Research articles for the 2021-05-19

A Fully Quantization-based Scheme for FBSDEs
Giorgia Callegaro,Alessandro Gnoatto,Martino Grasselli

We propose a quantization-based numerical scheme for a family of decoupled FBSDEs. We simplify the scheme for the control in Pag\`es and Sagna (2018) so that our approach is fully based on recursive marginal quantization and does not involve any Monte Carlo simulation for the computation of conditional expectations. We analyse in detail the numerical error of our scheme and we show through some examples the performance of the whole procedure, which proves to be very effective in view of financial applications.

A Generalized Framework for Measuring Pedestrian Accessibility around the World Using Open Data
Shiqin Liu,Carl Higgs,Jonathan Arundel,Geoff Boeing,Nicholas Cerdera,David Moctezuma,Ester Cerin,Deepti Adlakha,Melanie Lowe,Billie Giles-Corti

Pedestrian accessibility is an important factor in urban transport and land use policy and critical for creating healthy, sustainable cities. Developing and evaluating indicators measuring inequalities in pedestrian accessibility can help planners and policymakers benchmark and monitor the progress of city planning interventions. However, measuring and assessing indicators of urban design and transport features at high resolution worldwide to enable city comparisons is challenging due to limited availability of official, high quality, and comparable spatial data, as well as spatial analysis tools offering customizable frameworks for indicator construction and analysis. To address these challenges, this study develops an open source software framework to construct pedestrian accessibility indicators for cities using open and consistent data. It presents a generalized method to consistently measure pedestrian accessibility at high resolution and spatially aggregated scale, to allow for both within- and between-city analyses. The open source and open data methods developed in this study can be extended to other cities worldwide to support local planning and policymaking. The software is made publicly available for reuse in an open repository.

Accounting and Finance: Capital and Cost in Economics
Lewin, Peter,Cachanosky, Nicolas
This paper discusses the implications of considering capital in financial or market value terms rather than as a collection of productive assets. We emphasize potential areas of research from this conception of capital.

An efficient Monte Carlo method for utility-based pricing
Laurence Carassus,Massinissa Ferhoune

We propose an efficient numerical method, based on the Lambert function, for the computation and study of the reservation price as well as the value function in the case of illiquidity. Our theoretical results are illustrated by numerical simulations.

Asset Allocation with Liquidity-Adjusted Market Risk Modeling: Empirical Relevance to Multiple-Asset Portfolios
Al Janabi, Mazin A. M.
In this paper, the author demonstrate a practical approach for measurement, management and control of market risk exposure for financial trading portfolios. This approach is based on the renowned concept of Liquidity-Adjusted Value at Risk (L-VaR) along with the creation of a software tool utilizing matrix-algebra technique under the notion of different correlation factors and liquidation horizons.

Banks and Negative Interest Rates
Heider, Florian,Saidi, Farzad,Schepens, Glenn
In this paper, we survey the nascent literature on the transmission of negative policy rates. We discuss the theory of how the transmission depends on bank balance sheets, and how this changes once policy rates become negative. We review the growing evidence that negative policy rates are special because the pass-through to banks’ retail deposit rates is hindered by a zero lower bound. We summarize existing work on the impact of negative rates on banks’ lending and securities portfolios, and the consequences for the real economy. Finally, we discuss the role of different “initial” conditions when the policy rate becomes negative, and potential interactions between negative policy rates and other unconventional monetary policies.

Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets
Martin Huber,Jonas Meier,Hannes Wallimann

We assess the demand effects of discounts on train tickets issued by the Swiss Federal Railways, the so-called `supersaver tickets', based on machine learning, a subfield of artificial intelligence. Considering a survey-based sample of buyers of supersaver tickets, we investigate which customer- or trip-related characteristics (including the discount rate) predict buying behavior, namely: booking a trip otherwise not realized by train, buying a first- rather than second-class ticket, or rescheduling a trip (e.g.\ away from rush hours) when being offered a supersaver ticket. Predictive machine learning suggests that customer's age, demand-related information for a specific connection (like departure time and utilization), and the discount level permit forecasting buying behavior to a certain extent. Furthermore, we use causal machine learning to assess the impact of the discount rate on rescheduling a trip, which seems relevant in the light of capacity constraints at rush hours. Assuming that (i) the discount rate is quasi-random conditional on our rich set of characteristics and (ii) the buying decision increases weakly monotonically in the discount rate, we identify the discount rate's effect among `always buyers', who would have traveled even without a discount, based on our survey that asks about customer behavior in the absence of discounts. We find that on average, increasing the discount rate by one percentage point increases the share of rescheduled trips by 0.16 percentage points among always buyers. Investigating effect heterogeneity across observables suggests that the effects are higher for leisure travelers and during peak hours when controlling several other characteristics.

