Research articles for the 2020-06-15

A Study on the Initiatives Taken by Banker’s for Promoting Financial Literacy in Bangalore, India
Yadav, Sachita
The goal of achieving sustainable development of any country begins with the financial well being of its citizens. When the population is well connected & well informed about the financial services that a country can offer, they are in a stable position to manage & maximize their income. A survey conducted by NCFE (National Centre for Financial Education) in 2014-15 shows that only 11% of the population in India is financially included within the framework of the economy. Banks are the facilitators & initiators of financial services in an economy. They are uniquely positioned to impart financial education to the general public. After the initiation of ‘Project Financial Literacy ‘in 2012 & introduction of ‘Pradhan Mantri Jan Dhan Yojana’ in 2014, India’s Financial Literacy landscape is changing. Our study aims to bring out the details of initiatives taken by bankers to promote Financial Literacy in Bangalore City, India. The study uses primary data to analyze the initiatives taken by bankers to promote financial literacy in Bangalore, India. Secondary data from RBI websites, journals was used in studying the Financial Literacy programme implemented by RBI & till what extent do banks follow the guidelines under the purview of RBI. The empirical analysis of the study resulted that the banks have focused on running various seminars in schools & colleges to encourage students and youth to open bank a/c, teach digital banking and awareness regarding various banking services. Our study showed that emphasis on banking services such transaction, withdrawals, deposits etc dominated 22% of the themes taught in the programme. Savings (22%) was the next most popular theme covered during the financial literacy programs undertaken by banks in India.

A Systematic Literature Review on Shari’ah Governance Mechanism and Firm Performance in Islamic Banking
Nomran, Naji Mansour,Haron, Razali
urpose â€" This paper aims to systematically review the existing studies on the relationship of Shari’ah governance (SG), as represented by the Shari’ah supervisory board (SSB), with firm performance of Islamic banks (IBs), to suggest opportunities for future research in this field. Design/methodology/approach â€" By adopting a systematic literature review, 21 empirical and theoretical papers published in Scopus concerning the relationship between SSB and performance of IBs were selected for review and analysis. Findings â€" In light of the existing research studies’ limitations, this paper suggests that the effect of SSB on IBs’ performance still requires more empirical analyses using alternative analytical methods, alternative measures, and different periods (during crisis and non-crisis). Besides that, these studies should take into account the differences across jurisdictions in their SG models, the degree of agencies’ intervention in SG practices, the control over cross-memberships of scholars, and the differences across IBs in the position of SSB in the organization structure. Practical implications â€" The analysis undertaken in this paper would address the literature gaps on the effect of SSB on IBs’ performance as this study serves as a guide for the researchers, academicians, and interested researchers from Islamic international autonomous non-for-profit organizations, e.g. AAOIFI and IFSB in research related to this important area. Importantly, the findings of this study would support regulators and related authorities across jurisdictions with suggestions on improving the current SG practices. Originality/value â€" This paper presents a critical review of the existing research on SSB and IB performance and suggests new variables, measurements, analytical methods, and new issues for researchers in this area. Thus, it identifies the literature gap that still needs further empirical investigation and a suitable way to close it.

A fixed-point policy-iteration-type algorithm for symmetric nonzero-sum stochastic impulse control games
Diego Zabaljauregui

Nonzero-sum stochastic differential games with impulse controls offer a realistic and far-reaching modelling framework for applications within finance, energy markets, and other areas, but the difficulty in solving such problems has hindered their proliferation. Semi-analytical approaches make strong assumptions pertaining to very particular cases. To the author's best knowledge, the only numerical method in the literature is the heuristic one we put forward to solve an underlying system of quasi-variational inequalities. Focusing on symmetric games, this paper presents a simpler, more precise and efficient fixed-point policy-iteration-type algorithm which removes the strong dependence on the initial guess and the relaxation scheme of the previous method. A rigorous convergence analysis is undertaken with natural assumptions on the players strategies, which admit graph-theoretic interpretations in the context of weakly chained diagonally dominant matrices. A novel provably convergent single-player impulse control solver is also provided. The main algorithm is used to compute with high precision equilibrium payoffs and Nash equilibria of otherwise very challenging problems, and even some which go beyond the scope of the currently available theory.

An Empirical Analysis of Long-Term Brazilian Interest Rates
Akram, Tanweer,Uddin, Syed Al-Helal
This paper empirically models the dynamics of Brazilian government bond (BGB) yields based on monthly macroeconomic data in the context of the evolution of Brazil’s key macroeconomic variables. The results show that the current short-term interest rate has a decisive influence on BGBs’ long-term interest rates after controlling for various key macroeconomic variables, such as inflation and industrial production or economic activity. These findings support John Maynard Keynes’s claim that the central bank’s actions influence the long-term interest rate on government bonds mainly through the short-term interest rate. These findings have important policy implications for Brazil. This paper relates the findings of the estimated models to ongoing debates in fiscal and monetary policies.

An Inconvenient Fact: Private Equity Returns & The Billionaire Factory
Phalippou, Ludovic
Private Equity (PE) funds have returned about the same as public equity indices since at least 2006. Large public pension funds have received a net Multiple of Money (MoM) that sits within a narrow 1.51 to 1.54 range. The big four PE firms have also delivered estimated net MoMs within a narrow 1.54 to 1.67 range. Three large datasets show average net MoMs across all PE funds at 1.55, 1.57 and 1.63. These net MoMs imply an 11% p.a. return, which matches relevant public equity indices; a result confirmed by PME calculations. Yet, the estimated total performance-related fee collected by these PE funds is estimated to be $230 billion, most of which goes to a relatively small number of individuals. The number of PE multibillionaires rose from 3 in 2005 to over 22 in 2020. Rebuttals from the big four and the main industry lobby body are provided and discussed.

Anticipation of Consequences & Sectoral Impact of COVID-19 â€" An Indian Outlook
Jasuja, Deepmala,Sharma, Preeti
The outbreak of the new coronavirus respiratory disease COVID-19 has left the entire world shattered for which no vaccines and advanced treatment is available. WHO has declared this disease as pandemic causing panic and concerns for human health around the world. This virus knocked in India, when India reported its first case on January 30, 2020 in Kerala and till then there is no full stop. Indian economy has been kept on “pause” mode since ever Shri Narendra Modi, has declared nationwide lockdown to prevent the outburst of this pandemic. Beginning from Stock market to other financial markets to various sectors, this has left nothing untouched. The real magnitude of the economic impact would depend on severity of the situation, public health and lifting up of lockdown. So, the researcher has attempted to show the effect of COVID-19 on different sectors of Indian economy and analyze the risk and returns during the pandemic period along measuring volatility. For this purpose, closing stock prices of BSE SENSEX and various other sectoral indices have been taken for the period of 2nd December to 28th April 2020. The comparative sudy uses the basic descriptive, ANOVA and CAPM model in current study.Garch model has been applied to check the volatility.

