# Research articles for the 2021-03-30

SSRN

Financial markets have experienced several negative sigma events in recent years; these eventsoccur with much more regularity than current risk models can predict. There is no guarantee thatthe training set's data generating process will be the same in the test set in finance. Mathematicalmodels are designed to operate with unlimited and changing data, and yet, actual events keepmaking life hard for most models. The assumption of independent and identically distributedrandom variables and a stationary time series do not hold in reality. Over-reliance on historical data and backtesting of models is not a sufficient approach to overcome these challenges.Reinforcement-learning faces similar challenges when applied to financial time series.Out-of-distribution generalization is a problem that cannot be solved without assumptions onthe data generating process. If the test data is arbitrary or unrelated to the training data, thengeneralization is not possible. Finding these particular principles could potentially help us buildAI and financial modeling systems. N-Beats, Oreshkin et al. [2020], is a deep neural architecturewith backward and forward residual links and a deep stack of fully-connected layers. N-Beats canbe considered as a meta-learning model for time series prediction. Meta-Learning is a machinelearning approach that intends to design models that can learn new skills or adapt to new environments rapidly with few training examples. We explore the performance of N-Beats and compareits performance with other deep learning models. The results are not conclusive in establishingN-Beats as a better model than the other models tested in this study. We show in this study thatother neural network-based models offer similar performance.

arXiv

In this article we consider the infinite-horizon Merton investment-consumption problem in a constant-parameter Black - Scholes - Merton market for an agent with constant relative risk aversion R. The classical primal approach is to write down a candidate value function and to use a verification argument to prove that this is the solution to the problem. However, features of the problem take it outside the standard settings of stochastic control, and the existing primal verification proofs rely on parameter restrictions (especially, but not only, R<1), restrictions on the space of admissible strategies, or intricate approximation arguments.

The purpose of this paper is to show that these complications can be overcome using a simple and elegant argument involving a stochastic perturbation of the utility function.

SSRN

This paper examines the historic drop in West Texas Intermediate (â€œWTIâ€) oil futures contracts that occurred on April 20, 2020 as a result of significant events occurring at that time. While price movements around this time were jarring and raised the specter of â€˜broken marketsâ€™, in reality, the futures contracts did what they were supposed to do, including the potential for negative pricing, and the physical oil markets took the price swings in stride and adapted accordingly. On the other hand, a conflux of low trading liquidity on the day and relative position of remaining market participants, some of whom were not extremely well-versed in the underlying intentions and terms of crude futures contracts, especially as it relates to taking physical delivery, temporarily increased volatility of the May20 WTI crude futures contract on the day prior to settlement. Given the ongoing democratization of investing, the events that played out illustrate the concept of â€˜caveat emptorâ€™ and the need for pro-active education in addition to reasonable safeguards in investment products and platforms.

SSRN

There is a positive relationship between cross-border bank-to-nonbank flows and country-level sustainability scores. This result is consistent with the signaling theory pointing out that a countryâ€™s sustainability score is a signal to attract more international fund flows. This finding suggests public policy makers to focus more on country-level sustainability investments in order to improve financing of resident firms.

SSRN

Partial ownership of stock in multiple competing firms is an important scholarly and policy topic in both corporate and antitrust law. Until now, the discussion has focused on ownership. This essay shifts the debate from a focus on common ownership to a focus on common control. No prior work has addressed the role of debt-related corporate control in corporate governance and competition, but debt-control-based governance is a critical part of the corporate landscape. Further, various creditors can exert control over more than one company in the same industry without any ownership. These insights in the corporate finance and bankruptcy law literatures have not penetrated antitrust debates or policy. Applying such insights, this essay suggests that a fundamental change in antitrust policy is necessary to police against debt-control-based collusion.

