# Research articles for the 2021-07-20

A Incid\^encia Final dos Tributos Indiretos no Brasil: Estimativa Usando a Matriz de Insumo-Produto 2015
Rozane Bezerra de Siqueira,José Ricardo Bezerra Nogueira,Carlos Feitosa Luna
arXiv

Taxes on goods and services account for about 45% of total tax revenue in Brazil. This tax collection results in a highly complex system, with several taxes, different tax bases, and a multiplicity of rates. Moreover, about 43% of taxes on goods fall on inputs. In this context, the effective tax rates can substantially differ from the legal rates. In this study we estimate the final incidence of indirect taxes in Brazil using the 2015 Brazilian input-output matrix and a method that incorporates the multisector effects of the taxation of inputs.

A Universal Basic Income For Brazil: Fiscal and Distributional Effects of Alternative Schemes
Rozane Bezerra de Siqueira,Jose Ricardo Bezerra Nogueira
arXiv

The outbreak of the Covid-19 pandemic has led to an increasing interest in Universal Basic Income (UBI) proposals as it exposed the inadequacy of traditional welfare systems to provide basic financial security to a large share of the population. In this paper, we use a static tax-benefit microsimulation model to analyse the fiscal and distributional effects of the hypothetical implementation in Brazil of alternative UBI schemes which partially replace the existing tax-transfer system. The results indicate that the introduction of a UBI/Flat Tax system in the country could be both extremely effective in reducing poverty and inequality and economically viable.

A note on a PDE approach to option pricing under xVA
arXiv

In this paper we study partial differential equations (PDEs) that can be used to model value adjustments. Different value adjustments denoted generally as xVA are nowadays added to the risk-free financial derivative values and the PDE approach allows their easy incorporation. The aim of this paper is to show how to solve the PDE analytically in the Black-Scholes setting to get new semi-closed formulas that we compare to the widely used Monte-Carlo simulations and to the numerical solutions of the PDE. Particular example of collateral taken as the values from the past will be of interest.

AI in Finance: Challenges, Techniques and Opportunities
Longbing Cao
arXiv

AI in finance broadly refers to the applications of AI techniques in financial businesses. This area has been lasting for decades with both classic and modern AI techniques applied to increasingly broader areas of finance, economy and society. In contrast to either discussing the problems, aspects and opportunities of finance that have benefited from specific AI techniques and in particular some new-generation AI and data science (AIDS) areas or reviewing the progress of applying specific techniques to resolving certain financial problems, this review offers a comprehensive and dense roadmap of the overwhelming challenges, techniques and opportunities of AI research in finance over the past decades. The landscapes and challenges of financial businesses and data are firstly outlined, followed by a comprehensive categorization and a dense overview of the decades of AI research in finance. We then structure and illustrate the data-driven analytics and learning of financial businesses and data. The comparison, criticism and discussion of classic vs. modern AI techniques for finance are followed. Lastly, open issues and opportunities address future AI-empowered finance and finance-motivated AI research.

Adaptive Multilevel Monte Carlo for Probabilities
Abdul-Lateef Haji-Ali,Jonathan Spence,Aretha Teckentrup
arXiv

We consider the numerical approximation of $\mathbb{P}[G\in \Omega]$ where the $d$-dimensional random variable $G$ cannot be sampled directly, but there is a hierarchy of increasingly accurate approximations $\{G_\ell\}_{\ell\in\mathbb{N}}$ which can be sampled. The cost of standard Monte Carlo estimation scales poorly with accuracy in this setup since it compounds the approximation and sampling cost. A direct application of Multilevel Monte Carlo improves this cost scaling slightly, but returns sub-optimal computational complexities since estimation of the probability involves a discontinuous functional of $G_\ell$. We propose a general adaptive framework which is able to return the MLMC complexities seen for smooth or Lipschitz functionals of $G_\ell$. Our assumptions and numerical analysis are kept general allowing the methods to be used for a wide class of problems. We present numerical experiments on nested simulation for risk estimation, where $G = \mathbb{E}[X|Y]$ is approximated by an inner Monte Carlo estimate. Further experiments are given for digital option pricing, involving an approximation of a $d$-dimensional SDE.