Backward Deep BSDE Methods and Applications to Nonlinear Problems
Yajie Yu,Bernhard Hientzsch,Narayan Ganesan

In this paper, we present a backward deep BSDE method applied to Forward Backward Stochastic Differential Equations (FBSDE) with given terminal condition at maturity that time-steps the BSDE backwards. We present an application of this method to a nonlinear pricing problem - the differential rates problem. To time-step the BSDE backward, one needs to solve a nonlinear problem. For the differential rates problem, we derive an exact solution of this time-step problem and a Taylor-based approximation. Previously backward deep BSDE methods only treated zero or linear generators. While a Taylor approach for nonlinear generators was previously mentioned, it had not been implemented or applied, while we apply our method to nonlinear generators and derive details and present results. Likewise, previously backward deep BSDE methods were presented for fixed initial risk factor values $X_0$ only, while we present a version with random $X_0$ and a version that learns portfolio values at intermediate times as well. The method is able to solve nonlinear FBSDE problems in high dimensions.

Bank Risk Determinants in Latin America
Baselga-Pascual, Laura,Martínez-Malvar, Mariña
Systemic Banking crises are a recurrent phenomenon that affects society, and there is a need for a better understanding of the risk factors to support prudential regulation and reduce unnecessary risk intake in the financial system. This paper examines the main bank risk determinants in Latin America. The period analysed covers the timespan from 1999 to 2013, including the systemic banking crisis episodes in Argentina (2001-2003) and Uruguay (2002-2005). We apply a new data-driven comparable methodology to classify and select commercial banks from the sample. We study bank risk proxied by the Z-score. In the analysis, we apply bank specific, macroeconomic and regulatory variables. We use the system-GMM estimator as our main empirical analysis method. Our results show negative relationships between the profitability and the liquidity of a bank and its risk and a positive relationship between bank asset quality and its risk. However, we find a negative correlation between good management and bank risk. We perform several robustness tests by applying alternative methodologies, and the results are similar to those our original model.

COVID-19 and Global Economic Growth: Policy Simulations with a Pandemic-Enabled Neoclassical Growth Model
Ian M. Trotter,Luís A. C. Schmidt,Bruno C. M. Pinto,Andrezza L. Batista,Jéssica Pellenz,Maritza Isidro,Aline Rodrigues,Attawan G. S. Suela,Loredany Rodrigues

During the COVID-19 pandemic of 2019/2020, authorities have used temporary ad-hoc policy measures, such as lockdowns and mass quarantines, to slow its transmission. However, the consequences of widespread use of these unprecedented measures are poorly understood. To contribute to the understanding of the economic and human consequences of such policy measures, we therefore construct a mathematical model of an economy under the impact of a pandemic, select parameter values to represent the global economy under the impact of COVID-19, and perform numerical experiments by simulating a large number of possible policy responses. By varying the starting date of the policy intervention in the simulated scenarios, we find that the most effective policy intervention occurs around the time when the number of active infections is growing at its highest rate -- that is, the results suggest that the most severe measures should only be implemented when the disease is sufficiently spread. The intensity of the intervention, above a certain threshold, does not appear to have a great impact on the outcomes in our simulations, due to the strongly concave relationship that we identify between production shortfall and infection rate reductions. Our experiments further suggest that the intervention should last until after the peak established by the reduced infection rate, which implies that stricter policies should last longer. The model and its implementation, along with the general insights from our policy experiments, may help policymakers design effective emergency policy responses in the face of a serious pandemic, and contribute to our understanding of the relationship between the economic growth and the spread of infectious diseases.

COVID-19 causes record decline in global CO2 emissions
Zhu Liu,Philippe Ciais,Zhu Deng,Ruixue Lei,Steven J. Davis,Sha Feng,Bo Zheng,Duo Cui,Xinyu Dou,Pan He,Biqing Zhu,Chenxi Lu,Piyu Ke,Taochun Sun,Yuan Wang,Xu Yue,Yilong Wang,Yadong Lei,Hao Zhou,Zhaonan Cai,Yuhui Wu,Runtao Guo,Tingxuan Han,Jinjun Xue,Olivier Boucher,Eulalie Boucher,Frederic Chevallier,Yimin Wei,Haiwang Zhong,Chongqing Kang,Ning Zhang,Bin Chen,Fengming Xi,François Marie,Qiang Zhang,Dabo Guan,Peng Gong,Daniel M. Kammen,Kebin He,Hans Joachim Schellnhuber

The considerable cessation of human activities during the COVID-19 pandemic has affected global energy use and CO2 emissions. Here we show the unprecedented decrease in global fossil CO2 emissions from January to April 2020 was of 7.8% (938 Mt CO2 with a +6.8% of 2-{\sigma} uncertainty) when compared with the period last year. In addition other emerging estimates of COVID impacts based on monthly energy supply or estimated parameters, this study contributes to another step that constructed the near-real-time daily CO2 emission inventories based on activity from power generation (for 29 countries), industry (for 73 countries), road transportation (for 406 cities), aviation and maritime transportation and commercial and residential sectors emissions (for 206 countries). The estimates distinguished the decline of CO2 due to COVID-19 from the daily, weekly and seasonal variations as well as the holiday events. The COVID-related decreases in CO2 emissions in road transportation (340.4 Mt CO2, -15.5%), power (292.5 Mt CO2, -6.4% compared to 2019), industry (136.2 Mt CO2, -4.4%), aviation (92.8 Mt CO2, -28.9%), residential (43.4 Mt CO2, -2.7%), and international shipping (35.9Mt CO2, -15%). Regionally, decreases in China were the largest and earliest (234.5 Mt CO2,-6.9%), followed by Europe (EU-27 & UK) (138.3 Mt CO2, -12.0%) and the U.S. (162.4 Mt CO2, -9.5%). The declines of CO2 are consistent with regional nitrogen oxides concentrations observed by satellites and ground-based networks, but the calculated signal of emissions decreases (about 1Gt CO2) will have little impacts (less than 0.13ppm by April 30, 2020) on the overserved global CO2 concertation. However, with observed fast CO2 recovery in China and partial re-opening globally, our findings suggest the longer-term effects on CO2 emissions are unknown and should be carefully monitored using multiple measures.

Carbon Monitor: a near-real-time daily dataset of global CO2 emission from fossil fuel and cement production
Zhu Liu,Philippe Ciais,Zhu Deng,Steven J. Davis,Bo Zheng,Yilong Wang,Duo Cui,Biqing Zhu,Xinyu Dou,Piyu Ke,Taochun Sun,Rui Guo,Olivier Boucher,Francois-Marie Breon,Chenxi Lu,Runtao Guo,Eulalie Boucher,Frederic Chevallier

We constructed a near-real-time daily CO2 emission dataset, namely the Carbon Monitor, to monitor the variations of CO2 emissions from fossil fuel combustion and cement production since January 1st 2019 at national level with near-global coverage on a daily basis, with the potential to be frequently updated. Daily CO2 emissions are estimated from a diverse range of activity data, including: hourly to daily electrical power generation data of 29 countries, monthly production data and production indices of industry processes of 62 countries/regions, daily mobility data and mobility indices of road transportation of 416 cities worldwide. Individual flight location data and monthly data were utilised for aviation and maritime transportation sectors estimates. In addition, monthly fuel consumption data that corrected for daily air temperature of 206 countries were used for estimating the emissions from commercial and residential buildings. This Carbon Monitor dataset manifests the dynamic nature of CO2 emissions through daily, weekly and seasonal variations as influenced by workdays and holidays, as well as the unfolding impacts of the COVID-19 pandemic. The Carbon Monitor near-real-time CO2 emission dataset shows a 7.8% decline of CO2 emission globally from Jan 1st to Apr 30th in 2020 when compared with the same period in 2019, and detects a re-growth of CO2 emissions by late April which are mainly attributed to the recovery of economy activities in China and partial easing of lockdowns in other countries. Further, this daily updated CO2 emission dataset could offer a range of opportunities for related scientific research and policy making.

