The existing approaches to sparse wealth allocations (1) are limited to low-dimensional setup when the number of assets is less than the sample size; (2) lack theoretical analysis of sparse wealth allocations and their impact on portfolio exposure; (3) are suboptimal due to the bias induced by an $\ell_1$-penalty. We address these shortcomings and develop an approach to construct sparse portfolios in high dimensions. Our contribution is twofold: from the theoretical perspective, we establish the oracle bounds of sparse weight estimators and provide guidance regarding their distribution. From the empirical perspective, we examine the merit of sparse portfolios during different market scenarios. We find that in contrast to non-sparse counterparts, our strategy is robust to recessions and can be used as a hedging vehicle during such times.

Gulisashvili et al. [Quant. Finance, 2018, 18(10), 1753-1765] provide a small-time asymptotics for the mass at zero under the uncorrelated stochastic-alpha-beta-rho (SABR) model by approximating the integrated variance with a moment-matched lognormal distribution. We improve the accuracy of the numerical integration by using the Gauss--Hermite quadrature. We further obtain the option price by integrating the constant elasticity of variance (CEV) option prices in the same manner without resorting to the small-strike volatility smile asymptotics of De Marco et al. [SIAM J. Financ. Math., 2017, 8(1), 709-737]. For the uncorrelated SABR model, the new option pricing method is accurate and arbitrage-free across all strike prices.

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.

We introduce a new definition of speculative bubbles in discrete-time models based on the discounted stock price losing mass at some finite drop-down under an equivalent martingale measure. We provide equivalent probabilistic characterisations of this definition and give examples of discrete-time martingales that are speculative bubbles and those that are not. In the Markovian case, we provide sufficient analytic conditions for the presence of speculative bubbles. We also show that the existence of speculative bubbles is directly linked to the existence of a non-trivial solution to a linear Volterra integral equation of the second kind involving the Markov kernel. Finally, we show that our definition of speculative bubbles in discrete time is consistent with the strict local martingale definition of speculative bubbles in continuous time in the sense that a properly discretised strict local martingale in continuous time is a speculative bubble in discrete time.

A polycentric approach to ecosystem service (ES) governance that combines individual incentives for interdependent ES providers with collective action is a promising lever to overcome the decline in ES and generate win-win solutions in agricultural landscapes. In this study, we explored the effectiveness of such an approach by focusing on incentives for managed pollination targeting either beekeepers or farmers who were either in communication with each other or not. We used a stylized bioeconomic model to simulate (i) the mutual interdependency through pollination in intensive agricultural landscapes and (ii) the economic and ecological impacts of introducing two beekeeping subsidies and one pesticide tax. The findings showed that incentives generated a spillover effect, affecting not only targeted stakeholders but non-targeted stakeholders as well as the landscape, and that this effect was amplified by communication. However, none of the simulated types of polycentric ES governance proved sustainable overall: subsidies showed excellent economic but low environmental performance, while the tax led to economic losses but was beneficial for the landscape. Based on these results, we identified three conditions for sustainable ES governance based on communication between stakeholders and incentives: (i) strong mutual interdependency (i.e. few alternatives exist for stakeholders), (ii) the benefits of communication outweigh the costs, and (iii) the incentivized ES drivers are not detrimental to other ES. Further research is needed to systematize which combination of individual payments and collaboration are sustainable in which conditions.

Factor strategies have gained growing popularity in industry with the fast development of machine learning. Usually, multi-factors are fed to an algorithm for some cross-sectional return predictions, which are further used to construct a long-short portfolio. Instead of predicting the value of the stock return, emerging studies predict a ranked stock list using the mature learn-to-rank technology. In this study, we propose a new listwise learn-to-rank loss function which aims to emphasize both the top and the bottom of a rank list. Our loss function, motivated by the long-short strategy, is endogenously shift-invariant and can be viewed as a direct generalization of ListMLE. Under different transformation functions, our loss can lead to consistency with binary classification loss or permutation level 0-1 loss. A probabilistic explanation for our model is also given as a generalized Plackett-Luce model. Based on a dataset of 68 factors in China A-share market from 2006 to 2019, our empirical study has demonstrated the strength of our method which achieves an out-of-sample annual return of 38% with the Sharpe ratio being 2.