arXiv

This paper shows how reinforcement learning can be used to derive optimal hedging strategies for derivatives when there are transaction costs. The paper illustrates the approach by showing the difference between using delta hedging and optimal hedging for a short position in a call option when the objective is to minimize a function equal to the mean hedging cost plus a constant times the standard deviation of the hedging cost. Two situations are considered. In the first, the asset price follows a geometric Brownian motion. In the second, the asset price follows a stochastic volatility process. The paper extends the basic reinforcement learning approach in a number of ways. First, it uses two different Q-functions so that both the expected value of the cost and the expected value of the square of the cost are tracked for different state/action combinations. This approach increases the range of objective functions that can be used. Second, it uses a learning algorithm that allows for continuous state and action space. Third, it compares the accounting P&L approach (where the hedged position is valued at each step) and the cash flow approach (where cash inflows and outflows are used). We find that a hybrid approach involving the use of an accounting P&L approach that incorporates a relatively simple valuation model works well. The valuation model does not have to correspond to the process assumed for the underlying asset price.

SSRN

This study considers the informativeness of directorsâ€™ disclosure of going concern uncertainty in the financial statement notes that are regulated by AASB 101. It examines whether first-time disclosures and the linguistic tone of their content are related to distress resolution for companies that enter Voluntary Administration (VA), which is the insolvency rescue scheme in Australia. The results show that disclosures with a negative tone and those with a preponderant negative tone are related to VA outcomes. Overall, the findings show directors use AASB 101 note disclosures to provide useful information about going concern uncertainty.

SSRN

We apply the directed acyclic graph and spillover index models and find significant evidence of both implied volatility contagion and spillover. First, the global implied volatility smiles exhibit strong regional clustering. The European and American options markets form a separate contemporary contagion cluster from the Asia-Pacific region. However, the 30-day-lag test shows that none of these markets are completely independent of the other two regions in the long run. Among all of them, the European index options markets demonstrate the strongest implied volatility smile contagion. Second, there exists obvious heterogeneity among different markets in terms of the implied volatility spillover, and extreme market conditions like crises seem to intensify the spillover effect. A broader category of factors, including the short-run underlying index return trend, at-the-money option implied volatility and interest rate term spread, are the key determinants of implied volatility spillovers.

SSRN

We show that a bank with investors holding simultaneously both its equity and bond (dual-holders) exhibits lower risk and superior performance. Using the 2007-9 financial crisis as a quasi-natural experiment, we find that the presence of dual-holders reduces risky investment and risky business portfolio choices. Dual-holders' influence is higher in more opaque banks, indicating that the mechanism of transmission is through a decrease in information asymmetry and a reduction in debtholder-shareholder conflict. This effect translates into higher unconditional and risk-adjusted stock returns. Our results have important normative implications in improving the stability of financial systems.

SSRN

Using 102 sovereigns rated by the three largest credit rating agencies (CRA), S&P, Moodyâ€™s and Fitch between January 2000 and January 2019, we are the first to document that the first- mover CRA (S&P) in downgrades falls into a commercial trap. Namely, each sovereign downgrade by one notch by the first-mover CRA (S&P) results in 2.4% increase in the probability of a rating contract being cancelled by the sovereign client. The more downgrades S&P makes in a given month, the more their sovereign rating coverage falls relative to Moodyâ€™s. Our results are more pronounced for downgrades on small sovereign borrowers than on large sovereign borrowers. This paper explores the interaction between three themes of the literature: herding behaviour amongst CRAs, issues of conflict of interest and ratings quality.

SSRN

Shareholders are the residual claimants on the assets of a corporation. Creditors are fixed claimants whose interest lies in the solvency of the borrower. Consequently, shareholders are usually thought to have optimal incentives to maximise the value of the corporation. The article challenges this common wisdom in banking and proposes to reform bank governance granting (some) ex-ante governance rights to bank creditors. This aims at finetuning bank governance and incumbent substantive regulation, in particular the resolution framework for distressed banks, and enhance the quality of decision-making of banks in terms of risk-taking. At the same time, the proposed reform should increase the ex-ante credibility of resolution. The second part of the article operationalises this construct focusing on the specific case of the European Banking Union and discusses the design of the governance status of bail-inable creditors. The analysis demonstrates how bail-inable creditors can correct for shareholdersâ€™ perverse incentives and make debt governance work in banking. The policy proposal advanced in the paper would complement substantive regulation and prudential oversight. The governance role of creditors has the potential to be particularly helpful in preventing disproportionate risk-taking decisions in good times, when regulatory and supervisory standards are lax and systemic risk piles-up.