Central Bank Digital Currency: When Price and Bank Stability Collide
Fernández-Villaverde, Jesús,Sanches, Daniel R.,Schilling, Linda,Uhlig, Harald
An account-based central bank digital currency has the potential to replace demand-deposits in private banks. In that case, the central bank invests in the real economy and takes over the role of maturity transformation to allow risk-sharing among depositors. Her function as intermediary exposes the CB to demand-liquidity or 'spending' shocks by her depositors. Since demand-deposit contracts are nominal, high aggregate spending not necessarily demands excessive liquidation of real investment by the central bank. A run on a central bank can, therefore, manifest itself either as a standard run characterized by excessive real asset liquidation (rationing) or as a run on the price level where a small supply of real goods meets a high demand. The central bank thus trades off price stability against the excessive liquidation of real goods.

Do Star Analysts Shine in Opaque Industries? Evidence from European Banking
d'Udekem, Benoit
We assess whether skills allow sell-side analysts to mitigate the opaqueness of a whole industry. We rely on a sample of 5,150 recommendation changes relating to 80 European banks during a period that encompasses the financial and euro-zone crises. In contrast with other industries, recommendation changes made by talented analysts are not likelier to influence bank stock prices, as if opaqueness impedes analyst influence. However, these changes induce sharp negative revaluations of bank stock prices. Our results suggest that stars remain influential by focusing on selected large, complex institutions and being apt at identifying mis-pricings and uncovering bad news.

Does Share Pledge Business Bias Analyst Recommendations?
Li, Maoliang,Luo, Juan,Xu, Limin,Zurbruegg, Ralf
This paper investigates whether analysts issue biased opinions on stocks that have been pledged to their brokerage house. We find that, relative to recommendations issued by independent analysts, affiliated analysts under brokerage pressure to promote share pledge business exhibit an optimistic bias in their recommendations. Our results are robust when using firm-year-quarter fixed effects in our regressions, when we control for other business connections that might exist between the broker and the firm and also for when we conduct an exogenous shock test relating to stocks that become shortable. We further find that our main result is more pronounced when pledged shares experience lower growth in earnings, when pledged share prices drop closer to margin call levels, when pledged shares have higher crash risk, and when the amount of lending backed by pledged shares is larger. Pledge business connected analysts are also more likely to issue favorable recommendations when reputation costs are low, when firms are hard to value, and when firms are dominated by retail investors. Overall, our results shed light on the tangible impact that the conflict of interest affiliated analysts have in posting stock recommendations for share-pledged firms.

Evidence of Crowding on Russell 3000 Reconstitution Events
Alessandro Micheli,Eyal Neuman

We develop a methodology which replicates in great accuracy the FTSE Russell indexes reconstitutions, including the quarterly rebalancings due to new initial public offerings (IPOs). While using only data available in the CRSP US Stock database for our index reconstruction, we demonstrate the accuracy of this methodology by comparing it to the original Russell US indexes for the time period between 1989 to 2019. A python package that generates the replicated indexes is also provided.

As an application, we use our index reconstruction protocol to compute the permanent and temporary price impact on the Russell 3000 annual additions and deletions, and on the quarterly additions of new IPOs . We find that the index portfolios following the Russell 3000 index and rebalanced on an annual basis are overall more crowded than those following the index on a quarterly basis. This phenomenon implies that transaction costs of indexing strategies could be significantly reduced by buying new IPOs additions in proximity to quarterly rebalance dates.

First exit-time analysis for an approximate Barndorff-Nielsen and Shephard model with stationary self-decomposable variance process
Shantanu Awasthi,Indranil SenGupta

In this paper, an approximate version of the Barndorff-Nielsen and Shephard model, driven by a Brownian motion and a L\'evy subordinator, is formulated. The first-exit time of the log-return process for this model is analyzed. It is shown that with certain probability, the first-exit time process of the log-return is decomposable into the sum of the first exit time of the Brownian motion with drift, and the first exit time of a L\'evy subordinator with drift. Subsequently, the probability density functions of the first exit time of some specific L\'evy subordinators, connected to stationary, self-decomposable variance processes, are studied. Analytical expressions of the probability density function of the first-exit time of three such L\'evy subordinators are obtained in terms of various special functions. The results are implemented to empirical S&P 500 dataset.

Green hydrogen: optimal supply chains and power sector benefits
Fabian Stöckl,Wolf-Peter Schill,Alexander Zerrahn

Green hydrogen can help to decarbonize transportation, but its power sector interactions are not well understood. It may contribute to integrating variable renewable energy sources if production is sufficiently flexible in time. Using an open-source co-optimization model of the power sector and four options for supplying hydrogen at German filling stations, we find a trade-off between energy efficiency and temporal flexibility: for lower shares of renewables and hydrogen, more energy-efficient and less flexible small-scale on-site electrolysis is optimal. For higher shares of renewables and/or hydrogen, more flexible but less energy-efficient large-scale hydrogen supply chains gain importance as they allow disentangling hydrogen production from demand via storage. Liquid hydrogen emerges as particularly beneficial, followed by liquid organic hydrogen carriers and gaseous hydrogen. Large-scale hydrogen supply chains can deliver substantial power sector benefits, mainly through reduced renewable surplus generation. Energy modelers and system planners should consider the distinct flexibility characteristics of hydrogen supply chains in more detail when assessing the role of green hydrogen in future energy transition scenarios.

Hidden Markov Models Applied To Intraday Momentum Trading With Side Information
Hugh Christensen,Simon Godsill,Richard Turner

A Hidden Markov Model for intraday momentum trading is presented which specifies a latent momentum state responsible for generating the observed securities' noisy returns. Existing momentum trading models suffer from time-lagging caused by the delayed frequency response of digital filters. Time-lagging results in a momentum signal of the wrong sign, when the market changes trend direction. A key feature of this state space formulation, is no such lagging occurs, allowing for accurate shifts in signal sign at market change points. The number of latent states in the model is estimated using three techniques, cross validation, penalized likelihood criteria and simulation-based model selection for the marginal likelihood. All three techniques suggest either 2 or 3 hidden states. Model parameters are then found using Baum-Welch and Markov Chain Monte Carlo, whilst assuming a single (discretized) univariate Gaussian distribution for the emission matrix. Often a momentum trader will want to condition their trading signals on additional information. To reflect this, learning is also carried out in the presence of side information. Two sets of side information are considered, namely a ratio of realized volatilities and intraday seasonality. It is shown that splines can be used to capture statistically significant relationships from this information, allowing returns to be predicted. An Input Output Hidden Markov Model is used to incorporate these univariate predictive signals into the transition matrix, presenting a possible solution for dealing with the signal combination problem. Bayesian inference is then carried out to predict the securities $t+1$ return using the forward algorithm. Simple modifications to the current framework allow for a fully non-parametric model with asynchronous prediction.