Do Banks Fuel Climate Change?
Reghezza, Alessio,Altunbas, Yener,Marques-Ibanez, David,d’Acri, Costanza Rodriguez,Spaggiari, Martina
Do climate-oriented regulatory policies affect the flow of credit towards polluting corporations? We match loan-level data to firm-level greenhouse gas emissions to assess the impact of the Paris Agreement. We find that, following this agreement, European banks reallocated credit away from polluting firms. In the aftermath of President Trump’s 2017 announcement that the United States was withdrawing from the Paris Agreement, lending by European banks to polluting firms in the United States decreased even further in relative terms. It follows that green regulatory initiatives in banking can have a significant impact combating climate change.

Do Chinese Investors Underact to Goodwill
Li, Yongqing
This paper mainly studies goodwill as an intangible asset, and considers that goodwill and sales contain information about enterprise value. The results show that goodwill can predict the future return rate of China's stock market, indicating that investors in China’s stock market underact to information contained in goodwill. In addition, this paper also finds that the organizational structure of the company not only affects the merger behavior of the company, but also affects the predictability of the company's future stock returns. Specifically, in China, goodwill can only predict the future stock returns of non-state-owned enterprises, but not the future stock returns of state-owned enterprises. The difference is not due to the size of the company.

Financial System Regulation in a Pandemic: Evidence from Nigeria
Uddin, Godwin
Financial system soundness in world economies remains germane, but in the same vein, the COVID-19 outbreak had made governments scampering for any and every solution as experience has shown the need to incentivize businesses to enable economy-wide recovery. In this perspective, consideration of the Nigerian case is made, to re-echo possible collaboration by the Central Bank of Nigeria (CBN) and an operationally-associated agency - the Asset Management Corporation of Nigeria (AMCON). This viewpoint shows the role that AMCON could play to recoup extended facilities, in view to ensure financial system soundness, amidst others. Thus, efforts to leverage on this collaboration could aid going forward a fruitful operational effectiveness of so established policy responses.

Forecasting with fractional Brownian motion: a financial perspective
Matthieu Garcin

The fractional Brownian motion (fBm) extends the standard Brownian motion by introducing some dependence between non-overlapping increments. Consequently, if one considers for example that log-prices follow an fBm, one can exploit the non-Markovian nature of the fBm to forecast future states of the process and make statistical arbitrages. We provide new insights into forecasting an fBm, by proposing theoretical formulas for accuracy metrics relevant to a systematic trader, from the hit ratio to the expected gain and risk of a simple strategy. In addition, we answer some key questions about optimizing trading strategies in the fBm framework: Which lagged increments of the fBm, observed in discrete time, are to be considered? If the predicted increment is close to zero, up to which threshold is it more profitable not to invest? We also propose empirical applications on high-frequency FX rates, as well as on realized volatility series, exploring the rough volatility concept in a forecasting perspective.

How Optimistic and Pessimistic Narratives About COVID-19 Impact Economic Behavior
Harrs, Sören,Müller, Lara Marie,Rockenbach, Bettina
Politicians, scientists and journalists have aired vastly different assessments of the COVID-19 pandemic, ranging from rather optimistic to very pessimistic ones. In this paper we investigate how narratives conveying different assessments of the pandemic impact economic behavior. In a controlled experiment with incentivized economic games we find that subjects behave more risk averse and less patient when confronted with a pessimistic compared to an optimistic or balanced narrative. Further we find that narratives change subjects' expectations about the pandemic and the stock market. Hence our experiment provides causal evidence for an impact of narratives on fundamental determinants of household behavior.

Investing in Crises
Baron, Matthew,Laeven, Luc,Pénasse, Julien,Usenko, Yevhenii
We investigate asset returns around banking crises in 44 advanced and emerging economies from 1960 to 2018. In contrast to the view that buying assets during banking crises is a profitable long-run strategy, we find returns of equity and other asset classes generally underperform after banking crises. While prices are depressed during crises and partially recover after acute stress ends, consistent with theories of fire sales and intermediary-based asset pricing, we argue that investors do not fully anticipate the consequences of debt overhang, which result in lower long-run dividends. Our results on bank stock underperformance suggest that government-funded bank recapitalizations can often lead to substantial taxpayer losses.