Central Exchanges for Government Bonds? Evidence during COVID-19
Kutai, Ari,Nathan, Daniel,Wittwer, Milena
SSRN
In March 2020, government bond markets experienced severe illiquidity. Since then, regulators debate market reforms. One way to enhance liquidity could be to let government bonds, like stocks, be traded on central exchanges. We assess this reform with price data of the U.S, U.K., German, Japanese, and Israeli government bond and stock markets. We leverage a unique institutional feature of the Israeli government bond market---that it already operates on an exchange---to test whether and by how much having an exchange affected bid-ask spreads in March 2020 via difference-in-difference analyses. Our findings suggest that spreads in government bond markets without exchanges would have been 30%--60% lower if there had been an exchange. This implies higher liquidity and provides support in favor of the market reform

Co-optimization of Energy and Reserve with Incentives to Wind Generation: Case Study
arXiv

This case study presents an analysis and quantification of the impact of the lack of co-optimization of energy and reserve in the presence of high penetration of wind energy. The methodology is developed in a companion paper, Part I. Two models, with and without co-optimization are confronted. The modeling of reserve and the incentive to renewable as well as the calibration of the model are inspired by the Spanish market. A sensitivity analysis is performed on configurations that differ by generation capacity, ramping capability, and market parameters (available wind, Feed in Premium to wind, generators risk aversion, and reserve requirement). The models and the case study are purely illustrative but the methodology is general.

Cross-section Instability in Financial Markets: Impatience, Extrapolation, and Switching
Dieci, Roberto,He, Xuezhong
SSRN
This paper presents a stylized model of interaction among boundedly rational heterogeneousagents in a multi-asset financial market to examine how agents' impatience, extrapolation, andswitching behaviours can affect cross-section market stability. Besides extrapolation and performance based switching between fundamental and extrapolative trading documented in single asset market, we show that a high degree of impatience' of agents who are ready to switch to more protable trading strategy in the short run provides a further cross-section destabilizing mechanism. Though the fundamental' steady state values, which reflect the standard present-value of the dividends, represent an unbiased equilibrium market outcome in the long run (to a certain extent), the price deviation from the fundamental price in one asset can spill-over to other assets, resulting in cross section instability. Based on a (Neimark-Sacker) bifurcation analysis, we provide explicit conditions on how agents' impatience, extrapolation, and switching can destabilize the market and result in a variety of short and long-run patterns for the cross-section asset price dynamics.

Borri, Nicola,Santucci de Magistris, Paolo
SSRN
We show that sudden and large price moves in bitcoin prices, which we call jumps, explain a large portion of the variation in bitcoin returns. In order to do so, we use the general utility specification adopted in Maheu et al. (2013) for characterizing the conditional mean of daily bitcoin excess returns (the crypto premium), and we relate higher order moments of the return conditional distribution to the stochastic discount factor on the bitcoin. The conditional skewness and kurtosis are both significantly priced, and a relevant portion of the variability of bitcoin returns can be attributed to compensation for the jump term. We show that the price of crypto risk is time-varying, and higher in bad times for investors, when the conditional variance and kurtosis are high.

Data science and AI in FinTech: An overview
Longbing Cao,Qiang Yang,Philip S. Yu
arXiv

Financial technology (FinTech) has been playing an increasingly critical role in driving modern economies, society, technology, and many other areas. Smart FinTech is the new-generation FinTech, largely inspired and empowered by data science and new-generation AI and (DSAI) techniques. Smart FinTech synthesizes broad DSAI and transforms finance and economies to drive intelligent, automated, whole-of-business and personalized economic and financial businesses, services and systems. The research on data science and AI in FinTech involves many latest progress made in smart FinTech for BankingTech, TradeTech, LendTech, InsurTech, WealthTech, PayTech, RiskTech, cryptocurrencies, and blockchain, and the DSAI techniques including complex system methods, quantitative methods, intelligent interactions, recognition and responses, data analytics, deep learning, federated learning, privacy-preserving processing, augmentation, optimization, and system intelligence enhancement. Here, we present a highly dense research overview of smart financial businesses and their challenges, the smart FinTech ecosystem, the DSAI techniques to enable smart FinTech, and some research directions of smart FinTech futures to the DSAI communities.