Human Capital Efficiency and Equity Funds’ Performance during the COVID-19 Pandemic
Yarovaya, Larisa,Mirza, Nawazish,Abaidi, Jamila,Hasnaoui, Amir
The paper investigates the impact of human capital efficiency (HCE) on equity funds’ performance during three stages of the COVID-19 pandemic. We collected data for 799 open-ended equity funds across five EU countries and ranked them in five categories of HCE and compare their risk-adjusted performance across these categories. The results suggest that during the COVID-19 outbreak, the equity funds that were ranked higher in HCE outperformed their counterparts. We suggest that fund managers should invest in human capital to improve funds’ coping ability and resilience during periods of extreme stress.

Investor Attention and the Response of US Stock Sectors to the COVID-19 Crisis
Smales, Lee A.
COVID-19 has had an immense impact on global stock markets, with no sector escaping its effects. Investor attention toward COVID-19 surged as the virus spread and its consequences imposed on everyday life. Using Google search volume (GSV) as a proxy for retail investor attention, our results show that heightened attention towards COVID-19 negatively influences US stock returns. However, relatively speaking, some sectors appear to have gained from the increased attention. This outperformance is centred in the sectors most likely to benefit (or likely to lose least) from the crisis and associated spending by households and government (i.e. Consumer Staples, Healthcare, and I.T.). Such results may be explained by an information discovery hypothesis in the sense that retail investors are searching online for information to enable a greater understanding of COVID-19’s impact on stock performance.

Italia 1 Trim 2020: Pil, Debito & Co (Italy 1Q 2020: GDP, Debt & Co.)
Mazziero, Maurizio,Lawford, Andrew,Serafini, Gabriele
Italian Abstract: Ricerca sulla situazione economica italiana basata sui dati economici ufficiali; vengono analizzati e confrontati con il passato il debito pubblico, le riserve ufficiali, il PIL, l'inflazione e la disoccupazione. English Abstract: Research into the state of the Italian economy based on official economic data; the current Sovereign Debt, Official Reserves, GDP, Inflation and Unemployment situation is presented and and compared with the past.Note: Downloadable document is in Italian.

Lifetime Ruin under HWM Fees and Drift Uncertainty
Junbeom Lee,Xiang Yu,Chao Zhou

This paper aims to make a new contribution to the study of lifetime ruin problem by considering investment in two hedge funds with high-watermark fees and drift uncertainty. Due to multi-dimensional performance fees that are charged whenever each fund profit exceeds its historical maximum, the value function is expected to be multi-dimensional. New mathematical challenges arise as the standard dimension reduction cannot be applied, and the convexity of the value function and Isaacs condition may not hold in our ruin probability minimization problem with drift uncertainty. We propose to employ the stochastic Perron's method to characterize the value function as the unique viscosity solution to the associated Hamilton Jacobi Bellman (HJB) equation without resorting to the proof of dynamic programming principle. The required comparison principle is also established in our setting to close the loop of stochastic Perron's method.

Loss Rate Forecasting Framework Based on Macroeconomic Changes: Application to US Credit Card Industry
Sajjad Taghiyeh,David C Lengacher,Robert B Handfield

A major part of the balance sheets of the largest US banks consists of credit card portfolios. Hence, managing the charge-off rates is a vital task for the profitability of the credit card industry. Different macroeconomic conditions affect individuals' behavior in paying down their debts. In this paper, we propose an expert system for loss forecasting in the credit card industry using macroeconomic indicators. We select the indicators based on a thorough review of the literature and experts' opinions covering all aspects of the economy, consumer, business, and government sectors. The state of the art machine learning models are used to develop the proposed expert system framework. We develop two versions of the forecasting expert system, which utilize different approaches to select between the lags added to each indicator. Among 19 macroeconomic indicators that were used as the input, six were used in the model with optimal lags, and seven indicators were selected by the model using all lags. The features that were selected by each of these models covered all three sectors of the economy. Using the charge-off data for the top 100 US banks ranked by assets from the first quarter of 1985 to the second quarter of 2019, we achieve mean squared error values of 1.15E-03 and 1.04E-03 using the model with optimal lags and the model with all lags, respectively. The proposed expert system gives a holistic view of the economy to the practitioners in the credit card industry and helps them to see the impact of different macroeconomic conditions on their future loss.

Mean Field Exponential Utility Game: A Probabilistic Approach
Guanxing Fu,Xizhi Su,Chao Zhou

We study an $N$-player and a mean field exponential utility game. Each player manages two stocks; one is driven by an individual shock and the other is driven by a common shock. Moreover, each player is concerned not only with her own terminal wealth but also with the relative performance of her competitors. We use the probabilistic approach to study these two games. We show the unique equilibrium of the $N$-player game and the mean field game can be characterized by a novel multi-dimensional FBSDE with quadratic growth and a novel mean-field FBSDEs, respectively. The well-posedness result and the convergence result are established.

Model Secrecy and Stress Tests
Leitner, Yaron,Williams, Basil
We study whether regulators should reveal the models they use to stress test banks. In our setting, revealing leads to gaming, but not revealing can induce banks to under-invest in socially desirable assets for fear of failing the test. We show that although the regulator can solve this under-investment problem by making the test easier, some disclosure may still be optimal, which under some conditions takes the simple form of a cutoff rule. We characterize the optimal disclosure policy combined with test difficulty, provide comparative statics, and relate our results to recent policies. We also offer applications beyond stress tests.

Modeling Joint Lives within Families
Olivier Cabrignac,Arthur Charpentier,Ewen Gallic

Family history is usually seen as a significant factor insurance companies look at when applying for a life insurance policy. Where it is used, family history of cardiovascular diseases, death by cancer, or family history of high blood pressure and diabetes could result in higher premiums or no coverage at all. In this article, we use massive (historical) data to study dependencies between life length within families. If joint life contracts (between a husband and a wife) have been long studied in actuarial literature, little is known about child and parents dependencies. We illustrate those dependencies using 19th century family trees in France, and quantify implications in annuities computations. For parents and children, we observe a modest but significant positive association between life lengths. It yields different estimates for remaining life expectancy, present values of annuities, or whole life insurance guarantee, given information about the parents (such as the number of parents alive). A similar but weaker pattern is observed when using information on grandparents.

Modeling and Controlling the Spread of Epidemic with Various Social and Economic Scenarios
S.P. Lukyanets,I.S. Gandzha,O.V. Kliushnychenko

We propose a novel model for describing the spreading processes, in particular, epidemics. Our model is an extension of the SIQR (susceptible-infected-quarantine-recovered) and SIRP (susceptible-infected-recovered-pathogen) models used earlier to describe various scenarios of epidemic spread. As compared to the basic SIR model, our model takes into account two possible routes of virus transmission: direct from the infected compartment to the susceptible compartment and indirect via some intermediate medium or fomites. The transmission rates are estimated in terms of the average distances between the individuals in selected social environments and characteristic relaxation times. We also introduce a resource activation function that reflects the load of the epidemics on economics and the limited capacity of the medical infrastructure. Our model brings an advantage of building various control strategies to minimize the effect of the epidemic and can be applied to modeling the recent COVID-19 outbreak.