SSRN

With the advent of globalization and liberalization, the concept of M&As has become an integral part of the Indian economy. This phenomenon has had far reaching repercussions on Indian industrial milieu, its development and growth. The changing market conditions have brought very significant and spectacular developments in the economic scenario in India. One of the most fascinating of these developments is the emergence of merger and acquisition activity in the industrial sector. Corporate sector in India has been witnessing from time to time the incidents of M&A. In accordance with the respective business strategies, a large number of firms undergo M&A every year. It is in this context that the present study has been undertaken to analyze the impact of M&As on the efficiency of seventy seven acquiring firms undertaken for the study in terms of growth and size. For the acquiring firms in the year 1999, the time period 1996-99 has been taken as pre-merger period and 2000-04 as post-merger period. The pre-merger and post-merger period for firms in year 2000 has been taken as 1996-2000 and 2001-04, respectively.

SSRN

The COVID-19 pandemic has exerted a remarkable impact on stock market volatility around the globe. Can vaccination programs revert these adverse effects? To answer this question, we scrutinize daily data from 66 countries from January 1st, 2020, to February 18th, 2021. We provide convincing evidence that the vaccination helps to stabilize the global equity markets. The drop in volatility is robust to many considerations and does not depend on the pandemic itself or the government policy responses. The impact of vaccinations is relatively stronger in developed than in emerging markets.

SSRN

This paper considers the optimal demand for insurance in the presence of state dependent background uncertainty, that is, the uncertainty is dependent on the states which are clarified by the realization of the loss. First, we consider a situation in which the state dependent background risk is ordered by the stochastic dominance relation. When the background risk conditional on the loss state is worse (better) than the no-loss state, we determine the condition in which the insurance demand is higher (lower) compared with the state independent background risk. Second, we consider a situation in which the uncertainty is different between the loss and the no-loss state. When ambiguity is introduced into the loss (no-loss) state, introducing ambiguity is higher (lower) compared with the risk case. Last, we discuss resolving the insurance demand puzzles by the different recognition of state dependent background uncertainty.

SSRN

Growing evidence suggests that a large share of international trade transactions are made through intermediaries and that whether firms use them or not depends on different factors. In this paper, we investigate whether credit constraints introduce a degree of difference among firms in their mode of importing. To begin, we develop a simple analytical framework highlighting the possible links between credit constraints and reliance on import intermediaries, and then use firm-level data from 66 developing and developed countries to test the model's predictions. The results show that credit-constrained firms exhibit a higher probability of importing their inputs using an intermediary, while unconstrained firms are more likely to import directly. Our results also establish that the impact of credit constraints on the probability of indirect importing is amplified for firms with a higher distance from their international sourcing network. Moreover, if firms face other types of frictions to imports, then the probability that credit-constrained firms rely on intermediaries is estimated to be higher. The frictions we consider relate to the degree of regulatory burden and the extent of documentary compliance, time to import and other costs involved in import activities.

SSRN

In early 2021, non-fungible tokens (NFT) became the first application of blockchain technology to achieve clear public prominence. NFTs are tradeable rights to digital assets (images, music, videos, virtual creations) where ownership is recorded in smart contracts on a blockchain. Given the NFT market emerged out of cryptocurrencies, we explore if NFT pricing is related to cryptocurrency pricing. A spillover index shows only limited volatility transmission effects between cryptocurrencies and NFTs. But wavelet coherence analysis indicates co-movement between the two sets of markets. This suggests that cryptocurrency pricing behaviours might be of some benefit in understanding NFT pricing patterns. However, the low volatility transmissions also indicate that NFTs can potentially be considered as a low-correlation asset class distinct from cryptocurrencies.

