact of an STT, lowers resilience and increase that risk.
1.8
Robustness analyses
1.8.1 Alternative crisis definition
Our first robustness check involves the definition of financial crises. We re-estimate our model to gauge how a less stringent definition of crisis could have on our results. In this context, Table
1.6reports results arrived at when the decline in asset prices necessary to define the presence of a financial crisis is lower (20%) and Table1.7reports results obtained when banking crises solely define the occurrence of crisis. Both tables shows that our results are also robust to these alternative definitions : notably, the estimated coefficient associated to transaction costs continues to be positive and statistically significant in most cases.
Table 1.6 – Robustness I : lower threshold for asset price decline (20%) in crisis definition (1) (2) (3) (4) (5) (6) (7) Trans. Costs STT Index 0.3 0.2 * 0.4 0.2 * 0.3 0.2 * 0.4 0.2 ** 0.5 0.2 ** 0.9 0.6 * 0.6 0.3 ** Macro. Var. GDP Growth — — — −2.1 0.5 *** — — — — — — −2.2 0.7 *** −4.2 1.2 *** −2.5 1.0 *** Inflation — — — — — — — — — — — — 6.2 6.6 12.9 10.1 13.7 11.5
Real Interest Rate — — — — — — — — — — — — 1.3 0.8 0.5 0.5 1.6 0.8 * Bank. Var. Bank Costs — — — — — — — — — — — — — — — 0.9 0.5 ** — — — Total Depostis — — — — — — 0.6 2.0 — — — — — — 0.4 0.4 1.4 4.0 Liquid Assets — — — — — — −0.4 2.0 — — — — — — — — −0.8 4.2 Stck Mkt. Var. Capitalization — — — — — — — — — −1.2 0.8 0.0 0.8 — — — — — — Value Traded — — — — — — — — — 1.2 1.0 0.3 1.0 — — — 0.4 0.8 Stock Turnover — — — — — — — — — −0.6 0.8 0.0 0.8 — — — −0.0 0.8 Price Volatility — — — — — — — — — 1.3 0.3 *** 1.7 0.5 *** — — — 1.8 0.5 *** N. Obs 227 220 191 209 194 140 166 Dev. Expl.(%) 2.3 24.1 3.5 46.4 60.4 53.3 64.6 Success Rate (%) 93.8 94.1 93.2 95.7 96.4 95.0 97.6 Accuracy (%) 0 66.7 0 85.7 81.8 83.3 84.6 Sensibility (%) 0 14.3 0 42.9 64.3 45.5 84.6
Notes : Standard errors are in italic. Symbols∗,∗∗ and∗∗∗indicate statistical significance at 10%, 5% and 1% level.
Table 1.7 – Robustness II : crisis defined by banking crises (Laeven and Valencia,2012) only
(1) (2) (3) (4) (5) (6) (7) Trans. Costs STT Index 0.3 0.2 * 0.4 0.2 * 0.3 0.2 * 0.4 0.2 ** 1.0 0.4 ** 1.0 2.1 0.4 0.6 Macro. Var. GDP Growth — — — −1.9 0.6 *** — — — — — — −3.8 1.5 ** −6.2 2.6 ** −3.5 1.3 *** Inflation — — — — — — — — — — — — 12.2 9.5 7.8 20.7 −12.9 12.4
Real Interest Rate — — — 0.0 0.4 — — — — — — 3.1 1.6 * 0.6 1.5 −0.1 0.8 Bank. Var. Bank Costs — — — — — — — — — — — — — — — 2.0 1.2 * — — — Total Depostis — — — — — — −0.2 2.1 — — — — — — 0.9 5.5 1.3 4.4 Liquid Assets — — — — — — 0.5 2.1 — — — — — — −0.0 5.3 0.9 4.9 Stck. Mkt. Var. Capitalization — — — — — — — — — −0.5 0.9 2.3 1.2 ** — — — −3.4 1.7 * Value Traded — — — — — — — — — 0.6 0.9 −1.2 1.2 — — — 2.9 1.1 *** Stock Turnover — — — — — — — — — −0.3 0.8 2.3 1.3 * — — — −0.7 0.9 Price Volatility — — — — — — — — — 1.2 0.3 *** 3.1 1.2 *** — — — — — — N. Obs 193 177 165 178 166 117 144 Dev. Expl.(%) 2.7 25.4 5.4 41.6 75 72.4 62.5 Success Rate (%) 94.3 94.9 93.9 96.1 96.4 97.4 97.2 Accuracy (%) 0 100 0 83.3 77.8 85.7 87.5 Sensibility (%) 0 18.2 0 45.5 63.6 75 70
Notes : Standard errors are in italic. Symbols∗,∗∗ and∗∗∗indicate statistical significance at 10%, 5% and 1% level.
1.8.2 Unobserved country-specific risks
Although OECD countries, as a group of developed economies, share many macro-financial characteristics, important unobserved heterogeneity may subsist. Consequently, we consider a variable that may proxy for unobserved country-specific risk, ie the financial openness.