No-arbitrage concepts in topological vector lattices
Eckhard Platen,Stefan Tappe

We provide a general framework for no-arbitrage concepts in topological vector lattices, which covers many of the well-known no-arbitrage concepts as particular cases. The main structural condition which we impose is that the outcomes of trading strategies with initial wealth zero and those with positive initial wealth have the structure of a convex cone. As one consequence of our approach, the concepts NUPBR, NAA$_1$ and NA$_1$ may fail to be equivalent in our general setting. Furthermore, we derive abstract versions of the fundamental theorem of asset pricing. We also consider a financial market with semimartingales which does not need to have a num\'{e}raire, and derive results which show the links between the no-arbitrage concepts by only using the theory of topological vector lattices and well-known results from stochastic analysis in a sequence of short proofs.

Numerical Simulation of Exchange Option with Finite Liquidity: Controlled Variate Model
Kevin S. Zhang,Traian A. Pirvu

In this paper we develop numerical pricing methodologies for European style Exchange Options written on a pair of correlated assets, in a market with finite liquidity. In contrast to the standard multi-asset Black-Scholes framework, trading in our market model has a direct impact on the asset's price. The price impact is incorporated into the dynamics of the first asset through a specific trading strategy, as in large trader liquidity model. Two-dimensional Milstein scheme is implemented to simulate the pair of assets prices. The option value is numerically estimated by Monte Carlo with the Margrabe option as controlled variate. Time complexity of these numerical schemes are included. Finally, we provide a deep learning framework to implement this model effectively in a production environment.

On the Exchange Rate and Economic Policy Uncertainty Nexus: A Panel VAR Approach for Emerging Markets
Abid, Abir,Rault, Christophe
We examine the Exchange Rate Volatility (ERV) response to the Economic Policy Uncertainty (EPU) shocks from a panel VAR perspective used for the first time in this context. Focusing on Emerging Market Economies (EME), our noteworthy findings postulate that (a) both home and foreign EPU shocks are highly significant in explaining the ERV, (b) the contribution of the foreign EPU to the ERV fluctuation overcomes the local EPU's share, (c) the ERV acts as a significant transmission channel of the US-EPU to the economic activity, (d) the home EPU increases with higher US-EPU and vice versa and (e) the latter is surprisingly and markedly sensitive to EME macroeconomic conditions. Our findings are robust to different sensitivity analyses, provide novel insights into EPU international spillovers, and have interesting policy implications for EME decisions makers and investors.

One Vol to Rule Them All: Common Volatility Dynamics in Factor Returns
Kapadia, Nishad,Linn, Matthew,Paye, Bradley S.
We show that there is strong commonality in the volatility of a wide range of diversified equity portfolios. Common factor volatility (CFV) exists even when factor or anomaly returns are market-adjusted and does not appear to be attributable to common microstructure noise or a lack of diversification. CFV closely relates to previously identified commonality in idiosyncratic volatility, implying that a common volatility feature pervades the entire spectrum of equity return variation. We develop a `vector autoregressive index' that extracts CFV from a VAR model. Consistent with the interpretation of CFV as a latent, pervasive equity risk feature, CFV forecasts future dynamics of volatility for virtually all diversified equity portfolios we examine, surpassing the forecasting ability of value weighted market volatility. Several alternative tests indicate only a weak relation between CFV and time-variation in fundamental uncertainty. We also do not find strong support for the hypothesis that variation in growth options, operating or financial leverage drives CFV. The ultimate sources of common equity volatility dynamics therefore constitute an important unresolved puzzle in finance.

One model is not enough: heterogeneity in cryptocurrencies' multifractal profiles
Aurelio F. Bariviera

This paper studies of the multifractal dynamics in 84 cryptocurrencies. It fills an important gap in the literature, by studying this market using two alternative multi-scaling methodologies. We find compelling evidence that cryptocurrencies have different degree of long range dependence, and --more importantly -- follow different stochastic processes. Some of them follow models closer to monofractal fractional Gaussian noises, while others exhibit complex multifractal dynamics. Regarding the source of multifractality, our results are mixed. Time series shuffling produces a reduction in the level of multifractality, but not enough to offset it. We find an association of kurtosis with multifractality.

Ownership Comes with Responsibility â€" the Impact of Ownership Characteristics on CSR
Benz, Lukas,Paulus, Stefan,Rohleder, Martin,Wilkens, Marco
This article provides a novel methodology to investigate the influence of share ownership on corporate decision making. Quantifying the characteristics of firms’ owners based on their measurable investment habits enables us to assess their predominant preferences. We demonstrate that a preference by owners for eco-social investments is a positive force in their firms’ CSR performance. In contrast, firms exhibit a lower CSR performance when owners show a higher degree of heterogeneity in terms of eco-social preferences. Furthermore, we find that universal as well as long-term ownerships significantly encourage CSR, hence confirming prominent theoretical concepts.

Portfolio Choice with Path-Dependent Preferences
Kritzman, Mark,Li, Ding,Qiu, Grace (Tiantian),Turkington, David
Many sophisticated investors rely on scenario analysis to select a portfolio. These investors define prospective economic scenarios, assign probabilities to them, translate the scenarios into expected asset class returns, and select the portfolio with the highest expected return or expected utility, given all these inputs. With this approach, the investor only considers single period outcomes. The authors propose a new approach to scenario analysis that enables investors to consider sequential outcomes. They define prospective scenarios, not as average values of economic variables, but as paths for these variables. And they measure the likelihood that these paths will prevail in the future based on their statistical similarity to the historical sequences of these variables. The authors also employ a novel forecasting technique called partial sample regression to map economic outcomes onto asset class returns. This process allows investors to evaluate portfolios based on the likelihood they will produce a certain pattern of returns over a specified investment horizon.

Portfolio Delegation and 401(k) Plan Participant Responses to COVID-19
Blanchett, David,Finke, Michael S.,Reuter, Jonathan
We analyze the behavior of 401(k) plan participants during the first quarter of 2020, when COVID-19 generated historic volatility, large negative returns, and significant unemployment. Only 2% of participants invested in TDFs made any changes to their portfolios, with even lower rates of change among those defaulted into robo-advised managed accounts, suggesting that delegation can decrease the likelihood of portfolio mistakes by less sophisticated participants. While 16.6% of non-delegated participants made portfolio changes, these changes were more likely among more sophisticated participants and appear not to have reduced participants’ quarterly returns. Consistent with liquidity constraints, however, withdrawals spike following job loss.

Quantile Convolutional Neural Networks for Value at Risk Forecasting
Gábor Petneházi

This article presents a new method for forecasting Value at Risk. Convolutional neural networks can do time series forecasting, since they can learn local patterns in time. A simple modification enables them to forecast not the mean, but arbitrary quantiles of the distribution, and thus allows them to be applied to VaR-forecasting. The proposed model can learn from the price history of different assets, and it seems to produce fairly accurate forecasts.

Resource Allocation in the Brain and the Capital Asset Pricing Model
Siddiqi, Hammad
What happens when information reaches the human brain? In economics, a black-box approach to information processing in the brain is generally taken with an implicit assumption that information, once it reaches the brain, is accurately processed. In sharp contrast, research in brain sciences has established that when information reaches the brain, a mental template or schema (neural substrate of knowledge) is first activated, which influences information absorption. Schemas are created through a resource intensive process in which finite brain resources are allocated to different tasks, with resource allocation in the brain having an impact on the structure of schemas. In this article, we explore the implications of this richer view from brain sciences for the capital asset pricing model (CAPM). We show that two versions of CAPM arise depending on how the brain resources are allocated in schema creation. In one version, the relationship between beta and expected returns is flat along with features akin to value, size, and momentum effects. In the second version, the relationship between beta and expected return is strongly positive with an implied risk-free rate which could be negative. Novel predictions emerging from this approach are: momentum is negatively correlated with value, size, and betting-against-beta, and stocks that command a lion’s share of investor attention have lower risk-adjusted returns.