Drivers learn city-scale dynamic equilibrium
Ruda Zhang,Roger Ghanem
arXiv

Understanding driver behavior in on-demand mobility services is crucial for designing efficient and sustainable transport models. Drivers' delivery strategy is well understood, but their search strategy and learning process still lack an empirically validated model. Here we provide a game-theoretic model of driver search strategy and learning dynamics, interpret the collective outcome in a thermodynamic framework, and verify its various implications empirically. We capture driver search strategies in a multi-market oligopoly model, which has a unique Nash equilibrium and is globally asymptotically stable. The equilibrium can therefore be obtained via heuristic learning rules where drivers pursue the incentive gradient or simply imitate others. To help understand city-scale phenomena, we offer a macroscopic view with the laws of thermodynamics. With 870 million trips of over 50k drivers in New York City, we show that the equilibrium well explains the spatiotemporal patterns of driver search behavior, and estimate an empirical constitutive relation. We find that new drivers learn the equilibrium within a year, and those who stay longer learn better. The collective response to new competition is also as predicted. Among empirical studies of driver strategy in on-demand services, our work examines the longest period, the most trips, and is the largest for taxi industry.

Epidemic Exposure, Fintech Adoption, and the Digital Divide
Saka, Orkun,Eichengreen, Barry,Aksoy, Cevat Giray
SSRN
We ask whether epidemic exposure leads to a shift in financial technology usage within and across countries and if so who participates in this shift. We exploit a dataset combining Gallup World Polls and Global Findex surveys for some 250,000 individuals in 140 countries, merging them with information on the incidence of epidemics and local 3G internet infrastructure. Epidemic exposure is associated with an increase in remote-access (online/mobile) banking and substitution from bank branch-based to ATM-based activity. Using a machine-learning algorithm, we show that heterogeneity in this response centers on the age, income and employment of respondents. Young, high-income earners in full-time employment have the greatest propensity to shift to online/mobile transactions in response to epidemics. These effects are larger for individuals in subnational regions with better ex ante 3G signal coverage, highlighting the role of the digital divide in adaption to new technologies necessitated by adverse external shocks.

Ex-Intrusion Corporate Cyber-Risk: Evidence from Internet Protocol Networks
Francis, Bill B.,Hu, Wenyao,Shohfi, Thomas
SSRN
Previous event studies of corporate cyber-risk have been limited to successful attacks on public firms but are biased samples constructed based on the economic magnitude of equity losses. To address this selection bias, we construct a larger and more representative sample of cyber intrusions only to find diminished negative equity (and insignificant corporate bond) market reactions compared to these prior studies. To identify cyber-risk irrespective of observing successful attacks, we match public firms to Internet protocol (IP) network data from the American Registry for Internet Numbers (ARIN) from 1991 to 2017. We find that both stockholders and creditors incorporate external IP network size into firm value. Further, debt and equity market reactions to cyberattacks are mitigated for firms with registered IP networks and that have larger network deployments. Overall, our study demonstrates an important public data source that can help institutions proxy for and more accurately price firm cybersecurity risk.

