The use of multiple hypothesis testing adjustments varies widely in applied economic research, without consensus on when and how it should be done. We provide a game-theoretic foundation for this practice. Adjustments are often - but not always - appropriate in our framework when research influences multiple policy decisions. These adjustments depend on the nature of scale economies in the research production function and on economic interactions between policy decisions, with control of classical notions of compound error rates emerging in some but not all cases. When research examines multiple outcomes, on the other hand, this motivates either very conservative testing procedures or aggregating outcomes into sufficient statistics for policy-making.

Each individual in society experiences an evolution of their income during their lifetime. Macroscopically, this dynamics creates a statistical relationship between age and income for each society. In this study, we investigate income distribution and its relationship with age and identify a stable joint distribution function for age and income within the United Kingdom and the United States. We demonstrate a flexible calibration methodology using panel and population surveys and capture the characteristic differences between the UK and the US populations. The model here presented can be utilised for forecasting income and planning pensions.

In today's economy, selling a new zero-marginal cost product is a real challenge, as it is difficult to determine a product's "correct" sales price based on its profit and dissemination. As an example, think of the price of a new app or video game. New sales mechanisms for selling this type of product need to be designed, in particular ones that consider consumer preferences and reality. Current auction mechanisms establish a time deadline for the auction to take place. This deadline is set to increase the number of bidders and thus the final offering price. Consumers want to obtain the product as quickly as possible from the moment they become interested in it, and this time does not always coincide with the seller's deadline. Naturally, consumers also want to pay a price they consider "fair". Here we introduce an auction model where buyers continuously place bids and the challenge is to decide quickly whether or not to accept them. The model does not include a deadline for placing bids, and exhibits self-organized criticality; it presents a critical price from which a bid is accepted with probability one, and avalanches of sales above this value are observed. This model is of particular interest for startup companies interested in profit as well as making the product known on the market.

This paper presents the advantages of alternative data from Super-Apps to enhance user' s income estimation models. It compares the performance of these alternative data sources with the performance of industry-accepted bureau income estimators that takes into account only financial system information; successfully showing that the alternative data manage to capture information that bureau income estimators do not. By implementing the TreeSHAP method for Stochastic Gradient Boosting Interpretation, this paper highlights which of the customer' s behavioral and transactional patterns within a Super-App have a stronger predictive power when estimating user' s income. Ultimately, this paper shows the incentive for financial institutions to seek to incorporate alternative data into constructing their risk profiles.

This paper provides a fundamental study of the consistency of insurance contracts. We start from the observation that most insurance contracts are inherently linked to financial markets.. However insurance markets differ from those of finance since there is no trade for once purchased contracts; buying and selling simultaneously at different prices is not possible and the definition of finance arbitrage is not applicable. In addition, the fact that the insurer's information is much larger than the publicly available one used on financial markets gives rise to two different filtrations.

By defining strategies on an insurance portfolio and combining them with financial trading strategies, we arrive at the notion of insurance-finance arbitrage (IFA). In analogy to the classical fundamental theorem of asset pricing, we give a fundamental theorem on the absence of IFA, leading to the existence of an insurance-finance-consistent probability. Hereby we include the case where the insurers' filtration allows financial arbitrage.

In its generality, the insurance-finance consistent probability is of little use for insurance companies, since it has no relation whatsoever to statistical data. Instead, for coping at least with market- (or finance-) consistency, a simple valuation rule is used, constructed as a superposition of the statistical measure $P$ and a finance-consistent measure $Q$ defined on the restricted market filtration. For this rule, our main theorem provides two sufficient conditions for presence or absence of IFA, respectively.

The generality of the approach allows to incorporate many important aspects, like mortality risk or dependence of mortality and stock markets. Utilizing the theory of enlargements of filtrations, we construct a tractable framework for insurance-finance consistent valuation.

Most trading in cryptocurrency options is on inverse products, so called because the contract size is denominated in US dollars and they are margined and settled in crypto, typically bitcoin or ether. Their popularity stems from allowing professional traders in bitcoin or ether options to avoid transferring fiat currency to and from the exchanges. We derive new analytic pricing and hedging formulae for inverse options under the assumption that the underlying follows a geometric Brownian motion. The boundary conditions and hedge ratios exhibit relatively complex but very important new features which warrant further analysis and explanation. We also illustrate some inconsistencies, exhibited in time series of Deribit bitcoin option implied volatilities, which indicate that traders may be applying direct option hedging and valuation methods erroneously. This could be because they are unaware of the correct, inverse option characteristics which are derived in this paper.