Risk Minimization, Regret Minimization and Progressive Hedging Algorithms
Jie Sun,Xinmin Yang,Qiang Yao,Min Zhang

This paper begins with a study on the dual representations of risk and regret measures and their impact on modeling multistage decision making under uncertainty. A relationship between risk envelopes and regret envelopes is established by using the Lagrangian duality theory. Such a relationship opens a door to a decomposition scheme, called progressive hedging, for solving multistage risk minimization and regret minimization problems. In particular, the classical progressive hedging algorithm is modified in order to handle a new class of linkage constraints that arises from reformulations and other applications of risk and regret minimization problems. Numerical results are provided to show the efficiency of the progressive hedging algorithms.

Robust portfolio optimization with multi-factor stochastic volatility
Ben-Zhang Yang,Xiaoping Lu,Guiyuan Ma,Song-Ping Zhu

This paper studies a robust portfolio optimization problem under the multi-factor volatility model introduced by Christoffersen et al. (2009). The optimal strategy is derived analytically under the worst-case scenario with or without derivative trading. To illustrate the effects of ambiguity, we compare our optimal robust strategy with some strategies that ignore the information of uncertainty, and provide the corresponding welfare analysis. The effects of derivative trading to the optimal portfolio selection are also discussed by considering alternative strategies. Our study is further extended to the cases with jump risks in asset price and correlated volatility factors, respectively. Numerical experiments are provided to demonstrate the behavior of the optimal portfolio and utility loss.

Short Selling Efficiency
Chen, Yong,Da, Zhi,Huang, Dayong
We examine the effects of short selling efficiency (SSE) on the aggregate stock market, with SSE measured by the slope coefficient of a cross-sectional regression of abnormal short interest on the overpricing measure. We find that SSE strongly and negatively predicts the equity risk premium, suggesting that large overpricing exists when short selling is executed in the right stocks. The predictive power of SSE is at least as strong as aggregate short interest and appears stronger when the level of short interest is high. Moreover, low SSE signals more efficient market and the CAPM performs well in the subsequent month.

Slow Recoveries, Endogenous Growth and Policy
Bonciani, Dario,Gauthier, David,Kanngiesser, Derrick
We study the interaction between financial frictions and endogenous growth and its implications for conventional and unconventional monetary policy as well as macroprudentialpolicy. We show that disturbances to financial intermediation can lead to permanent lossesin output, which are more severe when the central bank’s policy rate is at the zero lowerbound (ZLB). Macroprudential policy can mitigate the effects of financial disturbances andreduce the frequency of hitting the lower bound, while quantitative easing is an effective toolin mitigating the negative effects of a binding ZLB constraint. Finally, we find that the gainsfrom macroprudential policy, in terms of stabilisation and welfare, are significantly larger ina model with endogenous growth compared to one with exogenous growth.

Stablecoins and Cryptocurrency Returns: Evidence From Large Bayesian VARs
Bianchi, Daniele,Rossini, Luca,Iacopini, Matteo
We study the relationship between the returns on stable-coins and major cryptocurrency pairs within the context of a large Bayesian Vector Auto-regressive (BVAR) model, and contribute to a growing literature that aims at understanding the role of cryptocurrency markets as alternative investments. Methodologically, we propose a global-local hierarchical shrinkage prior to regularize the model parameters and consider key features in cryptocurrency returns such as stochastic volatility and fat tails. The main results show that Tether (USDT), the main stable-coin currently traded, significantly and positively correlates with future returns on major cryptocurrency pairs, conditional on trading volume. A strategy that exploits the exposure to USDT delivers substantial economic gain out-of-sample relative to an equal-weight market portfolio and a buy-and-hold investment in Bitcoin.

Stock Returns, Leverage, and the COVID-19 Pandemic
Davison, Colin
This paper investigates how firm debt disproportionately impacted the stock returns of firms who were highly exposed to the economic consequences of social distancing. Specifically, we use a difference-in-difference design to causally identify the impact that higher levels of firm debt had for firms who were more exposed to social distancing requirements. We find that the effects on stock returns are economically meaningful. We also show that both the sign and magnitude of this effect vary as the pandemic unfolds. In the period from February 20, 2020-March 20, 2020, when the stock market was declining significantly, increased debt caused lower stock returns for firms less able to transition their workers to teleworking relative to those who were more able to comply with social distancing. This effect abruptly reverses when the Federal Reserve announces their major intervention into the corporate bond market on March 23, 2020. In the period from March 23, 2020-March 24, 2020 we document that increased debt caused higher stock returns for firms more affected by COVID-19. Our results illustrate how real economic shocks interact with firm financial positions and translate into stock market returns. Also, our results highlight how Federal Reserve policy causally impacted stock market returns, giving us a window into the market's perceptions of the Federal Reserve's unprecedented intervention into the corporate bond market.

Suffocating Fire Sales
Nils Detering,Thilo Meyer-Brandis,Konstantinos Panagiotou,Daniel Ritter

Fire sales are among the major drivers of market instability in modern financial systems. Due to iterated distressed selling and the associated price impact, initial shocks to some institutions can be amplified dramatically through the network induced by portfolio overlaps. In this paper we develop models that allow us to investigate central characteristics that drive or hinder the propagation of distress. We investigate single systems as well as ensembles of systems that are alike, where similarity is measured in terms of the empirical distribution of all defining properties of a system. This asymptotic approach ensures a great deal of robustness to statistical uncertainty and temporal fluctuations, and we give various applications. A natural characterization of systems resilient to fire sales emerges, and we provide explicit criteria that regulators may exploit in order to assess the stability of any system. Moreover, we propose risk management guidelines in form of minimal capital requirements, and we investigate the effect of portfolio diversification and portfolio overlap. We test our results by Monte Carlo simulations for exemplary configurations.

Tail Events, Emotions and Risk Taking
Corgnet, Brice,Cornand, Camille,Hanaki, Nobuyuki
Recent works have shown how tail events could account for financial anomalies such as the equity premiumpuzzle. These models do not explore, however, why investors would discount tail risk so heavily. We take on this challenge by designing a novel tail-event experiment to assess both investors’ behavioral and physiological reactions. We show that investors who observe the tail event without suffering losses tend to decrease their pricing of the asset subsequently. By contrast, loss-averse investors who suffer tail losses tend to increase their bids. This response is especially pronounced for those who exhibit a strong emotional response to tail losses. This demonstrates the key role played by emotions in influencing investors’ response to tail events. Finally, investors who exhibit high anticipatory arousal, as measured with electrodermal activity, posted lower bids and were less likely to suffer tail losses and go bankrupt. They also achieved higher earnings when tail events occurred frequently. This finding contrasts with the common view that investors should silence their emotions.