Exclusion of Extreme Jurors and Minority Representation: The Effect of Jury Selection Procedures
Andrea Moro,Martin Van der Linden
arXiv

We compare two established jury selection procedures meant to safeguard against the inclusion of biased jurors that are also perceived as causing minorities to be under-represented in juries. The Strike and Replace procedure presents potential jurors one-by-one to the parties, while the Struck procedure presents all potential jurors before the parties exercise vetoes. In equilibrium, Struck more effectively excludes extreme jurors than Strike and Replace but leads to a worse representation of minorities. Simulations suggest that the advantage of Struck in terms of excluding extremes is sizable in a wide range of cases. In contrast, Strike and Replace only provides a significantly better representation of minorities if the minority and majority are heavily polarized. When parameters are estimated to match the parties' selection of jurors by race with jury-selection data from Mississippi in trials against black defendants, the procedures' outcomes are substantially different, and the size of the trade-off between objectives can be quantitatively evaluated.

Financial Stability Amidst the Pandemic Crisis: On Top of the Wave
Gortsos, Christos,Ringe, Wolf-Georg,Annunziata, Filippo,Avgouleas, Emilios,Ayadi, Rym,Bodellini, Marco,Bosque, Carlos,Brescia Morra, Concetta,Busch, Danny,Casu, Barbara,Clarke, Blanaid J.,Enriques, Luca,Ferri, Giovanni,Frigeni, Claudio,De Groen, Willem,Grunewald, Seraina N.,Haselmann, Rainer F. H.,Joosen, Bart,Lamandini, Marco,Lehmann, Matthias,Morais, Luis Silva,Ramos Muñoz, David,Pagano, Marco,Pulgar Ezquerra, Juana,Sciarrone Alibrandi, Antonella,de Mesa, Ignacio Signes,Siri, Michele,Troeger, Tobias H.,Wyplosz, Charles
SSRN

International High-Frequency Arbitrage for Cross-Listed Stocks
Poutré, Cédric,Dionne, Georges,Yergeau, Gabriel
RePEC
We explore latency arbitrage activities with a new arbitrage strategy that we test with high-frequency data during the first six months of 2019. We study the profitability of mean-reverting arbitrage activities of 74 cross-listed stocks involving three exchanges in Canada and the United States. Our arbitrage strategy is a hybrid between triangular arbitrage and pairs trading. We synchronize the high-frequency data feeds from the three exchange venues considering explicitly the latency that comes from the transportation of information between the exchanges and its treatment time. Other trading costs and arbitrage risks are also considered. The annual net profit of an HFT firm that uses limit orders is around CAD $8 million (USD$6 million), a result that we consider reasonable when compared with the previous literature. International latency arbitrage with market orders is never profitable.

Internet Appendix: How Resilient is Mortgage Credit Supply? Evidence from the COVID-19 Pandemic
Fuster, Andreas,Hizmo, Aurel,Lambie-Hanson, Lauren,Vickery, James I.,Willen, Paul
SSRN
We study the evolution of US mortgage credit supply during the COVID-19 pandemic. Although the mortgage market experienced a historic boom in 2020, we show there was also a large and sustained increase in intermediation markups that limited the pass-through of low rates to borrowers. Markups typically rise during periods of peak demand, but this historical relationship explains only part of the large increase during the pandemic. We present evidence that pandemic-related labor market frictions and operational bottlenecks contributed to unusually inelastic credit supply, and that technology-based lenders, likely less constrained by these frictions, gained market share. Rising forbearance and default risk did not significantly affect rates on â€œplain-vanillaâ€ conforming mortgages, but it did lead to higher spreads on mortgages without government guarantees and loans to the riskiest borrowers. Mortgage-backed securities purchases by the Federal Reserve also supported the flow of credit in the conforming segment.

Machine Learning and Factor-Based Portfolio Optimization
Conlon, Thomas,Cotter, John,Kynigakis, Iason
SSRN
We examine machine learning and factor-based portfolio optimization. We find that factors based on autoencoder neural networks exhibit a weaker relationship with commonly used characteristic-sorted portfolios than popular dimensionality reduction techniques. Machine learning methods also lead to covariance and portfolio weight structures that diverge from simpler estimators. Minimum-variance portfolios using latent factors derived from autoencoders and sparse methods outperform simpler benchmarks in terms of risk minimization. These effects are amplified for investors with an increased sensitivity to risk-adjusted returns, during high volatility periods or when accounting for tail risk.

