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This note discusses the basic economics of central clearing for derivatives and the need for a proper regulation, supervision and resolution of central counterparty clearing houses (CCPs). New regulation in the U.S. and in Europe renders the involvement of a central counterparty mandatory for standardized OTC derivatives’ trading and sets higher capital and collateral requirements for non-centrally cleared derivatives.
From a macrofinance perspective, CCPs provide a trade-off between reduced contagion risk in the financial industry and the creation of a significant systemic risk. However, so far, regulation and supervision of CCPs is very fragmented, limited and ignores two important aspects: the risk of consolidation of CCPs on the one side and the competition among CCPs on the other side. i) As the economies of scale of CCP operations in risk and cost reduction can be large, they provide an argument in favor of consolidation, leading at the extreme to a monopoly CCP that poses the ultimate default risk – a systemic risk for the entire financial sector. As a systemic risk event requires a government bailout, there is a public policy issue here. ii) As long as no monopoly CCP exists, there is competition for market share among existing CCPs. Such competition may undermine the stability of the entire financial system because it induces “predatory margining”: a reduction of margin requirements to increase market share.
The policy lesson from our consideration emphasizes the importance of a single authority supervising all competing CCPs as well as of a specific regulation and resolution framework for CCPs. Our general recommendations can be applied to the current situation in Europe, and the proposed merger between Deutsche Börse and London Stock Exchange.
We focus on the role of social media as a high-frequency, unfiltered mass information transmission channel and how its use for government communication affects the aggregate stock markets. To measure this effect, we concentrate on one of the most prominent Twitter users, the 45th President of the United States, Donald J. Trump. We analyze around 1,400 of his tweets related to the US economy and classify them by topic and textual sentiment using machine learning algorithms. We investigate whether the tweets contain relevant information for financial markets, i.e. whether they affect market returns, volatility, and trading volumes. Using high-frequency data, we find that Trump’s tweets are most often a reaction to pre-existing market trends and therefore do not provide material new information that would influence prices or trading. We show that past market information can help predict Trump’s decision to tweet about the economy.
In this paper, we investigate the relation between buildings' energy efficiency and the probability of mortgage default. To this end, we construct a novel panel dataset by combining Dutch loan-level mortgage information with provisional building energy ratings that are calculated by the Netherlands Enterprise Agency. By employing the Logistic regression and the extended Cox model, we find that buildings' energy efficiency is associated with lower likelihood of mortgage default. The results hold for a battery of robustness checks. Additional findings indicate that credit risk varies with the degree of energy efficiency.
We delve into the EU's regulatory changes aimed at boosting transparency in sustainable investments. By examining disparities among ESG rating agencies, we assess how these differences challenge standardization and consensus. Our analysis underscores the critical need for clearer ESG assessments to guide the sustainable investment landscape.
We show that bond purchases undertaken in the context of quantitative easing efforts by the European Central Bank created a large mispricing between the market for German and Italian government bonds and their respective futures contracts. On top of the direct effect the buying pressure exerted on bond prices, we show three indirect effects through which the scarcity of bonds, resulting from the asset purchases, drove a wedge between the futures contracts and the underlying bonds: the deterioration of bond market liquidity, the increased bond specialness on the repurchase agreement market, and the greater uncertainty about bond availability as collateral.
We study how the Eurosystem Collateral Framework for corporate bonds helps the European Central Bank (ECB) fulfill its policy mandate. Using the ECBs eligibility list, we identify the first inclusion date of both bonds and issuers. We find that due to the increased supply and demand for pledgeable collateral following eligibility, (i) securities lending market trading activity increases, (ii) eligible bonds have lower yields, and (iii) the liquidity of newly-issued bonds declines, whereas the liquidity of older bonds is unaffected/improves. Corporate bond lending relaxes the constraint of limited collateral supply, thereby making the market more cohesive and complete. Following eligibility, bond-issuing firms reduce bank debt and expand corporate bond issuance, thus increasing overall debt size and extending maturity.