Tempered Stable Processes with Time Varying Exponential Tails
Young Shin Kim,Kum-Hwan Roh,Raphael Douady

In this paper, we introduce a new time series model having the stochastic exponential tail. This model is constructed based on the Normal Tempered Stable distribution with a time varying parameter. The model captures the stochastic exponential tail which generates the volatility smile effect and volatility term structure in option pricing. Moreover, the model describes the time varying volatility of volatility. We empirically show the stochastic skewness and stochastic kurtosis by applying the model to analyze S\&P 500 index return data. We present Monte-Carlo simulation technique for a parameter calibration of the model for the S\&P 500 option prices. By the calibration, we can see that the stochastic exponential tail makes the model better to analyze the market option prices.

The COVID-19 Stock Market Downturn: Expected Growth or Discount Rate?
Böni, Pascal,Zimmermann, Heinz
We use the Gordon (1959) constant growth model to explain stock returns of S&P500 index constituents during the COVID-19 implied market downturn and subsequent V-shaped recovery. Stock returns are largely affected by a change in the implied growth rate w and only to a lesser extent by a change in discount rate k, the latter typ-ically used to explain stock returns in the classical asset pricing literature. We reach this conclusion by using ordinary least squares (OLS) regressions of stock returns on the unobservable Gordon factors, which we estimate from firm level valuation ratios D/P, P/E and P/B. The effects from a decrease in implied growth outweigh those from an in-crease in discount rate by a factor of approximately 1.6 to 1.7, implying the COVID-19 stock market downturn is of long-term rather than transitory character. We evaluate the performance of the Gordon factors for an early period of the Global Financial Crisis (GFC) and find strong support for our observations.

The Cost of Undisturbed Landscapes
Sebastian Wehrle,Johannes Schmidt,Christian Mikovits

By 2030 Austria aims to meet \SI{100}{\percent} of its electricity demand from domestic renewable sources, predominantly from wind and solar energy. While wind power reduces \COO emissions, it is also connected to negative impacts at the local level. such as interference with landscape aesthetics. Nevertheless, wind power comes at lower system integration cost than solar power, so that it effectively reduces system cost. We quantify the opportunity cost of replacing wind turbines with solar power, using the power system model medea. Our findings suggest that these cost of undisturbed landscapes are considerable, particularly when PV is not entirely rolled out as utility scale, open space installations. The opportunity cost is likely high enough to allow for significant compensation of the ones affected by local wind turbine externalities.

The Dark side of Independent Boards, the Case of Corporate Social Responsibility
Lu, Fangzhou
Independent boards have been documented to have a positive effect on corporate governance, however, I show that independent boards improve shareholder value at the cost of sacrificing corporate social responsibility (CSR). Specifically, I find that stakeholders such as employees and consumers' interest have been compromised as a result of board myopia. This result is even stronger among firms in industries with intense product market competition, and among firms with more analysts followed. My result also suggests that negative corporate social behavior may not be fully priced by investors.

The Fiscal Footprint of Macroprudential Policy
Reis, Ricardo
Monetary policy leaves a fiscal footprint. In some circumstances, relieving the fiscal burden becomes the main goal of policy, and inflation control is subordinate. This article notes that the same is true of macroprudential policy, and it characterizes the size and sign of its fiscal footprint, as well as the states of the world in which the temptation for fiscal goals to dominate may be higher. Macroprudential policies that increase the demand for government bonds by banks directly lower the cost of rolling over public debt, but decrease lending, real activity, and tax collections. They lower the incidence and fiscal cost of a financial crisis, but they may make a fiscal crisis more likely.

The Gauss2++ Model -- A Comparison of Different Measure Change Specifications for a Consistent Risk Neutral and Real World Calibration
Christoph Berninger,Julian Pfeiffer

Especially in the insurance industry interest rate models play a crucial role e.g. to calculate the insurance company's liabilities, performance scenarios or risk measures. A prominant candidate is the 2-Additive-Factor Gaussian Model (Gauss2++) - in a different representation also known as the 2-Factor Hull-White model. In this paper, we propose a framework to estimate the model such that it can be applied under the risk neutral and the real world measure in a consistent manner. We first show that any progressive and square-integrable function can be used to specify the change of measure without loosing the analytic tractability of e.g. zero-coupon bond prices in both worlds. We further propose two time dependent candidates, which are easy to calibrate: a step and a linear function. They represent two variants of our framework and distinguish between a short and a long term risk premium, which allows to regularize the interest rates in the long horizon. We apply both variants to historical data and show that they indeed produce realistic and much more stable long term interest rate forecast than the usage of a constant function. This stability over time would translate to performance scenarios of e.g. interest rate sensitive fonds and risk measures.

The Hedging Channel of Exchange Rate Determination
Liao, Gordon,Zhang, Tony
We document the exchange rate hedging channel that connects country-level measures of net external financial imbalances with exchange rates. In times of market distress, countries with large positive external imbalances (e.g. Japan) experience domestic currency appreciation, and crucially, forward exchange rates appreciate relatively more than the spot after adjusting for interest rate differentials. Countries with large negative foreign asset positions experience the opposite currency movements. We present a model demonstrating that exchange rate hedging coupled with intermediary constraints can explain these observed relationships between net external imbalances and spot and forward exchange rates. We find empirical support for this currency hedging channel of exchange rate determination in both the conditional and unconditional moments of exchange rates, option prices, large institutional investors' disclosure of hedging activities, and central bank swap line usage during the COVID-19 market turmoil.

The Impact of Financial Sanctions: The Case of Iran 2011-2016
Ghasseminejad, Saeed,Jahan-Parvar, Mohammad R.
This study provides a detailed analysis of the impact of financial sanctions on publicly traded companies. We consider the effect of imposing and lifting sanctions on the target country's traded equities and examine the differences in the reaction of politically connected firms and those without such connections. The paper focuses on Iran due to (1) its sizable financial markets, (2) imposition of sanctions of varying severity and duration on private and state-owned companies, (3) the significant presence of politically connected firms in the stock market, and (4) the unique event of the 2015 nuclear deal, resulting in fairly rapid lifting of a sizable portion of imposed sanctions. We find that sanctions affect politically connected firms more than ordinary firms, have lasting negative effects on profitability ratios, and that politically connected firms stock prices bounce back more slowly after removal of sanctions. Firms targeted by financial sanctions decrease their leverage and increase their cash holding to manage their perceived increase in risk profile.

The Linear and Non-Linear Effects of Internal Control and its Five Components on Corporate Innovation: Evidence from Chinese Firms using the COSO Framework
Chan, Kam C.,Chen, Yining,Liu, Baohua
This study examines the impact of internal control on firm innovation using a sample of Chinese firms. The impact of the internal control system as a whole, as well as the impact of the five components of internal control individually (i.e., control environment, risk assessment, control activities, information & communication, and monitoring), are analyzed. Our results suggest that internal control, as an integrated system, has significant positive impact on firm innovation, as measured by patent applications. We document that the magnitude of impact on innovation varies across different subcategories (components) of internal control, with control environment, control activities, and information & communication components exhibiting stronger impacts on innovation than those of risk assessment and monitoring components. In addition, we find that a high level of control environment, control activities, and information & communication (risk assessment and monitoring) components have a stronger (weaker) impact on innovation compared to a low level.