Moore's law, Wright's law and the Countdown to Exponential Space
Daniel Berleant,Venkat Kodali,Richard Segall,Hyacinthe Aboudja,Michael Howell
arXiv

Technologies have often been observed to improve exponentially over time. In practice this often means identifying a constant known as the doubling time, describing the time period over which the technology roughly doubles in some measure of performance or of performance per dollar. Moore's law is, classically, the empirical observation that the number of electronic components that can be put on a chip doubles every 18 months to 2 years. Today it is frequently stated as the number of computations available per unit of cost. Generalized to the appropriate doubling time, it describes the rate of advancement in many technologies. A frequently noted competitor to Moore's law is known as Wright's law, which has aeronautical roots. Wright's law (also called power law, experience curve and Henderson's law) relates some quality of a manufactured unit (for Wright, airplanes) to the volume of units manufactured. The Wright's law equation expresses the idea that performance - price or a quality metric - improves according to a power of the number produced, or alternatively stated, improves by a constant percentage for every doubling of the total number produced.

Does exploration of outer space conform to Moore's law or Wright's law-like behavior? Our results below are broadly consistent with these laws. This is true for many technologies. Although the two laws can make somewhat different predictions, Sahal found that they converge to the same predictions when manufacturing volume increases exponentially over time. When space exploration transitions into an independent commercial sector, as many people hope and expect, spacecraft technology will then likely enter an era of unambiguously exponential advancement.

Negative Interest Rates, COVID-19, and the Finances of Listed Euro Firms
von Eije, J. Henk
SSRN
Negative interest rates are a new phenomenon. Short-term deposit rates of the European Central Banks became negative in 2014 and sovereign debts of highly solvent countries followed. This paper measures the effect of short-term rates on short-term financial variables and of long-term rates on structural variables of listed euro firms. I thereby test if negative rates have an additional effect. I find that liquidity ratios and creditors ratios significantly decline if short-term rates fall, while negative ECB rates reduce liquidity ratios by an additional 0.6 percentage points. Declining long-term German government bond yields increase non-liquid assets significantly, and negative yields per se increase these assets in addition by 4.5%. The latter positive effects arise from the southern-euro countries and Finland and France. For the first full COVID-19 year (2020), the investments in non-liquid assets were 7.6% smaller. This will have contributed to the fact that -despite the corona crisis- the liquidity ratios increased by 2.3 percentage points. Similar COVID-19 effects are also found for small and large firms, for different sectors, and in most of the euro countries.

On Track for Retirement?
Matthew Olckers
arXiv

Over sixty percent of employees at a large South African company contribute the minimum rate of 7.5 percent to a retirement fund, far below the rate of 15 percent recommended by financial advisers. I use a field experiment to investigate whether providing employees with a retirement calculator, which shows projections of retirement income, leads to increases in contributions. The impact is negligible. The lack of response to the calculator suggests many employees may wish to save less than the minimum. I use a model of asymmetric information to explain why the employer sets a binding minimum.

Order Book Queue Hawkes-Markovian Modeling
Philip Protter,Qianfan Wu,Shihao Yang
arXiv

This article presents a Hawkes process model with Markovian baseline intensities for high-frequency order book data modeling. We classify intraday order book trading events into a range of categories based on their order types and the price changes after their arrivals. To capture the stimulating effects between multiple types of order book events, we use the multivariate Hawkes process to model the self- and mutually-exciting event arrivals. We also integrate a Markovian baseline intensity into the event arrival dynamic, by including the impacts of order book liquidity state and time factor to the baseline intensity. A regression-based non-parametric estimation procedure is adopted to estimate the model parameters in our Hawkes+Markovian model. To eliminate redundant model parameters, LASSO regularization is incorporated in the estimation procedure. Besides, model selection method based on Akaike Information Criteria is applied to evaluate the effect of each part of the proposed model. An implementation example based on real LOB data is provided. Through the example, we study the empirical shapes of Hawkes excitement functions, the effects of liquidity state as well as time factors, the LASSO variable selection, and the explanatory power of Hawkes and Markovian elements to the dynamics of the order book.