This paper provides a novel five-component decomposition of optimal dynamic portfolio choice. It reveals the simultaneous impacts from market incompleteness and wealth-dependent utilities. The decomposition leads to implementation via either closed-form solutions or Monte Carlo simulations. With a nonrandom but time-varying interest rate, we can explicitly solve for the optimal policy under the wealth-dependent HARA utility. It is a combination of a bond holding scheme and the corresponding simpler CRRA strategy. Under incomplete markets with a stochastic volatility model estimated on US equity data, wealth-dependence introduces sophisticated cycle-dependence for optimal policy and hysteresis effect in Sharpe ratio.

Predicting future values at risk (fVaR) is an important problem in finance. They arise in the modelling of future initial margin requirements for counterparty credit risk and future market risk VaR. One is also interested in derived quantities such as: i) Dynamic Initial Margin (DIM) and Margin Value Adjustment (MVA) in the counterparty risk context; and ii) risk weighted assets (RWA) and Capital Value Adjustment (KVA) for market risk. This paper describes several methods that can be used to predict fVaRs. We begin with the Nested MC-empirical quantile method as benchmark, but it is too computationally intensive for routine use. We review several known methods and discuss their novel applications to the problem at hand.

The techniques considered include computing percentiles from distributions (Normal and Johnson) that were matched to parametric moments or percentile estimates, quantile regressions methods, and others with more specific assumptions or requirements.

We also consider how limited inner simulations can be used to improve the performance of these techniques. The paper also provides illustrations, results, and visualizations of intermediate and final results for the various approaches and methods.

Form 10-Q, the quarterly financial statement, is one of the most crucial filings for US public firms to disclose their financial and other relevant business operation information. Due to the gigantic number of 10-Q filings prevailing in the market for each quarter and diverse variations in the implementation of format given company-specific nature, it has long been a problem in the field to provide a generalized way to dissect and retrieve the itemized information. In this paper, we create a tool to itemize 10-Q filings using multi-stage processes, blending a rule-based algorithm with a CNN deep learning model. The implementation is an integrated pipeline which provides a solution to the item retrieval on a large scale. This would enable cross sectional and longitudinal textual analysis on massive number of companies.

Ever since its debut, generative adversarial networks (GANs) have attracted tremendous amount of attention. Over the past years, different variations of GANs models have been developed and tailored to different applications in practice. Meanwhile, some issues regarding the performance and training of GANs have been noticed and investigated from various theoretical perspectives. This subchapter will start from an introduction of GANs from an analytical perspective, then move on the training of GANs via SDE approximations and finally discuss some applications of GANs in computing high dimensional MFGs as well as tackling mathematical finance problems.

We present a new approximation scheme for the price and exercise policy of American options. The scheme is based on Hermite polynomial expansions of the transition density of the underlying asset dynamics and the early exercise premium representation of the American option price. The advantages of the proposed approach are threefold. First, our approach does not require the transition density and characteristic functions of the underlying asset dynamics to be attainable in closed form. Second, our approach is fast and accurate, while the prices and exercise policy can be jointly produced. Third, our approach has a wide range of applications. We show that the proposed approximations of the price and optimal exercise boundary converge to the true ones. We also provide a numerical method based on a step function to implement our proposed approach. Applications to nonlinear mean-reverting models, double mean-reverting models, Merton's and Kou's jump-diffusion models are presented and discussed.

The paper proposes a new algorithm for the high-dimensional financial data -- the Groupwise Interpretable Basis Selection (GIBS) algorithm, to estimate a new Adaptive Multi-Factor (AMF) asset pricing model, implied by the recently developed Generalized Arbitrage Pricing Theory, which relaxes the convention that the number of risk-factors is small. We first obtain an adaptive collection of basis assets and then simultaneously test which basis assets correspond to which securities, using high-dimensional methods. The AMF model, along with the GIBS algorithm, is shown to have a significantly better fitting and prediction power than the Fama-French 5-factor model.