We study the many implications of the Eurosystem collateral framework for corporate bonds. Using data on the evolving collateral eligibility list, we identify the first inclusion dates of bonds and issuers and use these events to find that the increased supply and demand for pledgeable collateral following eligibility (a) increases activity in the corporate securities lending market, (b) lowers eligible bond yields, and (c) affects bond liquidity. Thus, corporate bond lending relaxes the constraint of limited collateral supply and thereby improves market functioning.
Coming early to the party
(2017)
We examine the strategic behavior of High Frequency Traders (HFTs) during the pre-opening phase and the opening auction of the NYSE-Euronext Paris exchange. HFTs actively participate, and profitably extract information from the order flow. They also post "flash crash" orders, to gain time priority. They make profits on their last-second orders; however, so do others, suggesting that there is no speed advantage. HFTs lead price discovery, and neither harm nor improve liquidity. They "come early to the party", and enjoy it (make profits); however, they also help others enjoy the party (improve market quality) and do not have privileges (their speed advantage is not crucial).
With the second wave of the Covid-19 pandemic in full swing, banks face a challenging environment. They will need to address disappointing results and adverse balance sheet restatements, the intensity of which depends on the evolution of the euro area economies. At the same time, vulnerable banks reinforce real economy deficiencies. The contribution of this paper is to provide a comparative assessment of the various policy responses to address a looming banking crisis. Such a crisis will fully materialize when non-performing assets drag down banks simultaneously, raising the specter of a full-blown systemic crisis. The policy responses available range from forbearance, recapitalization (with public or private resources), asset separation (bad banks, at national or EU level), to debt conversion schemes. We evaluate these responses according to a set of five criteria that define the efficacy of each. These responses are not mutually exclusive, in practice, as they have never been. They may also go hand in hand with other restructuring initiatives, including potential consolidation in the banking sector. Although we do not make a specific recommendation, we provide a framework for policymakers to guide them in their decision making.
This policy letter adds to the current discussion on how to design a program of government assistance for firms hurt by the Coronavirus crisis. While not pretending to provide a cure-all proposal, the advocated scheme could help to bring funding to firms, even small firms, quickly, without increasing their leverage and default risk. The plan combines outright cash transfers to firms with a temporary, elevated corporate profit tax at the firm level as a form of conditional payback. The implied equity-like payment structure has positive risk-sharing features for firms, without impinging on ownership structures. The proposal has to be implemented at the pan-European level to strengthen Euro area resilience.
This Policy Letter presents a proposal for designing a program of government assistance for firms hurt by the Coronavirus crisis in the European Union (EU). In our recent Policy Letter 81, we introduced a new, equity-type instrument, a cash-against-tax surcharge scheme, bundled across firms and countries in a European Pandemic Equity Fund (EPEF). The present Policy Letter 84 focuses on the principles and conditions relevant for the operationalization of a EPEF. Our proposal has several desirable features. It: a) offers better risk sharing opportunities, augmenting the resilience of businesses and EU economies; b) is need-based, thereby contributing to an effective use of resources; c) builds on conditions and credible controls, addressing adverse selection and moral hazard; d) is accessible to smaller and medium-sized firms, the backbone of Europe’s economy; e) applies Europe-wide uniform eligibility criteria, strengthening support among member states; f) is a scheme of limited duration, reducing (perceived) government interference in businesses; g) creates a template for a growth-oriented public policy, aligning public and private sector interests; and h) builds on the existing institutional infrastructure and requires minimal legislative adjustments.
We investigate the default probability, recovery rates and loss distribution of a portfolio of securitised loans granted to Italian small and medium enterprises (SMEs). To this end, we use loan level data information provided by the European DataWarehouse platform and employ a logistic regression to estimate the company default probability. We include loan-level default probabilities and recovery rates to estimate the loss distribution of the underlying assets. We find that bank securitised loans are less risky, compared to the average bank lending to small and medium enterprises.
We investigate the default probability, recovery rates and loss distribution of a portfolio of securitised loans granted to Italian small and medium enterprises (SMEs). To this end, we use loan level data information provided by the European DataWarehouse platform and employ a logistic regression to estimate the company default probability. We include loan-level default probabilities and recovery rates to estimate the loss distribution of the underlying assets. We find that bank securitised loans are less risky, compared to the average bank lending to small and medium enterprises.