Lending Relationships in Loan Renegotiation: Evidence from Corporate Loans
Papoutsi, Melina
This paper presents evidence that personal relationships between corporate borrowers and bank loan officers improve the outcomes of loan renegotiation. Analysing a bank reorganization in Greece in the mid-2010s, I find that firms that experience an exogenous interruption in their loan officer relationship confront three consequences: one, the firms are less likely to renegotiate their loans; two, conditional on renegotiation, the firms are given tougher loan terms; and three, the firms are more likely to alter their capital structure. These results point to the importance of lending relationships in mitigating the cost of distress for borrowers in loan renegotiations.

Managerial and Financial Barriers to the Net-Zero Transition
De Haas, Ralph,Martin, Ralf,Muûls, Mirabelle,Schweiger, Helena
We use data on 11,233 firms across 22 emerging markets to analyze how credit constraints and low-quality firm management inhibit corporate investment in green technologies. For identification we exploit quasi-exogenous variation in local credit conditions and in exposure to weather shocks. Our results suggest that both financial frictions and managerial constraints slow down firm investment in more energy efficient and less polluting technologies. Complementary analysis of data from the European Pollutant Release and Transfer Register (E-PRTR) corroborates some of this evidence by revealing that in areas where banks deleveraged more after the global financial crisis, industrial facilities reduced their carbon emissions by less. On aggregate this kept local emissions 15% above the level they would have been in the absence of financial frictions.

Market Risk Prediction under Illiquid Market Environments: A Comparison of Alternative Modeling Techniques
Al Janabi, Mazin A. M.
This paper bridges the gap in trading risk management literatures, and particularly from the perspective of emerging and illiquid markets. We find that under certain trading strategies, such as short-selling of stocks, the sensitivity of L-VaR statistics are rather critical to the selected internal asset liquidity model in addition to the degree of correlation factors among trading assets.

Microfinance Institution’s Dual Missions: Does Profit Status Matter?
Kim, Daehong,Lee, Kye Woo
This study aims to assess the impact of Microfinance Institution (MFI)’s profit status on their dual missions: poverty alleviation and financial sustainability. As the issue of financial sustainability and viability of MFIs has been raised in microfinance industry, more attention has been paid to transforming from non-profit entities to for-profits as a major solution to serve the impoverished with better financial services on a sustainable basis. Yet, this has been challenged with the label “mission drift” that the original mission serving the poorest of the poor for the poverty reduction would switch to targeting better-off clients to achieve financial profitability. Therefore, this study, based on the Dynamic Panel System GMM estimations, examines whether for-profit MFIs indeed more significantly contribute to reducing poverty at a financially self-sustainable basis in comparison with non-profit counterparts across 41 countries over 2008-2012. The results of the estimations indicate that for-profits were validated to be financially self-sustaining, but failed to help the poor alleviate the level of poverty. By contrast, non-profits were not able to operate in a financial sustainable way, whereas they served the poor segment of the population to reduce poverty. These findings suggest that the profit status of MFIs does really matter with the failure of achieving the dual missions in microfinance industry. In particular, it would not be possible to achieve the two missions when more priority is given to one over the other. Thus, it is recommended to take a balanced operational strategy between the poverty lending and financial systems approaches in microfinance industry, which would lead both non-profits and for-profits to play a significant role of ending extreme poverty as a sustainable financial instrument in a Post-2015 era.

Online Labour Index 2020: New ways to measure the world's remote freelancing market
Fabian Stephany,Otto Kässi,Uma Rani,Vili Lehdonvirta

The Online Labour Index (OLI) was launched in 2016 to measure the global utilisation of online freelance work at scale. Five years after its creation, the OLI has become a point of reference for scholars and policy experts investigating the online gig economy. As the market for online freelancing work matures, a high volume of data and new analytical tools allow us to revisit half a decade of online freelance monitoring and extend the index's scope to more dimensions of the global online freelancing market. In addition to measuring the utilisation of online labour across countries and occupations by tracking the number of projects and tasks posted on major English-language platforms, the new Online Labour Index 2020 (OLI 2020) also tracks Spanish- and Russian-language platforms, reveals changes over time in the geography of labour supply, and estimates female participation in the online gig economy. The rising popularity of software and tech work and the concentration of freelancers on the Indian subcontinent are examples of the insights that the OLI 2020 provides. The OLI 2020 delivers a more detailed picture of the world of online freelancing via an interactive online visualisation updated daily. It provides easy access to downloadable open data for policymakers, labour market researchers, and the general public (