Why do household saving rates differ so much across countries? This micro-level question has global implications: countries that systematically "oversave" export capital by running current account surpluses. In the recipient countries, interest rates are thus too low and financial stability is put at risk. Existing theories argue that saving is precautionary, but tests are limited to cross-country comparisons and are not always supportive. We report the findings of an original survey experiment. Using a simulated financial saving task implemented online, we compare the saving preferences of a large and diverse sample of Chinese-Canadians with other Canadians. This comparison is instructive given that Chinese-Canadians migrated from, or descend from those who migrated from, a high-saving environment to a low-savings, high-debt environment. We also compare behavior in the presence and absence of a simulated "welfare state," which we represent in the form of mandatory insurance. Our respondents exhibit behavior in the saving task that corresponds to standard economic assumptions about lifecycle savings and risk aversion. We find strong evidence that precautionary saving is reduced when a mandatory insurance is present, but no sign that Chinese cultural influences - represented in linguistic or ethnic terms - have any effect on saving behavior.

In electronic trading markets often only the price or volume time series, that result from interaction of multiple market participants, are directly observable. In order to test trading strategies before deploying them to real-time trading, multi-agent market environments calibrated so that the time series that result from interaction of simulated agents resemble historical are often used. To ensure adequate testing, one must test trading strategies in a variety of market scenarios -- which includes both scenarios that represent ordinary market days as well as stressed markets (most recently observed due to the beginning of COVID pandemic). In this paper, we address the problem of multi-agent simulator parameter calibration to allow simulator capture characteristics of different market regimes. We propose a novel two-step method to train a discriminator that is able to distinguish between "real" and "fake" price and volume time series as a part of GAN with self-attention, and then utilize it within an optimization framework to tune parameters of a simulator model with known agent archetypes to represent a market scenario. We conclude with experimental results that demonstrate effectiveness of our method.

This paper uses the panel data of Chinese listed companies from 2007 to 2019, uses the relaxation of China's margin trading and short selling restrictions as the basis of quasi experimental research, and then constructs a double difference model to analyze whether the margin trading and short selling will encourage enterprises to engage in green technology innovation activities. Firstly, our research results show that after the implementation of the margin trading and short selling, the green technology innovation behavior of pilot companies will increase significantly. We believe that the short selling threat and pressure brought by short selling to enterprises are the main reasons for pilot enterprises to engage in green technology innovation. Secondly, the empirical results show that the implementation of margin trading and short selling will significantly promote the quantity of green technology innovation of pilot enterprises, but will not significantly promote the quality of green technology innovation of pilot enterprises. Furthermore, we analyze the difference of the impact of margin trading and short selling on the quantity of green technology innovation of pilot enterprises in different periods. Finally, we find that the performance decline, yield gap between financial assets and operating assets, the risk of stock price crash, management shareholding, institutional shareholding ratio, product market competition, bull market and formal environmental regulation intensity will affect the role of policy in promoting green technology innovation of pilot enterprises.

This paper studies a mean field game (MFG) problem in a market with a large population of heterogeneous agents. Each agent aims to maximize the terminal wealth under a CRRA type relative performance, in which the interaction occurs by the competition with peers. We start from the model with n agents, in which the underlying risky assets subject to a common noise and contagious jump risk modelled by a multi-dimensional Hawkes process. With a continuum of agents, we formulate the MFG problem and characterize a deterministic mean field equilibrium in an analytical form, allowing us to investigate some impacts of model parameters in the limiting model and discuss the financial implications. More importantly, it is shown that this mean field equilibrium can serve as an approximate Nash equilibrium for the n-player game problem when n is sufficiently large. The explicit order of the approximation error is also derived.