Energy efficiency represents one of the key planned actions aiming at reducing greenhouse emissions and the consumption of fossil fuel to mitigate the impact of climate change. In this paper, we investigate the relationship between energy efficiency and the borrower’s solvency risk in the Italian market. Specifically, we analyze a residential mortgage portfolio of four financial institutions which includes about 70,000 loans matched with the energy performance certificate of the associated buildings. Our findings show that there is a negative relationship between a building’s energy efficiency and the owner’s probability of default. Findings survive after we account for dwelling, household, mortgage, market control variables, and regional and year fixed effect. Additionally, a ROC analysis shows that there is an improvement in the estimation of the mortgage default probability when the energy efficiency characteristic is included as a risk predictor in the model.
This policy note summarizes our assessment of financial sanctions against Russia. We see an increase in sanctions severity starting from (1) the widely discussed SWIFT exclusions, followed by (2) blocking of correspondent banking relationships with Russian banks, including the Central Bank, alongside secondary sanctions, and (3) a full blacklisting of the ‘real’ export-import flows underlying the financial transactions. We assess option (1) as being less impactful than often believed yet sending a strong signal of EU unity; option (2) as an effective way to isolate the Russian banking system, particularly if secondary sanctions are in place, to avoid workarounds. Option (3) represents possibly the most effective way to apply economic and financial pressure, interrupting trade relationships.
We show that FED policy announcements lead to a significant increase in international comovements in the cross-section of equity and in particular sovereign CDS markets. The relaxation of unconventionary monetary policies is felt strongly by emerging markets, and by countries that are open to the trading of goods and flows, even in the presence of floating exchange rates. It also impacts closed economies whose currencies are pegged to the dollar. This evidence is consistent with recent theories of a global financial cycle and the pricing of a FED’s put. In contrast, ECB announcements hardly affect comovements, even in the Eurozone.
The SVB case is a wake-up call for Europe’s regulators as it demonstrates the destructive power of a bank-run: it undermines the role of loss absorbing capital, elbowing governments to bailout affected banks. Many types of bank management weaknesses, like excessive duration risk, may raise concerns of bank losses – but to serve as a run-trigger, there needs to be a large enough group of bank depositors that fails to be fully covered by a deposit insurance scheme. Latent run-risk is the root cause of inefficient liquidations, and we argue that a run on SVB assets could have been avoided altogether by a more thoughtful deposit insurance scheme, sharply distinguishing between loss absorbing capital (equity plus bail-in debt) and other liabilities which are deemed not to be bail-inable, namely demand deposits. These evidence-based insights have direct implications for Europe’s banking regulation, suggesting a minimum and a maximum for a banks’ loss absorption capacity.
Even if the importance of micro data transparency is a well-established fact, European institutions are still lacking behind the US when it comes to the provision of financial market data to academics. In this Policy Letter we discuss five different types of micro data that are crucial for monitoring (systemic) risk in the financial system, identifying and understanding inter-linkages in financial markets and thus have important implications for policymakers and regulatory authorities. We come to the conclusion that for all five areas of micro data, outlined in this Policy Letter (bank balance sheet data, asset portfolio data, market transaction data, market high frequency data and central bank data), the benefits of increased transparency greatly offset potential downsides. Hence, European policymakers would do well to follow the US example and close the sizeable gap in micro data transparency. For most cases, relevant data is already collected (at least on national level), but just not made available to academics for partly incomprehensible reasons. Overcoming these obstacles could foster financial stability in Europe and assure level playing fields with US regulators and policymakers.
The centrality of the United States in the global financial system is taken for granted, but its response to recent political and epidemiological events has suggested that China now holds a comparable position. Using minute-by-minute data from 2012 to 2020 on the financial performance of twelve country-specific exchange-traded funds, we construct daily snapshots of the global financial network and analyze them for the centrality and connectedness of each country in our sample. We find evidence that the U.S. was central to the global financial system into 2018, but that the U.S.-China trade war of 2018–2019 diminished its centrality, and the Covid-19 outbreak of 2019–2020 increased the centrality of China. These indicators may be the first signals that the global financial system is moving from a unipolar to a bipolar world.