SSRN

How big is the working capital channel? To answer this question, I estimate a dynamic model of investment with a working capital channel. I study this question for all firms listed in Compustat and for seven industries. For all the sample, I find that the working capital channel is not full as commonly is assumed in macroeconomic models, but it is still quantitatively important since its estimated value is 0.76. Analysis by industry suggests that the Retail Trade sector has the lowest working capital channel (0.48), the biggest sector in terms of number of firms -Manufacturing- has a strong one (0.7), and Agriculture and Construction sectors have a full working capital channel (1). These results provide microeconomic evidence on the quantitative relevance of working capital channel with a potential effect on macroeconomic models and monetary policy.

arXiv

Based on some analytic structural properties of the Gini and Kolkata indices for social inequality, as obtained from a generic form of the Lorenz function, we make a conjecture that the limiting (effective saturation) value of the above-mentioned indices is about 0.865. This, together with some more new observations on the citation statistics of individual authors (including Nobel laureates), suggests that about $14\%$ of people or papers or social conflicts tend to earn or attract or cause about $86\%$ of wealth or citations or deaths respectively in very competitive situations in markets, universities or wars. This is a modified form of the (more than a) century old $80-20$ law of Pareto in economy (not visible today because of various welfare and other strategies) and gives an universal value ($0.86$) of social (inequality) constant or number.

SSRN

The Eurosystem purchased â‚¬178 billion of corporate bonds between June 2016 and December 2018 under the Corporate Sector Purchase Programme (CSPP). Did these purchases lead to a deterioration of liquidity conditions in the corporate bond market, thus raising concerns about unintended consequences of large-scale asset purchases? To answer this question, we combine the Bundesbankâ€™s detailed CSPP purchase records with a range of liquidity indicators for both purchased and nonpurchased bonds. We find that while the flow of purchases supported secondary market liquidity, liquidity conditions deteriorated in the long-run as the Bundesbank reduced the stock of corporate bonds available for trading in the secondary market.

SSRN

Neuropsychological studies propose that listeners unconsciously assess speakersâ€™ trustworthiness via their facial expressions. Building on this theory, we investigate how investors respond to CEOsâ€™ dynamic hemifacial asymmetry of expressions (HFAsy) shown on CNBCâ€™s video interviews about corporate earnings. We employ a machine-learning approach of face-detection and facial-expression-recognition based on conventional neural network to measure CEOsâ€™ dynamic HFAsy. Consistent with the neuropsychological prediction that facial asymmetry induces distrust, we document that the stock market reacts negatively to the CEOâ€™s HFAsy shown on the interview video. We also find that the abnormal bid-ask spread around the interview date is positively associated with the CEOâ€™s HFAsy. We further show that these effects are more pronounced for firms with weaker information environments. Finally, we document that analyst forecast revisions are negatively associated with CEOsâ€™ HFAsy. Overall, our study provides evidence that investor trust and trading behavior are affected by the dynamic hemifacial asymmetry of expressions appeared on CEOsâ€™ faces.

SSRN

We study the 'interconnectedness' of stress-tested banks by exploiting how they are mentioned together in the context of financial news. We start by constructing weekly co-occurrence network matrices following Ronnqvist and Sarlin (2015) text-to-network approach. Using the COVID-19 pandemic as an external shock, we examine how bank networks behave during high stress periods. We find that banks become more interconnected during peaks of COVID-19 induced stress. We put forth a new measure of systemic risk that utilizes text-based eigenvector centrality. This measure provides a more stable ranking system than the traditional SRISK measure during both high and low stress periods.

arXiv

This paper addresses allocation methodologies for a risk measure inherited from ruin theory. Specifically, we consider a dynamic value-at-risk (VaR) measure defined as the smallest initial capital needed to ensure that the ultimate ruin probability is less than a given threshold. We introduce an intuitively appealing, novel allocation method, with a focus on its application to capital reserves which are determined through the dynamic value-at-risk (VaR) measure. Various desirable properties of the presented approach are derived including a limit result when considering a large time horizon and the comparison with the frequently used gradient allocation method. In passing, we introduce a second allocation method and discuss its relation to the other allocation approaches. A number of examples illustrate the applicability and performance of the allocation approaches.