Real Estate and Rental Markets During COVID Times
Iliopulos, Eleni,Achou, Bertrand,d'Albis, Hippolyte
In this work we introduce a general equilibrium model with landlords, indebted owner-occupiers and renters to study housing markets' dynamics. We estimate it by using standard Bayesian methods and match the US data of the last decades. This framework is particularly suited to explain current trends on housing markets. We highlight the crucial relationship between interest rates, house prices and rents, and argue that it helps understanding the main driving forces. Our analysis suggests that current developments on housing markets can play a role for a recovery from the Covid pandemic as they have an expansionary effect on aggregate output. Moreover, we account for the heterogeneous impact of crisis-induced policies depending on agents' status on the housing market. We show how, despite an increase in housing prices, the welfare of landlords has been negatively hit. This is associated to the joint decrease in returns on housing and financial assets that reduces their financial incomes.

Robo-Advising: Enhancing Investment with Inverse Optimization and Deep Reinforcement Learning
Haoran Wang,Shi Yu

Machine Learning (ML) has been embraced as a powerful tool by the financial industry, with notable applications spreading in various domains including investment management. In this work, we propose a full-cycle data-driven investment robo-advising framework, consisting of two ML agents. The first agent, an inverse portfolio optimization agent, infers an investor's risk preference and expected return directly from historical allocation data using online inverse optimization. The second agent, a deep reinforcement learning (RL) agent, aggregates the inferred sequence of expected returns to formulate a new multi-period mean-variance portfolio optimization problem that can be solved using deep RL approaches. The proposed investment pipeline is applied on real market data from April 1, 2016 to February 1, 2021 and has shown to consistently outperform the S&P 500 benchmark portfolio that represents the aggregate market optimal allocation. The outperformance may be attributed to the the multi-period planning (versus single-period planning) and the data-driven RL approach (versus classical estimation approach).

Taking Stock of Chapter 11
Skeel, David A.
In this Essay, written for a symposium honoring Sam Gerdano, I offer an assessment of current Chapter 11 theory and practice. The most distinctive feature of current Chapter 11 practice is the extent to which the parties now enter into intercreditor agreements, restructuring support agreements and other actual contracts governing their rights and responsibilities. One question raised by the dramatic shift in bankruptcy practice is whether the leading normative theory of bankruptcy, the Creditors’ Bargain Theory, is now obsolete, as some scholars have suggested. The Creditors’ Bargain Theory explains bankruptcy as a solution to coordination problems that might lead to the dismemberment of an otherwise viable firm if creditors were simply left to their own devices. Although the particular coordination problem foregrounded by the theoryâ€"the collective action problem faced by widely scattered unsecured creditorsâ€"no longer characterizes most Chapter 11 cases, I argue that this bargaining failure has been replaced by others to which the same logic applies.The theory provides a much more complete picture of why bankruptcy is necessary than of the optimal framework for resolving financial distress. In current practice, the key question is how to make sense of the contracts that now govern the Chapter 11 process. Building on earlier work with George Triantis, I argue that the principal objective should be to distinguish between ex ante and ex post agreements, and to balance the costs and benefits of each. I consider the implications of this perspective for debtor-in-possession financing and managerial bonuses.

The Disciplining Effect of Supervisory Scrutiny in the EU-wide Stress Test
Kok, Christoffer,Müller, Carola,Ongena, Steven,Pancaro, Cosimo
Using a difference-in-differences approach and relying on confidential supervisory data and an unique proprietary data set available at the European Central Bank related to the 2016 EU-wide stress test, this paper presents novel empirical evidence that supervisory scrutiny associated to stress testing has a disciplining effect on bank risk. We find that banks that participated in the 2016 EU-wide stress test subsequently reduced their credit risk relative to banks that were not part of this exercise. Relying on new metrics for supervisory scrutiny that measure the quantity, potential impact, and duration of interactions between banks and supervisors during the stress test, we find that the disciplining effect is stronger for banks subject to more intrusive supervisory scrutiny during the exercise.