We show that High Frequency Traders (HFTs) are not beneficial to the stock market during flash crashes. They actually consume liquidity when it is most needed, even when they are rewarded by the exchange to provide immediacy. The behavior of HFTs exacerbate the transient price impact, unrelated to fundamentals, typically observed during a flash crash. Slow traders provide liquidity instead of HFTs, taking advantage of the discounted price. We thus uncover a trade-o↵ between the greater liquidity and efficiency provided by HFTs in normal times, and the disruptive consequences of their trading activity during distressed times.
Amid increasing regulation, structural changes of the market and Quantitative Easing as well as extremely low yields, concerns about the market liquidity of the Eurozone sovereign debt markets have been raised. We aim to quantify illiquidity risks, especially such related to liquidity dry-ups, and illiquidity spillover across maturities by examining the reaction to illiquidity shocks at high frequencies in two ways:
a) the regular response to shocks using a variance decomposition and,
b) the response to shocks in the extremes by detecting illiquidity shocks and modeling those as ultivariate Hawkes processes.
We find that:
a) market liquidity is more fragile and less predictable when an asset is very illiquid and,
b) the response to shocks in the extremes is structurally different from the regular response.
In 2015 long-term bonds are less liquid and the medium-term bonds are liquid, although we observe that in the extremes the medium-term bonds are increasingly driven by illiquidity spillover from the long-term titles.
This literature survey explores the potential avenues for the design of a green auto asset-backed security by focusing on the European auto securitization market. In this context, we examine the entire value chain of the securitization process to understand the incentives and interests involved at various stages of the transaction. We review recent regulatory developments, feasibility concerns, and potential designs of a sustainable securitization framework. Our study suggests that a Green Auto ABS should be based on both a green use of proceeds and a green collateral-based methodology.
Recent advances in natural language processing have contributed to the development of market sentiment measures through text content analysis in news providers and social media. The effectiveness of these sentiment variables depends on the imple- mented techniques and the type of source on which they are based. In this paper, we investigate the impact of the release of public financial news on the S&P 500. Using automatic labeling techniques based on either stock index returns or dictionaries, we apply a classification problem based on long short-term memory neural networks to extract alternative proxies of investor sentiment. Our findings provide evidence that there exists an impact of those sentiments in the market on a 20-minute time frame. We find that dictionary-based sentiment provides meaningful results with respect to those based on stock index returns, which partly fails in the mapping process between news and financial returns.
We analyze the ESG rating criteria used by prominent agencies and show that there is a lack of a commonality in the definition of ESG (i) characteristics, (ii) attributes and (iii) standards in defining E, S and G components. We provide evidence that heterogeneity in rating criteria can lead agencies to have opposite opinions on the same evaluated companies and that agreement across those providers is substantially low. Those alternative definitions of ESG also affect sustainable investments leading to the identification of different investment universes and consequently to the creation of different benchmarks. This implies that in the asset management industry it is extremely difficult to measure the ability of a fund manager if financial performances are strongly conditioned by the chosen ESG benchmark. Finally, we find that the disagreement in the scores provided by the rating agencies disperses the effect of preferences of ESG investors on asset prices, to the point that even when there is agreement, it has no impact on financial performances.
We analyze the ESG rating criteria used by prominent agencies and show that there is a lack of a commonality in the definition of ESG (i) characteristics, (ii) attributes and (iii) standards in defining E, S and G components. We provide evidence that heterogeneity in rating criteria can lead agencies to have opposite opinions on the same evaluated companies and that agreement across those providers is substantially low. Those alternative definitions of ESG also a↵ect sustainable investments leading to the identification of di↵erent investment universes and consequently to the creation of di↵erent benchmarks. This implies that in the asset management industry it is extremely dicult to measure the ability of a fund manager if financial performances are strongly conditioned by the chosen ESG benchmark. Finally, we find that the disagreement in the scores provided by the rating agencies disperses the e↵ect of preferences of ESG investors on asset prices, to the point that even when there is agreement, it has no impact on financial performances.