SSRN

The COVID-19 pandemic has highlighted the impacts that rare disasters can have on credit markets. We discuss and quantify the asset-pricing implications of disaster risk on the risk-free rate, credit spreads, and their term structures. The findings underscore the heterogeneous effects of disasters on the risk-free and risky debt segments of credit markets. The results reveal that federal and private debt are ``two sides of the same coinâ€, call for a closer coordination between these two distinct sectors of the credit market, and shed light on deleveraging issues that likely lie ahead in the post-disaster world.

SSRN

Subsidiaries of a firm can use their reporting discretion for several goals, such as reporting earnings comparable to other subsidiaries or reporting earnings that are smooth over time. Prior theoretical work on reporting discretion recognizes the tension among these goals (Holmstrom, 1982; Demski and Sappington, 1974), but empirical work has not sufficiently examined it. This study exploits the bank holding company setting to investigate how subsidiaries use reporting discretion to navigate these competing objectives. I find that while subsidiaries use reporting discretion to smooth their own earnings, they also use reporting discretion to herd around the earnings of internal peers. Through a number of cross-sectional analyses, I find that this herding behavior appears consistent with relative performance evaluation motivations. These results provide new insight into prior mixed findings on the reporting choices of bank holding companies.

SSRN

Using a unique hand collected sample of professional connections between finance ministers and the directors and top executives of the three largest credit rating agencies for 38 European sovereigns between January 2000 and November 2017, we show that professional connections result in higher sovereign ratings. We find that the subjective component of ratings, captured by the current professional connection, has a more important role for developing than developed countries, whereby the average effect in developing European countries is estimated to be about 1.18, 0.38, and 0.80 notch by S&P, Moodyâ€™s and Fitch respectively. We also reveal that solicited sovereign ratings are significantly higher than unsolicited ratings.

arXiv

Although maximizing median and quantiles is intuitively appealing and has an axiomatic foundation, it is difficult to study the optimal portfolio strategy due to the discontinuity and time inconsistency in the objective function. We use the intra-personal equilibrium approach to study the problem. Interestingly, we find that the only viable outcome is from the median maximization, because for other quantiles either the equilibrium does not exist or there is no investment in the risky assets. The median maximization strategy gives a simple explanation to why wealthier people invest more percentage of their wealth in risky assets.

SSRN

Margin requirements protect a central counterparty (CCP) and its users against potential losses generated by the default of any of its members. They have several components, one of them being the initial margin requirement, which is typically calculated using a market risk model to estimate the potential future exposure of each member's portfolio. By definition, market risk models - whether for centrally cleared or bilateral cleared trades - have to be sensitive to changes in market risk and, as a consequence, when market risk increases, initial margin requirements will tend to increase. After the 2008 crisis, regulators had concerns about this becoming "procyclical", in the non-technical sense of amplifying financial stress. As a result, CCPs have put in place different procyclicality mitigation tools. But when the markets are stressed and participants face larger margin calls, like in the recent events of March 2020, interest on the procyclicality of initial margin models seems renewed. In this paper we argue that the focus on initial margin models is misplaced. First, margin calls are largely driven by variation margin, not initial margin. Second, the inherent risk sensitivity of margin models, the stochastic nature of the problem, and the different trade-offs involved, constrain what can be achieved with model calibration. We illustrate why this is the case by empirically testing the performance of standard initial margin models during the recent March 2020 events and quantifying the different trade-offs. Therefore, if procyclicality of IM has been mitigated to the limit of what is practical and prudent, but fragilities in the system persist, how should these be addressed and who is responsible for addressing them? We argue that, since the ultimate objective is to minimize systemic propensities to adverse feedback loops, these questions demand a systemic perspective, focusing on the interactions between participants rather than on a single node.