Quantifying tail dependence is an important issue in insurance and risk management. The prevalent tail dependence coefficient (TDC), however, is known to underestimate the degree of tail dependence and it fails to capture non-exchangeable tail dependence since the TDC evaluates the limiting tail probability only along the main diagonal. To overcome these issues, two novel tail dependence measures called the maximal tail concordance measure (MTCM) and the average tail concordance measure (ATCM) are proposed. Both measures are constructed based on tail copulas and possess clear probabilistic interpretations in that the MTCM evaluates the largest limiting probability among all comparable rectangles in the tail, and the ATCM is a normalized average of these limiting probabilities. In contrast to the TDC, the MTCM and the ATCM can capture non-exchangeable tail dependence. Moreover, they often admit analytical forms, and satisfy axiomatic properties naturally required to quantify tail dependence. Estimators of the two measures are also constructed. A real data analysis reveals striking tail dependence and tail non-exchangeability of the return series of stock indices, particularly in periods of financial distress.

This study examines the impact of nighttime light intensity on child health outcomes in Bangladesh. We use nighttime light intensity as a proxy measure of urbanization and argue that the higher intensity of nighttime light, the higher is the degree of urbanization, which positively affects child health outcomes. In econometric estimation, we employ a methodology that combines parametric and non-parametric approaches using the Gradient Boosting Machine (GBM), K-Nearest Neighbors (KNN), and Bootstrap Aggregating that originate from machine learning algorithms. Based on our benchmark estimates, findings show that one standard deviation increase of nighttime light intensity is associated with a 1.515 rise of Z-score of weight for age after controlling for several control variables. The maximum increase of weight for height and height for age score range from 5.35 to 7.18 units. To further understand our benchmark estimates, generalized additive models also provide a robust positive relationship between nighttime light intensity and children's health outcomes. Finally, we develop an economic model that supports the empirical findings of this study that the marginal effect of urbanization on children's nutritional outcomes is strictly positive.

Mastering semiconductor technology is essential to insert any country into the trends of the future, such as smart cities, internet of things, space exploration, etc. In this paper we present the growing annual revenue of the semiconductor industry in the last 20 years and comment on the importance of mastering semiconductor production technology and its implications for the development of a nation.

We study the convergence properties of the short maturity expansion of option prices in the uncorrelated log-normal ($\beta=1$) SABR model. In this model the option time-value can be represented as an integral of the form $V(T) = \int_{0}^\infty e^{-\frac{u^2}{2T}} g(u) du$ with $g(u)$ a "payoff function" which is given by an integral over the McKean kernel $G(s,t)$. We study the analyticity properties of the function $g(u)$ in the complex $u$-plane and show that it is holomorphic in the strip $|\Im(u) |< \pi$. Using this result we show that the $T$-series expansion of $V(T)$ and implied volatility are asymptotic (non-convergent for any $T>0$). In a certain limit which can be defined either as the large volatility limit $\sigma_0\to \infty$ at fixed $\omega=1$, or the small vol-of-vol limit $\omega\to 0$ limit at fixed $\omega\sigma_0$, the short maturity $T$-expansion for the implied volatility has a finite convergence radius $T_c = \frac{1.32}{\omega\sigma_0}$.

We develop FinText, a novel, state-of-the-art, financial word embedding from Dow Jones Newswires Text News Feed Database. Incorporating this word embedding in a machine learning model produces a substantial increase in volatility forecasting performance on days with volatility jumps for 23 NASDAQ stocks from 27 July 2007 to 18 November 2016. A simple ensemble model, combining our word embedding and another machine learning model that uses limit order book data, provides the best forecasting performance for both normal and jump volatility days. Finally, we use Integrated Gradients and SHAP (SHapley Additive exPlanations) to make the results more 'explainable' and the model comparisons more transparent.

The economic downturn and the air travel crisis triggered by the recent coronavirus pandemic pose a substantial threat to the new consumer class of many emerging economies. In Brazil, considerable improvements in social inclusion have fostered the emergence of hundreds of thousands of first-time fliers over the past decades. We apply a two-step regression methodology in which the first step consists of identifying air transport markets characterized by greater social inclusion, using indicators of the local economies' income distribution, credit availability, and access to the Internet. In the second step, we inspect the drivers of the plunge in air travel demand since the pandemic began, differentiating markets by their predicted social inclusion intensity. After controlling for potential endogeneity stemming from the spread of COVID-19 through air travel, our results suggest that short and low-density routes are among the most impacted airline markets and that business-oriented routes are more impacted than leisure ones. Finally, we estimate that a market with 1 per cent higher social inclusion is associated with a 0.153 per cent to 0.166 per cent more pronounced decline in demand during the pandemic. Therefore, markets that have benefited from greater social inclusion in the country may be the most vulnerable to the current crisis.