The Ownership Concentration - Innovation Nexus: Evidence from SMEs around The World
Truong, Quang-Thai
This study investigates the association between ownership concentration and firm innovation using a large data sample of small and medium-sized enterprises (SMEs) spanning 93 countries from 2011 to 2018. We find that higher ownership concentration is associated with a lower likelihood of introducing innovative activities. Further, we aim to validate the possible mechanisms through which concentrated ownership is detrimental to corporate innovation. The results reveal that concentrated ownership has detrimental impacts on innovation for firms with higher degree of asymmetric information, and firms lead by less experienced managers. In addition, we show that the negative association between ownership concentration and innovation only exists for financially constrained firms, which are younger enterprises and SMEs with higher financing obstacles.

The Ratio Problem
Bartlett, Robert P.,Partnoy, Frank
We use the term "ratio problem" to describe the omitted variable and measurement error bias that can arise when a ratio is the dependent variable in an economic model. First, we show how bias can arise from the omission of two classes of variables based on a ratio's denominator. As an example, we demonstrate that the widely-cited "inverse-U" relationship between managerial ownership and Tobin's Q is reversed when these variables are included. Second, we show how measurement error in the ratio's denominator can produce bias. We provide empirical tests and solutions, and urge caution about ratios as dependent variables.

The energy representation of world GDP
Boris M. Dolgonosov

The dependence of world GDP on current energy consumption and total energy produced over the previous period and materialized in the form of production infrastructure is studied. The dependence describes empirical data with high accuracy over the entire observation interval 1965-2018.

The forecasting power of short-term options
Böök, Arthur,Sala, Carlo
We propose option-implied measures of conditional asymmetry based upon quantiles and expectiles inferred from weekly options. All quantities are by construction forward looking and estimated non-parametrically through a novel arbitrage-free natural smoothing spline technique that produces quick to estimate volatility smiles. We find that option implied asymmetry indicators exhibit short, medium and long-term predictive ability for the U.S. equity risk premium and market volatility, both in- and out-of-sample, and outperform equal indicators inferred from historical returns.

Time Inconsistency and Endogenous Borrowing Constraints
Bhattacharya, Joydeep,Bishnu, Monisankar,Wang, Min
This paper studies the welfare of time-inconsistent, partially sophisticated agents living under two different regimes, one with complete, unfettered credit markets (CM) and the other with endogenous borrowing constraints (EBC) where the borrowing limits are set to make agents indifferent between defaulting and paying back their unsecured loans. The CM regime cannot deliver the first best because partially sophisticated agents would undo plans laid out by previous selves and borrow too much. Somewhat counterintuitively, in some cases, the EBC regime may deliver higher welfare than the CM regime. These results speak to the academic debate surrounding the creation and functioning of the CFPB (Consumer Financial Protection Bureau) in the U.S. and its implementation of the ability-to-repay rule on lenders after the 2007-8 crisis. Such institutions generate commitment publicly and may help time inconsistent agents economize on the costs of private commitment provision.

Time-inhomogeneous Gaussian stochastic volatility models: Large deviations and super roughness
Archil Gulisashvili

We introduce time-inhomogeneous stochastic volatility models, in which the volatility is described by a nonnegative function of a Volterra type continuous Gaussian process that may have extremely rough sample paths. The drift function and the volatility function are assumed to be time-dependent and locally $\omega$-continuous for some modulus of continuity $\omega$. The main results obtained in the paper are sample path and small-noise large deviation principles for the log-price process in a Gaussian model under very mild restrictions. We use these results to study the asymptotic behavior of binary up-and-in barrier options and binary call options.

Trends, Reversion, and Critical Phenomena in Financial Markets
Christof Schmidhuber

Financial markets across all asset classes are known to exhibit trends. These trends have been exploited by traders for decades. Here, we empirically measure when trends revert, based on 30 years of daily futures prices for equity indices, interest rates, currencies and commodities. We find that trends tend to revert once they reach a critical level of statistical significance. Based on polynomial regression, we carefully measure this critical level. We find that it is universal across asset classes and has a universal scaling behavior, as the trend's time horizon runs from a few days to several years. The corresponding regression coefficients are small, but statistically highly significant, as confirmed by bootstrapping and out-of-sample testing. Our results signal to investors when to exit a trend. They also reveal how markets have become more efficient over the decades. Moreover, they point towards a potential deep analogy between financial markets and critical phenomena: our analysis supports the conjecture that financial markets can be modeled as statistical mechanical ensembles of Buy/Sell orders near critical points. In this analogy, the trend strength plays the role of an order parameter, whose dynamcis is described by a Langevin equation.

V-, U-, L-, or W-shaped recovery after COVID? Insights from an Agent Based Model
Dhruv Sharma,Jean-Philippe Bouchaud,Stanislao Gualdi,Marco Tarzia,Francesco Zamponi

We discuss the impact of a Covid-like shock on a simple toy economy, described by the Mark-0 Agent-Based Model that we developed and discussed in a series of previous papers. We consider a mixed supply and demand shock, and show that depending on the shock parameters (amplitude and duration), our toy economy can display V-shaped, U-shaped or W-shaped recoveries, and even an L-shaped output curve with permanent output loss. This is due to the existence of a self-sustained "bad" state of the economy. We then discuss two policies that attempt to moderate the impact of the shock: giving easy credit to firms, and the so-called helicopter money, i.e. injecting new money into the households savings. We find that both policies are effective if strong enough, and we highlight the potential danger of terminating these policies too early. While we only discuss a limited number of scenarios, our model is flexible and versatile enough to allow for a much wider exploration, thus serving as a useful tool for the qualitative understanding of post-Covid recovery.

When Crisis Hits: The Role of Regulations
Wu, Xi
This paper shows that regulations act as a stabilizer for firms during crises. During COVID-19 pandemic, firms with more regulations ex ante have superior crisis performance. More regulated firms experience four to five percent less decline in both stock and corporate bond prices. Prior to the crisis, firms with high regulations hold more cash, have lower leverage, and are less likely to pay dividends, making them more resilient to extreme market conditions. Moreover, these more regulated firms have less systematic risk exposures during the crisis. I also find similar effects of regulations during the 2008 Financial Crisis.

When the Market Went Viral: COVID-19, Stock Returns, and Firm Characteristics
Bansal, Avijit,Gopalakrishnan, Balagopal,Jacob, Joshy,Srivastava, Pranjal
COVID-19 has brought heightened uncertainty in product markets, business operations, and financing plans for firms, within a short time. The unexpected and severe impact of the crisis allows us to estimate the role of many risk factors that offer an explanation for stock returns. We empirically examine how the market valuation of firms vary on account of firm-specific characteristics that make them more vulnerable or resilient to the pandemic. Firms with greater financial flexibility enjoy greater market value suggesting that investors are willing to accept lower expected returns in such firms. We find that firms with higher inflexibility in rescaling operations and a greater degree of operating leverage face lower market valuation. The magnifying impact of this crisis on operational risk strengthens its ability to explain stock returns. Reinforcing the significant role of governance in times of uncertainty, we find that firms with concentrated ownership by promoter shareholders and widely networked boards suffer less market value loss. State-owned firms remain relatively resilient in the crisis, indicating their greater role in post-COVID recovery. The signalling value of insider actions strengthens during the crisis where firm-level information is scarce. We find that firms with higher insider buying outperformed others. Indicative of the greater vulnerability of the unaffiliated firms, we find that markets attach a higher marginal value to the liquidity held in such firms compared to their group affiliated peers. Group affiliated firms are more resilient to the crisis relative to unaffiliated firms.