SSRN

This paper shows related guarantees increase corporate tunneling. The effect is more pronounce for firms with smaller size, higher liabilities, and poorer profitability. Implicit tunneling has a significant effect on the mechanism of tunneling and bank financing. However, individual tunneling shows a smaller and shorter term effect. Furthermore, economic development, time variant economic freedom, regional economic development, business-government relationship and government intervention are all significant variables that can explain variations in the magnitude of tunneling. Our results are robust in that we provide two Instrumental Variables that can further predict frauds of â€œunrelatedâ€ loans, implicit related transactions and textual analysis on corporate loan announcements.

arXiv

In this paper we investigate a utility maximization problem with drift uncertainty in a multivariate continuous-time Black-Scholes type financial market which may be incomplete. We impose a constraint on the admissible strategies that prevents a pure bond investment and we include uncertainty by means of ellipsoidal uncertainty sets for the drift. Our main results consist firstly in finding an explicit representation of the optimal strategy and the worst-case parameter, secondly in proving a minimax theorem that connects our robust utility maximization problem with the corresponding dual problem. Thirdly, we show that, as the degree of model uncertainty increases, the optimal strategy converges to a generalized uniform diversification strategy.

arXiv

We propose a data-driven portfolio selection model that integrates side information, conditional estimation and robustness using the framework of distributionally robust optimization. Conditioning on the observed side information, the portfolio manager solves an allocation problem that minimizes the worst-case conditional risk-return trade-off, subject to all possible perturbations of the covariate-return probability distribution in an optimal transport ambiguity set. Despite the non-linearity of the objective function in the probability measure, we show that the distributionally robust portfolio allocation with side information problem can be reformulated as a finite-dimensional optimization problem. If portfolio decisions are made based on either the mean-variance or the mean-Conditional Value-at-Risk criterion, the resulting reformulation can be further simplified to second-order or semi-definite cone programs. Empirical studies in the US and Chinese equity markets demonstrate the advantage of our integrative framework against other benchmarks.

SSRN

This paper aims to develop some static and conditional (dynamic) models to predict portfolio returns in the Borsa Istanbul (BIST) that are calibrated to combine the capital asset-pricing model (CAPM) and corporate governance quality. In our conditional model proposals, both the traditional CAPM (beta) coefficient and model constant are allowed to vary on a binary basis with any degradation or improvement in the countryâ€™s international trade competitiveness, and meanwhile a new variable is added to the models to represent the portfolioâ€™s sensitivity to excess returns on the governance portfolio (BIST Governance) over the market. Some robust and Bayesian linear models have been derived using the monthly capital gains between December 2009 and December 2019 of four leading index portfolios. A crude measure is then introduced that we think can be used in assessing governance quality of portfolios. This is called governance quality score (GQS). Our robust regression findings suggest both superiority of conditional models assuming varying beta coefficients over static model proposals and significant impact of corporate governance quality on portfolio returns. The Bayesian model proposals, however, exhibited robust findings that favor the static model with fixed beta estimates and were lacking in supporting significance of corporate governance quality.

SSRN

This paper studies the restrictions on consumption, portfolio choice, and social discounting implied by a sustainability constraint, that utility should not be expected to decline over time, in an economy with risky investment opportunities. The sustainability constraint does not distort portfolio choice and implies a consumption-wealth ratio and social discount rate that can be considerably higher than the riskless interest rate.

SSRN

Mutual fund risk-taking via active portfolio rebalancing varies both in the cross- section and over time. In this paper, I show that the same is true for funds' off- balance sheet risk-taking, even after controlling for on-balance sheet activities. For this purpose, I propose a novel measure of synthetic leverage, which can be estimated based on publicly available information. In the empirical application, I show that German equity funds have increased their risk-taking via synthetic leverage from mid-2015 up until early 2019. In the cross-section, I find that synthetically leveraged funds tend to underperform and display higher levels of fragility.

arXiv

This study examines the impact of technological leapfrogging on manufacturing value-added in SSA. The study utilizes secondary data spanning 1990 to 2018. The data is analyzed using cross-sectional autoregressive distributed lags (CS-ARDL) and cross-sectional distributed lags (CS-DL) techniques. The study found that technological leapfrogging is a positive driver of manufacturing value-added in SSA. This implies that SSA can copy the foreign technologies and adapt them for domestic uses, rather than going through the evolutionary process of the old technologies that are relatively less efficient. If the governments of SSA could reinforce their absorptive capacity and beef up productivity through proper utilization of the existing technology. The productive activities of the domestic firms will stir new innovations and discoveries that will eventually translate into indigenous technology