Time and the choice of measurement time scales is fundamental to how we choose to represent information and data in finance. This choice implies both the units and the aggregation scales for the resulting statistical measurables used to describe a financial system. It also defines how we measure the relationship between different traded instruments. As we move from high-frequency time scales, when individual trade and quote events occur, to the mesoscales when correlations emerge in ways that can conform to various latent models; it remains unclear what choice of time and sampling rates are appropriate to faithfully capture system dynamics and asset correlations for decision making. The Epps effect is the key phenomenology that couples the emergence of correlations to the choice of sampling time scales. Here we consider and compare the Epps effect under different sampling schemes in order to contrast three choices of time: calendar time, volume time and trade time. Using a toy model based on a Hawkes process, we are able to achieve simulation results that conform well with empirical dynamics. Concretely, we find that the Epps effect is present under all three definitions of time and that correlations emerge faster under trade time compared to calendar time, whereas correlations emerge linearly under volume time.

We attempt to reconcile Gabaix and Koijen's (GK) recent Inelastic Market Hypothesis with the order-driven view of markets that emerged within the microstructure literature in the past 20 years. We review the most salient empirical facts and arguments that give credence to the idea that market price fluctuations are mostly due to order flow, whether informed or non-informed. We show that the Latent Liquidity Theory of price impact makes a precise prediction for GK's multiplier $M$, which measures by how many dollars, on average, the market value of a company goes up if one buys one dollar worth of its stocks. Our central result is that $M$ increases with the volatility of the stock and decreases with the fraction of the market cap. that is traded daily. We discuss several empirical results suggesting that the lion's share of volatility is due to trading activity.

We investigate a dividend maximization problem under stochastic interest rates with Ornstein-Uhlenbeck dynamics. This setup also takes negative rates into account. First a deterministic time is considered, where an explicit separating curve $\alpha(t)$ can be found to determine the optimal strategy at time $t$. In a second setting we introduce a strategy-independent stopping time. The properties and behavior of these optimal control problems in both settings are analyzed in an analytical HJB-driven approach as well as using backward stochastic differential equations.

This paper presents evidence of an informational effect in changes of the federal funds rate around FOMC announcements by exploiting exchange rate variations for a panel of emerging economies. For several FOMC announcements dates, emerging market economies' exchange rate strengthened relative to the US dollar, in contrast to what the standard theory predicts. These results are in line with the information effect, which denote the Federal Reserve's disclosure of information about the state of the economy. Using Jarocinski \& Karadi 2020's identification scheme relying on sign restrictions and high-frequency surprises of multiple financial instruments, I show how different US monetary policy shocks imply different spillovers on emerging markets financial flows and macroeconomic performance. I emphasize the contrast in dynamics of financial flows and equity indexes and how different exchange rate regimes shape aggregate fluctuations. Using a structural DSGE model and IRFs matching techniques I argue that ignoring information shocks bias the inference over key frictions for small open economy models.

We prove the existence of incomplete Radner equilibria in two models with exponential investors and different types of noise traders: an exogenous noise trader and an endogenous noise tracker. In each model, we analyze a coupled system of ODEs and reduce it to a system of two coupled ODEs in order to establish equilibrium existence. As an application, we study the impact of the noise trader types on welfare. We show that the aggregate welfare comparison depends in a non-trivial manner on every equilibrium parameter, and there is no ordering in general. Our models suggest that care should be used when drawing conclusions about welfare effects when noise traders are used.

This study investigates the relationship between patenting activity, productivity, and market competition at the firm level. We focus on the Information and Communication Technology (ICT) industry as a particular case of an innovative sector whose contribution to modern economies is pivotal. For our purpose, we exploit financial accounts and patenting activity in 2009-2017 by 179,660 companies operating in 39 countries. Our identification strategy relies on the most recent approaches for a difference-in-difference setup in the presence of multiple periods and with variation in treatment time. We find that companies being granted patents increase on average market shares by 11%, firm size by 12%, and capital intensity by 10%. Notably, we do not register a significant impact of patenting on firms' productivity after challenging results for reverse causality and robustness checks. Findings are robust after we consider ownership structures separating patents owned by parent companies and their subsidiaries. We complement our investigation with an analysis of market allocation dynamics. Eventually, we argue that policymakers should reconsider the trade-off between IPR protection and market competition, especially when the benefits to firms' competitiveness are not immediately evident.