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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.