The paper presents the results of a behavioral experiment conducted between February 2020 and March 2021 at Universit\`a Cattolica del Sacro Cuore, Milan Campus in which students were matched to either a human or a humanoid robotic partner to play an iterated Prisoner's Dilemma. The results of a Logit estimation procedure show that subjects are more likely to cooperate with human rather robotic partners; that are more likely to cooperate after receiving a dialogic verbal reaction following the realization of a sub-obtimal social outcome; that the effect of the verbal reaction is independent on the nature of the partner. Our findings provide new evidence on the effect of verbal communication in strategic frameworks. Results are robust to the exclusion of students of Economics related subjects, to the inclusion of a set of psychological and behavioral controls, to the way subjects perceive robots' behavior and to potential gender biases in human-human interactions.

The present study investigates the price (co)volatility of four dairy commodities -- skim milk powder, whole milk powder, butter and cheddar cheese -- in three major dairy markets. It uses a multivariate factor stochastic volatility model for estimating the time-varying covariance and correlation matrices by imposing a low-dimensional latent dynamic factor structure. The empirical results support four factors representing the European Union and Oceania dairy sectors as well as the milk powder markets. Factor volatilities and marginal posterior volatilities of each dairy commodity increase after the 2006/07 global (food) crisis, which also coincides with the free trade agreements enacted from 2007 onwards and EU and US liberalization policy changes. The model-implied correlation matrices show increasing dependence during the second half of 2006, throughout the first half of 2007, as well as during 2008 and 2014, which can be attributed to various regional agricultural dairy policies. Furthermore, in-sample value at risk measures (VaRs and CoVaRs) are provided for each dairy commodity under consideration.

In this article we analyse the oil-food price co-movement and its determinants in both time and frequency domains, using the wavelet analysis approach. Our results show that the significant local correlation between food and oil is only apparent. This is mainly due to the activity of commodity index investments and, to a lower extent, to the increasing demand from emerging economies. Furthermore, we employ the wavelet entropy to assess the predictability of the time series under consideration. We find that some variables share with both food and oil a similar predictability structure. These variables are those that mostly co-move with both oil and food. Some policy implications can be derived from our results, the most relevant being that the activity of commodity index investments is able to increase correlation between food and oil. This activity generates highly integrated markets and an increasing risk of joint price movements which is potentially dangerous in periods of economic downturn and financial stress. In our work we suggest that governments should also provide subsidy packages based on the commodity traded in commodity indices to protect producers and consumers from adverse price movements due to financial activity rather than lack of supply or demand.

Financial regulatory agencies are struggling to manage the systemic risks attributed to negative economic shocks. Preventive interventions are prominent to eliminate the risks and help to build a more resilient financial system. Although tremendous efforts have been made to measure multi-risk severity levels, understand the contagion behaviors and other risk management problems, there still lacks a theoretical framework revealing what and how regulatory intervention measurements can mitigate systemic risk. Here we demonstrate regshock, a practical visual analytical approach to support the exploration and evaluation of financial regulation measurements. We propose risk-island, an unprecedented risk-centered visualization algorithm to help uncover the risk patterns while preserving the topology of financial networks. We further propose regshock, a novel visual exploration and assessment approach based on the simulation-intervention-evaluation analysis loop, to provide a heuristic surgical intervention capability for systemic risk mitigation. We evaluate our approach through extensive case studies and expert reviews. To our knowledge, this is the first practical systemic method for the financial network intervention and risk mitigation problem; our validated approach potentially improves the risk management and control capabilities of financial experts.

Black-Scholes equation as one of the most celebrated mathematical models has an explicit analytical solution known as the Black-Scholes formula. Later variations of the equation, such as fractional or nonlinear Black-Scholes equations, do not have a closed form expression for the corresponding formula. In that case, one will need asymptotic expansions, including homotopy perturbation method, to give an approximate analytical solution. However, the solution is non-smooth at a special point. We modify the method by {first} performing variable transformations that push the point to infinity. As a test bed, we apply the method to the solvable Black-Scholes equation, where excellent agreement with the exact solution is obtained. We also extend our study to multi-asset basket and quanto options by reducing the cases to single-asset ones. Additionally we provide a novel analytical solution of the single-asset quanto option that is simple and different from the existing expression.