Robo-advisors: Development and Regulation in China
Huang, (Robin) Hui
SSRN
This chapter aims to analyze the regulation of robo-advisors in mainland China and give suggestions for its future development. In recent years, the Chinese government has set the development of smart finance as one of the economic policy priorities, including robo-advisors which rely on artificial intelligence and big data analytics. The development of robo-advisors in China has been facilitated by some local factors, such as strong consumer demand and a rapidly rising middle class, while at the same time faces significant challenges, such as the overly high and inconsistent entry threshold, insufficient asset management powers, weak fiduciary duties, and inadequate information disclosure duties. Based on a careful consideration of relevant overseas experiences and Chinese local conditions, this chapter argues that China should allow robo-advisors to provide discretionary asset management services and that a uniform regime should be established to cover both human and AI investment advisers. A streamlined and refined entry threshold should be set for the advisory service market. Robo-advisors should be subject to more detailed and functional information disclosure rules and fiduciary duties.

Stock price prediction using BERT and GAN
Priyank Sonkiya,Vikas Bajpai,Anukriti Bansal
arXiv

The stock market has been a popular topic of interest in the recent past. The growth in the inflation rate has compelled people to invest in the stock and commodity markets and other areas rather than saving. Further, the ability of Deep Learning models to make predictions on the time series data has been proven time and again. Technical analysis on the stock market with the help of technical indicators has been the most common practice among traders and investors. One more aspect is the sentiment analysis - the emotion of the investors that shows the willingness to invest. A variety of techniques have been used by people around the globe involving basic Machine Learning and Neural Networks. Ranging from the basic linear regression to the advanced neural networks people have experimented with all possible techniques to predict the stock market. It's evident from recent events how news and headlines affect the stock markets and cryptocurrencies. This paper proposes an ensemble of state-of-the-art methods for predicting stock prices. Firstly sentiment analysis of the news and the headlines for the company Apple Inc, listed on the NASDAQ is performed using a version of BERT, which is a pre-trained transformer model by Google for Natural Language Processing (NLP). Afterward, a Generative Adversarial Network (GAN) predicts the stock price for Apple Inc using the technical indicators, stock indexes of various countries, some commodities, and historical prices along with the sentiment scores. Comparison is done with baseline models like - Long Short Term Memory (LSTM), Gated Recurrent Units (GRU), vanilla GAN, and Auto-Regressive Integrated Moving Average (ARIMA) model.

The Impact of Tax Reforms on EVA in the U.S.: An Empirical Examination
Boshra, Joseph,Bishara, Mina
SSRN
This paper examines the impact of tax reforms on EVA on the non-financial S&P 500 firms by examining how ROIC, WACC, and growth in Invested Capital were affected by the 2017 tax reform through paired sample t-tests. We find that while the tax cut has increased ROIC-WACC, it hindered growth in Invested Capital. Furthermore, due to the distribution of ROIC-WACC across the sample firms being negative on aggregate, the increase in ROIC-WACC, which is still a negative figure, multiplied by a growing capital base, resulted in a decrease in aggregate EVA. This leads to the discussion of a critical invested capital growth rate, at which a given change in ROIC-WACC results in no change in EVA, and a discussion of how different firms have their EVA affected differently depending on their ROIC-WACC and Invested Capital growth rate.

Time-adaptive high-order compact finite difference schemes for option pricing in a family of stochastic volatility models
Bertram Düring,Christof Heuer
arXiv

We propose a time-adaptive, high-order compact finite difference scheme for option pricing in a family of stochastic volatility models. We employ a semi-discrete high-order compact finite difference method for the spatial discretisation, and combine this with an adaptive time discretisation, extending ideas from [LSRHF02] to fourth-order multistep methods in time.