To ensure the credibility of market discipline induced by bail-in, neither retail investors nor peer banks should appear prominently among the investor base of banks’ loss absorbing capital. Empirical evidence on bank-level data provided by the German Federal Financial Supervisory Authority raises a few red flags. Our list of policy recommendations encompasses disclosure policy, data sharing among supervisors, information transparency on holdings of bail-inable debt for all stakeholders, threshold values, and a well-defined upper limit for any bail-in activity. This document was provided by the Economic Governance Support Unit at the request of the ECON Committee.
Wir untersuchen die regulatorischen Änderungen in der EU, die die Transparenz bei nachhaltigen Investitionen erhöhen sollen. Durch eine Untersuchung der Unterschiede zwischen ESG-Ratingagenturen bewerten wir Herausforderungen für Standardisierung und Konsens von Ratings. Unsere Analyse unterstreicht die Dringlichkeit klarerer ESG-Ratings für eine nachhaltige Invesitionslandschaft.
We study the impact of transparency on liquidity in OTC markets. We do so by providing an analysis of liquidity in a corporate bond market without trade transparency (Germany), and comparing our findings to a market with full post-trade disclosure (the U.S.). We employ a unique regulatory dataset of transactions of German financial institutions from 2008 until 2014 to find that: First, overall trading activity is much lower in the German market than in the U.S. Second, similar to the U.S., the determinants of German corporate bond liquidity are in line with search theories of OTC markets. Third, surprisingly, frequently traded German bonds have transaction costs that are 39-61 bp lower than a matched sample of bonds in the U.S. Our results support the notion that, while market liquidity is generally higher in transparent markets, a sub-set of bonds could be more liquid in more opaque markets because of investors "crowding" their demand into a small number of more actively traded securities.
Banks can deal with their liquidity risk by holding liquid assets (self-insurance), by participating in interbank markets (coinsurance), or by using flexible financing instruments, such as bank capital (risk-sharing). We use a simple model to show that undiversifiable liquidity risk, i.e. the liquidity risk that banks are unable to coinsure on interbank markets, represents an important risk factor affecting their capital structures. Banks facing higher undiversifiable liquidity risk hold more capital. We posit that empirically banks that are more exposed to undiversifiable liquidity risk are less active on interbank markets. Therefore, we test for the existence of a negative relationship between bank capital and interbank market activity and find support in a large sample of U.S. commercial banks.
Central clearing counterparties (CCPs) were established to mitigate default losses resulting from counterparty risk in derivatives markets. In a parsimonious model, we show that clearing benefits are distributed unevenly across market participants. Loss sharing rules determine who wins or loses from clearing. Current rules disproportionately benefit market participants with flat portfolios. Instead, those with directional portfolios are relatively worse off, consistent with their reluctance to voluntarily use central clearing. Alternative loss sharing rules can address cross-sectional disparities in clearing benefits. However, we show that CCPs may favor current rules to maximize fee income, with externalities on clearing participation.
We study whether the presence of low-latency traders (including high-frequency traders (HFTs)) in the pre-opening period contributes to market quality, defined by price discovery and liquidity provision, in the opening auction. We use a unique dataset from the Tokyo Stock Exchange (TSE) based on server-IDs and find that HFTs dynamically alter their presence in different stocks and on different days. In spite of the lack of immediate execution, about one quarter of HFTs participate in the pre-opening period, and contribute significantly to market quality in the pre-opening period, the opening auction that ensues and the continuous trading period. Their contribution is largely different from that of the other HFTs during the continuous period.
The possibility to investigate the impact of news on stock prices has observed a strong evolution thanks to the recent use of natural language processing (NLP) in finance and economics. In this paper, we investigate COVID-19 news, elaborated with the ”Natural Language Toolkit” that uses machine learning models to extract the news’ sentiment. We consider the period from January till June 2020 and analyze 203,886 online articles that deal with the pandemic and that were published on three platforms: MarketWatch.com, Reuters.com and NYtimes.com. Our findings show that there is a significant and positive relationship between sentiment score and market returns. This result indicates that an increase (decrease) in the sentiment score implies a rise in positive (negative) news and corresponds to positive (negative) market returns. We also find that the variance of the sentiments and the volume of the news sources for Reuters and MarketWatch, respectively, are negatively associated to market returns indicating that an increase of the uncertainty of the sentiment and an increase in the arrival of news have an adverse impact on the stock market.