The SINC way: A fast and accurate approach to Fourier pricing
Fabio Baschetti,Giacomo Bormetti,Silvia Romagnoli,Pietro Rossi

The goal of this paper is to investigate the method outlined by one of us (PR) in Cherubini et al. (2009) to compute option prices. We name it the SINC approach. While the COS method by Fang and Osterlee (2009) leverages the Fourier-cosine expansion of truncated densities, the SINC approach builds on the Shannon Sampling Theorem revisited for functions with bounded support. We provide several results which were missing in the early derivation: i) a rigorous proof of the convergence of the SINC formula to the correct option price when the support grows and the number of Fourier frequencies increases; ii) ready to implement formulas for put, Cash-or-Nothing, and Asset-or-Nothing options; iii) a systematic comparison with the COS formula for several log-price models; iv) a numerical challenge against alternative Fast Fourier specifications, such as Carr and Madan (1999) and Lewis (2000); v) an extensive pricing exercise under the rough Heston model of Jaisson and Rosenbaum (2015); vi) formulas to evaluate numerically the moments of a truncated density. The advantages of the SINC approach are numerous. When compared to benchmark methodologies, SINC provides the most accurate and fast pricing computation. The method naturally lends itself to price all options in a smile concurrently by means of Fast Fourier techniques, boosting fast calibration. Pricing requires to resort only to odd moments in the Fourier space. A previous version of this manuscript circulated with the title `Rough Heston: The SINC way'.

The Value of Marriage to Family Firms
Wiwattanakantang, Yupana,Bunkanwanicha, Pramuan,Fan, Joseph P. H.
This paper presents the first empirical evidence showing that the marriage of a member of the controlling family adds value to public corporations. The results, based on a uniquely comprehensive data set from Thailand, show that the family firm’s stock price increases when the partner is from either a prominent business or a political family. Abnormal returns tend to be higher for firms whose operation depends on extensive networks. In contrast, marriages to ordinary citizens are not associated with any abnormal returns. These findings are generally supportive of the value of networks in general and marriage in particular.

Using four different online media sources to forecast the crude oil price
M. Elshendy,A. Fronzetti Colladon,E. Battistoni,P. A. Gloor

This study looks for signals of economic awareness on online social media and tests their significance in economic predictions. The study analyses, over a period of two years, the relationship between the West Texas Intermediate daily crude oil price and multiple predictors extracted from Twitter, Google Trends, Wikipedia, and the Global Data on Events, Language, and Tone database (GDELT). Semantic analysis is applied to study the sentiment, emotionality and complexity of the language used. Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) models are used to make predictions and to confirm the value of the study variables. Results show that the combined analysis of the four media platforms carries valuable information in making financial forecasting. Twitter language complexity, GDELT number of articles and Wikipedia page reads have the highest predictive power. This study also allows a comparison of the different fore-sighting abilities of each platform, in terms of how many days ahead a platform can predict a price movement before it happens. In comparison with previous work, more media sources and more dimensions of the interaction and of the language used are combined in a joint analysis.

When It Comes to the Crunch: Retail Investor Decision-Making During Periods of Market Volatility
Brooks, Chris,Williams, Louis
Attitude to risk questionnaires are widely used by financial advisors to recommend investments of appropriate risk levels to their clients. Yet the usefulness of this instrument to gauge how investors will react when faced with extreme volatility in the values of their assets remains untested. Using realistic scenarios and based on a large-scale survey in the UK, in this study we examine how the investing public reacts to actual portfolio losses. We find that conventional risk tolerance measures are inadequate for determining whether investors would 'sell out' or hold their portfolios in such circumstances. On the other hand, we find that past experience, emotions and personality characteristics, including measures of financial self-efficacy and extraversion, are significant predictors of investor reactions to market crashes.

While Stability Lasts: A Stochastic Model of Stablecoins
Ariah Klages-Mundt,Andreea Minca

The `Black Thursday' crisis in cryptocurrency markets demonstrated deleveraging risks in over-collateralized lending and stablecoins. We develop a stochastic model of over-collateralized stablecoins that helps explain such crises. In our model, the stablecoin supply is decided by speculators who optimize the profitability of a leveraged position while incorporating the forward-looking cost of collateral liquidations, which involves the endogenous price of the stablecoin. We formally characterize regimes that are interpreted as stable and unstable for the stablecoin. We prove bounds on the probabilities of large deviations and quadratic variation in the stable domain and distinctly greater price variance in the unstable domain. The unstable domain can be triggered by large deviations, collapsed expectations, or liquidity problems from deleveraging. We formally characterize a deflationary deleveraging spiral as a submartingale that can cause the system to behave in counterintuitive ways due to liquidity problems in a crisis. These deleveraging spirals, which resemble short squeezes, lead to faster collateral drawdown (and potential shortfalls) and are accompanied by higher price variance, as experienced on Black Thursday. We also demonstrate `perfect' stability results in idealized settings and discuss mechanisms which could bring realistic settings closer to such idealized stable settings.