Trust and Financial Development: Forms of Trust and Ethnic Fractionalization Matter
Ogcem, Ali Recayi,Tacneng , Ruth C.,Tarazi, Amine
SSRN
We examine the relationship between trust and financial development using detailed regional data in Turkey. We distinguish different forms of trust (i.e., generalized, narrow, and wide) and investigate whether varying degrees of generalized and narrow trust, as well as wide and narrow trust imply different financial development outcomes. Moreover, we assess how different forms of trust and their combination affect financial development in the presence of ethnically fragmented populations. We use instrumental variable (IV) estimations to address endogeneity issues and the potential reverse causality between trust and financial development. Our main results indicate that wide trust has a significantly positive impact on financial development. Moreover, in regions where narrow trust is relatively high, we find financial development benefits from increasing generalized trust. Our findings also highlight that whereas wide trust leads to more developed financial markets in more ethnically fragmented regions, generalized trust plays a stronger role in less fragmented ones. Further, we also analyze the impact of trust on the proportion of credit backed by stable funds such as deposits. Our findings show that generalized trust plays an important role in mitigating the adverse effects that ethnic fractionalization have on the availability of deposits or stable sources to fund loans. On the whole, our study highlights the importance of distinguishing the impact of different forms and combinations of trust. Generalized trust, which is the focus of most studies, is not an all-encompassing one-size-fits-all solution to enhance economic performance.

Un-Used Bank Capital Buffers and Credit Supply Shocks at SMEs During the Pandemic
Berrospide, Jose M.,Gupta, Arun,Seay, Matthew P.
SSRN
Did banks curb lending to creditworthy small and mid-sized enterprises (SME) during the COVID-19 pandemic? Sitting on top of minimum capital requirements, regulatory capital buffers introduced after the 2008 global financial crisis (GFC) are costly regions of Ã¢â‚¬Å"rainy dayÃ¢â‚¬ equity capital designed to absorb losses and provide lending capacity in a downturn. Using a novel set of confidential loan level data that includes private SME firms, we show that Ã¢â‚¬Å"buffer-constrainedÃ¢â‚¬ banks (those entering the pandemic with capital ratios close to this regulatory buffer region) reduced loan commitments to SME firms by an average of 1.4 percent more (quarterly) and were 4 percent more likely to end pre-existing lending relationships during the pandemic as compared to Ã¢â‚¬Å"buffer-unconstrainedÃ¢â‚¬ banks (those entering the pandemic with capital ratios far from the regulatory capital buffer region). We further find heterogenous effects across firms, as buffer-constrained banks disproportionately curtailed credit to three types of borrowers: (1) private, bank-dependent SME firms, (2) firms whose lending relationships were relatively young, and (3) firms whose pre-pandemic credit lines contractually matured at the start of the pandemic (and thus were up for renegotiation). While the post-2008 period saw the rise of banking system capital to historically high levels, these capital buffers went effectively unused during the pandemic. To the best of our knowledge, our study is the first to: (1) empirically test the usability of these Basel III regulatory buffers in a downturn, and (2) contribute a bank capital-based transmission channel to the literature studying the effects of the pandemic on SME firms.

Volatility of S&P500: Estimation and Evaluation
Wen Su
arXiv

In an era when derivatives is getting popular, risk management has gradually become the core content of modern finance. In order to study how to accurately estimate the volatility of the S&P 500 index, after introducing the theoretical background of several methods, this paper uses the historical volatility method, GARCH model method and implied volatility method to estimate the real volatility respectively. At the same time, two ways of adjusting the estimation window, rolling and increasing, are also considered. The unbiased test and goodness of fit test are used to evaluate these methods. The empirical result shows that the implied volatility is the best estimator of the real volatility. The rolling estimation window is recommended when using the historical volatility. On the contrary, the estimation window is supposed to be increased when using the GARCH model.