The recent COVID-19 pandemic represents an unprecedented worldwide event to study the influence of related news on the financial markets, especially during the early stage of the pandemic when information on the new threat came rapidly and was complex for investors to process. In this paper, we investigate whether the flow of news on COVID-19 had an impact on forming market expectations. We analyze 203,886 online articles dealing with COVID-19 and published on three news platforms (MarketWatch.com, NYTimes.com, and Reuters.com) in the period from January to June 2020. Using machine learning techniques, we extract the news sentiment through a financial market-adapted BERT model that enables recognizing the context of each word in a given item. Our results show that there is a statistically significant and positive relationship between sentiment scores and S&P 500 market. Furthermore, we provide evidence that sentiment components and news categories on NYTimes.com were differently related to market returns.
This paper analyzes sovereign risk shift-contagion, i.e. positive and significant changes in the propagation mechanisms, using bond yield spreads for the major eurozone countries. By emphasizing the use of two econometric approaches based on quantile regressions (standard quantile regression and Bayesian quantile regression with heteroskedasticity) we find that the propagation of shocks in euro's bond yield spreads shows almost no presence of shift-contagion. All the increases in correlation we have witnessed over the last years come from larger shocks propagated with higher intensity across Europe.
We study self- and cross-excitation of shocks in the Eurozone sovereign CDS market. We adopt a multivariate setting with credit default intensities driven by mutually exciting jump processes, to capture the salient features observed in the data, in particular, the clustering of high default probabilities both in time (over days) and in space (across countries). The feedback between jump events and the intensity of these jumps is the key element of the model. We derive closed-form formulae for CDS prices, and estimate the model by matching theoretical prices to their empirical counterparts. We find evidence of self-excitation and asymmetric cross-excitation. Using impulse-response analysis, we assess the impact of shocks and a potential policy intervention not just on a single country under scrutiny but also, through the effect on cross-excitation risk which generates systemic sovereign risk, on other interconnected countries.
We propose a spatiotemporal approach for modeling risk spillovers using time-varying proximity matrices based on observable financial networks and introduce a new bilateral specification. We study covariance stationarity and identification of the model, and analyze consistency and asymptotic normality of the quasi-maximum-likelihood estimator. We show how to isolate risk channels and we discuss how to compute target exposure able to reduce system variance. An empirical analysis on Euro-area cross-country holdings shows that Italy and Ireland are key players in spreading risk, France and Portugal are the major risk receivers, and we uncover Spain's non-trivial role as risk middleman.
This paper discusses policy implications of a potential surge in NPLs due to COVID-19. The study provides an empirical assessment of potential scenarios and draws lessons from previous crises for effective NPL treatment. The paper highlights the importance of early and realistic assessment of loan losses to avoid adverse incentives for banks. Secondary loan markets would help in this process and further facilitate bank resolution as laid down in the BRRD, which should be uphold even in extreme scenarios.
Non-standard errors
(2021)
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.
OTC discount
(2020)
We document a sizable OTC discount in the interdealer market for German sovereign bonds where exchange and over-the-counter trading coexist: the vastmajority of OTC prices are favorable with respect to exchange quotes. This is a challenge for theories of OTC markets centered around search frictions but consistent with models of hybrid markets based on information frictions. We show empiricallythat proxies for both frictions determine variation in the discount, which is largely passed on to customers. Dealers trade on the exchange for immediacy and via brokers for opacity and anonymity, highlighting the complementary roles played by the di↵erent protocols.
We develop a simple theoretical model to motivate testable hypotheses about how peer-to-peer (P2P) platforms compete with banks for loans. The model predicts that (i) P2P lending grows when some banks are faced with exogenously higher regulatory costs; (ii) P2P loans are riskier than bank loans; and (iii) the risk-adjusted interest rates on P2P loans are lower than those on bank loans. We confront these predictions with data on P2P lending and the consumer bank credit market in Germany and find empirical support. Overall, our analysis indicates the P2P lenders are bottom fishing when regulatory shocks create a competitive disadvantage for some banks.