How does an entrepreneur's social capital improve small informal business productivity? Although studies have investigated this relationship, we still know little about the underlying theoretical mechanisms driving these findings. Using a unique Zambian Business Survey of 1,971 entrepreneurs administered by the World Bank, we find an entrepreneur's social capital facilitates small business productivity through the mediating channels of firm financing and customer relationships. Our findings identify specific mechanisms that channel social capital toward an informal business' productivity, which prior studies have overlooked.

This brief technical note introduces PRZI (Parameterised-Response Zero Intelligence), a new form of zero-intelligence trader intended for use in simulation studies of auction markets. Like Gode & Sunder's classic Zero-Intelligence Constrained (ZIC) trader, PRZI generates quote-prices from a random distribution over some specified domain of discretely-valued allowable quote-prices. Unlike ZIC, which uses a uniform distribution to generate prices, the probability distribution in a PRZI trader is parameterised in such a way that its probability mass function (PMF) is determined by a real-valued control variable s in the range [-1.0, +1.0] that determines the strategy for that trader. When s is zero, a PRZI trader behaves identically to the ZIC strategy, with a flat/rectangular PMF; but when s is close to plus or minus one the PRZI trader's PMF becomes asymptotically maximally skewed to one extreme or the other of the price-range, thereby enabling the PRZI trader to act in the same way as the "Shaver" strategy (SHVR) or the "Giveaway" strategy (GVWY), both of which have recently been demonstrated to be surprisingly dominant over more sophisticated, and supposedly more profitable, trader-strategies that incorporate adaptive mechanisms and machine learning. Depending on the value of s, a PRZI trader will behave either as a ZIC, or as a SHVR, or as a GVWY, or as some hybrid strategy part-way between two of these three previously-reported strategies. The novel smoothly-varying strategy in PRZI has value in giving trader-agents plausibly useful "market impact" responses to imbalances in an auction-market's limit-order-book, and also allows for the study of co-adaptive dynamics in continuous strategy-spaces rather than the discrete spaces that have traditionally been studied in the literature.

Brazil rose as a global powerhouse producer of soybeans and corn over the past 15 years has fundamentally changed global markets in these commodities. This is arguably due to the development of varieties of soybean and corn adapted to climates within Brazil, allowing farmers to double-crop corn after soybeans in the same year. Corn and soybean market participants increasingly look to Brazil for fundamental price information, and studies have shown that the two markets have become cointegrated. However little is known about how much volatility from each market spills over to the other. In this article we measure volatility spillover ratios between U.S. and Brazilian first crop corn, second crop corn, and soybeans. We find that linkages between the two countries increased after double cropping corn after soybeans expanded, volatility spillover magnitudes expanded, and the direction of volatility spillovers flipped from U.S. volatility spilling over to Brazil before double cropping, to Brazil spilling over to U.S. after double cropping.

The paper provides a new explanation of the low-volatility anomaly. We use the Adaptive Multi-Factor (AMF) model estimated by the Groupwise Interpretable Basis Selection (GIBS) algorithm to find those basis assets significantly related to low and high volatility portfolios. These two portfolios load on very different factors, indicating that volatility is not an independent risk, but that it's related to existing risk factors. The out-performance of the low-volatility portfolio is due to the (equilibrium) performance of these loaded risk factors. The AMF model outperforms the Fama-French 5-factor model both in-sample and out-of-sample.

Consider a financial market with nonnegative semimartingales which does not need to have a num\'{e}raire. We are interested in the absence of arbitrage in the sense that no self-financing portfolio gives rise to arbitrage opportunities, where we are allowed to add a savings account to the market. We will prove that in this sense the market is free of arbitrage if and only if there exists an equivalent local martingale deflator which is a multiplicative special semimartingale. In this case, the additional savings account relates to the finite variation part of the multiplicative decomposition of the deflator.