arXiv

Stock price prediction can be made more efficient by considering the price fluctuations and understanding the sentiments of people. A limited number of models understand financial jargon or have labelled datasets concerning stock price change. To overcome this challenge, we introduced FinALBERT, an ALBERT based model trained to handle financial domain text classification tasks by labelling Stocktwits text data based on stock price change. We collected Stocktwits data for over ten years for 25 different companies, including the major five FAANG (Facebook, Amazon, Apple, Netflix, Google). These datasets were labelled with three labelling techniques based on stock price changes. Our proposed model FinALBERT is fine-tuned with these labels to achieve optimal results. We experimented with the labelled dataset by training it on traditional machine learning, BERT, and FinBERT models, which helped us understand how these labels behaved with different model architectures. Our labelling method competitive advantage is that it can help analyse the historical data effectively, and the mathematical function can be easily customised to predict stock movement.

SSRN

Capital requirements are one of the key elements in banking regulation. As the failure of a systemically important institution poses a risk to the whole economy, they have to meet special regulations. Equity can contribute to the stability of a credit institution but also might limit its ability to fulfil its role as financial intermediary. As the availability of bank financing has a major influence on the performance of firms and economies, regulators might reluctantly implement new multinational capital requirements on a national level. Our research uses a unique data set to show the influence of economic factors on the capital buffer calibration for Other Systemically Important Insti-tutions (O-SIIs). We show that the scoring process to identify and rank O-SIIs is comparable, but the respective equity requirements are not. Nations with higher unemployment as well as a higher amount of non-performing loans demand less capital from their banks. Hence, their country average of capital buffer requirements per score depend on the economic situation rather than the scoring process as such.

SSRN

Urban structures and urban growth rates are highly persistent. This has far-reaching implications for the optimal size and timing of new construction. We prove that rational developers postpone construction not because prospects are gloomy, but because they are bright. The slow mean reversion in urban growth rates for the Netherlands and the United States (estimated at 0.07 per annum) implies that a substantial share of cities should optimally postpone construction due to high growth. Observed heterogeneity in floorspace density across cities can be explained not by differences in population levels, but in growth rates.

SSRN

We study the effects of central bank communication about financial stability on individuals' expectations and risk-taking. Using a randomized information experiment, we show that communication causally affects individuals' beliefs and investment behavior, consistent with an expectations channel of financial stability communication. Individuals receiving a warning from the central bank expect a higher probability of a financial crisis and reduce their demand for risky assets. This reduction is driven by downward revisions in individuals' expected Sharpe ratios due to lower expected returns and higher perceived downside risks. In addition, these individuals deposit a smaller fraction of their savings at riskier banks.

SSRN

This paper examines the relationship between wealth holdings and patterns of various household characteristics, including education, occupation, wealth portfolio structures and inheritance. The focus is on comparing the wealth levels of Black and White Americans, and relating differences in these levels to socio-economic characteristics. We find that a combination of inheritance, education and occupation is significantly related to differences in wealth levels. However, household characteristics such as education, homeownership or business ownership are not by themselves pathways to reducing wealth gaps, let alone eliminating them.

SSRN

Using survey evidence from European asset managers, we provide insights into their green bond investment activities and the factors that affect their investment decisions. We find that the majority of investors are actively invested in the green bond market via a variety of investment channels. Investors prefer green bonds issued from corporate issuers and sovereigns and we find that there is strong unmet investor demand for green bonds from these issuer types, in particular from non-financial corporates in the industrials, automotive and utilities sectors. Competitive pricing and strong green credentials, both pre- and post-issuance, are the most frequently named factors impacting respondentsâ€™ decision to invest in a green bond, and unclear and poor reporting on how bond proceeds are allocated to green projects induces a majority of investors to not invest in a green bond or to sell a bond if already included in the portfolio. Among policy measures to grow the green bond market, preferential capital treatment for low-carbon assets and minimum standards for green definitions receive the highest investor support, but respondents are divided whether a strict definition of â€˜greenâ€™ or a less strict definition would be more beneficial for scaling up the green bond market.