The purpose of this paper is to test the time-invariance of the beta coefficients estimated by the Adaptive Multi-Factor (AMF) model. The AMF model is implied by the generalized arbitrage pricing theory (GAPT), which implies constant beta coefficients. The AMF model utilizes a Groupwise Interpretable Basis Selection (GIBS) algorithm to identify the relevant factors from among all traded ETFs. We compare the AMF model with the Fama-French 5-factor (FF5) model. We show that for nearly all time periods with length less than 6 years, the beta coefficients are time-invariant for the AMF model, but not for the FF5 model. This implies that the AMF model with a rolling window (such as 5 years) is more consistent with realized asset returns than is the FF5 model.

Unemployment benefits in the US were extended by up to 73 weeks during the Great Recession. Equilibrium labor market theory indicates that extensions of benefit duration impact not only search decisions by job seekers but also job vacancy creations by employers. Most of the literature focused on the former to show partial equilibrium effect that increment of unemployment benefits discourage job search and lead to a rise in unemployment. To study the total effect of UI benefit extensions on unemployment, I follow border county identification strategy, take advantage of quasi-differenced specification to control for changes in future benefit policies, apply interactive fixed effects model to deal with unobserved shocks so as to obtain unbiased and consistent estimation. I find that benefit extensions have a statistically significant positive effect on unemployment, which is consistent with the results of prevailing literature.

The rigorous evaluation of anti-poverty programs is key to the fight against global poverty. Traditional evaluation approaches rely heavily on repeated in-person field surveys to measure changes in economic well-being and thus program effects. However, this is known to be costly, time-consuming, and often logistically challenging. Here we provide the first evidence that we can conduct such program evaluations based solely on high-resolution satellite imagery and deep learning methods. Our application estimates changes in household welfare in the context of a recent anti-poverty program in rural Kenya. The approach we use is based on a large literature documenting a reliable relationship between housing quality and household wealth. We infer changes in household wealth based on satellite-derived changes in housing quality and obtain consistent results with the traditional field-survey based approach. Our approach can be used to obtain inexpensive and timely insights on program effectiveness in international development programs.

This study examines how foreign aid and institutions affect entrepreneurship activity following natural disasters. We use insights from the entrepreneurship, development, and institutions literature to develop a model of entrepreneurship activity in the aftermath of natural disasters. First, we hypothesize the effect of natural disasters on entrepreneurship activity depends on the amount of foreign aid received. Second, we hypothesize that natural disasters and foreign aid either encourages or discourages entrepreneurship activity depending on two important institutional conditions: the quality of government and economic freedom. The findings from our panel of 85 countries from 2006 to 2016 indicate that natural disasters are negatively associated with entrepreneurship activity, but both foreign aid and economic freedom attenuate this effect. In addition, we observe that foreign aid is positively associated with entrepreneurship activity but only in countries with high quality government. Hence, we conclude that the effect of natural disasters on entrepreneurship depends crucially on the quality of government, economic freedom, and foreign aid. Our findings provide new insights into how natural disasters and foreign aid affect entrepreneurship and highlight the important role of the institutional context.

One important dimension of Conditional Cash Transfer Programs apart from conditionality is the provision of continuous frequency of payouts. On the contrary, the Apni Beti Apna Dhan program, implemented in the state of Haryana in India from 1994 to 1998 offers a promised amount to female beneficiaries redeemable only after attaining 18 years of age if she remains unmarried. This paper assesses the impact of this long-term financial incentivization on outcomes, not directly associated with the conditionality. Using multiple datasets in a triple difference framework, the findings reveal a significant positive impact on years of education though it does not translate into gains in labor participation. While gauging the potential channels, we did not observe higher educational effects beyond secondary education. Additionally, impact on time allocation for leisure, socialization or self-care, age of marriage beyond 18 years, age at first birth, and post-marital empowerment indicators are found to be limited. These evidence indicate failure of the program in altering the prevailing gender norms despite improvements in educational outcomes. The paper recommends a set of complementary potential policy instruments that include altering gender norms through behavioral interventions skill development and incentives to encourage female work participation.