We perform a quantitative analysis of the gain/loss asymmetry for financial time series by using a Bayesian approach. In particular, we focus on some selected indices and analyze the statistical significance of the asymmetry amount through a Bayesian generalization of the t-Test, which relaxes the normality assumption on the underlying distribution. We propose two different models for data distribution, we study the convergence of our method and we provide several graphical representations of our numerical results. Finally, we perform a sensitivity analysis with respect to model parameters in order to study the reliability and robustness of our results.

We study a class of deterministic mean field games on finite and infinite time horizons arising in models of optimal exploitation of exhaustible resources. The main characteristic of our game is an absorption constraint on the players' state process. As a result of the state constraint the optimal time of absorption becomes part of the equilibrium. This requires a novel approach when applying Pontyagin's maximum principle. We prove the existence and uniqueness of equilibria and solve the infinite horizon models in closed form. As players may drop out of the game over time, equilibrium production rates need not be monotone nor smooth.

We derive a change of variable formula for $C^1$ functions $U:\mathbb{R}_+\times\mathbb{R}^m\to\mathbb{R}$ whose second order spatial derivatives may explode and not be integrable in the neighbourhood of a surface $b:\mathbb{R}_+\times\mathbb{R}^{m-1}\to \mathbb{R}$ that splits the state space into two sets $\mathcal{C}$ and $\mathcal{D}$. The formula is tailored for applications in problems of optimal stopping where it is generally very hard to control the second derivatives of the value function near the optimal stopping boundary. Differently to other existing papers on similar topics we only require that the surface $b$ be monotonic in each variable and we formally obtain the same expression as the classical It\^o's formula.

In this paper, we consider the portfolio optimization problem in a financial market under a general utility function. Empirical results suggest that if a significant market fluctuation occurs, invested wealth tends to have a notable change from its current value. We consider an incomplete stochastic volatility market model, that is driven by both a Brownian motion and a jump process. At first, we obtain a closed-form formula for an approximation to the optimal portfolio in a small-time horizon. This is obtained by finding the associated Hamilton-Jacobi-Bellman integro-differential equation and then approximating the value function by constructing appropriate super-solution and sub-solution. It is shown that the true value function can be obtained by sandwiching the constructed super-solution and sub-solution. We also prove the accuracy of the approximation formulas. Finally, we provide a procedure for generating a close-to-optimal portfolio for a finite time horizon.

Various measures have been taken in different countries to mitigate the Covid-19 epidemic. But, throughout the world, many citizens don't understand well how these measures are taken and even question the decisions taken by their government. Should the measures be more (or less) restrictive? Are they taken for a too long (or too short) period of time? To provide some quantitative elements of response to these questions, we consider the well-known SEIR model for the Covid-19 epidemic propagation and propose a pragmatic model of the government decision-making operation. Although simple and obviously improvable, the proposed model allows us to study the tradeoff between health and economic aspects in a pragmatic and insightful way. Assuming a given number of phases for the epidemic and a desired tradeoff between health and economic aspects, it is then possible to determine the optimal duration of each phase and the optimal severity level for each of them. The numerical analysis is performed for the case of France but the adopted approach can be applied to any country. One of the takeaway messages of this analysis is that being able to implement the optimal 4-phase epidemic management strategy in France would have led to 1.05 million infected people and a GDP loss of 231 billion euro instead of 6.88 million of infected and a loss of 241 billion euro. This indicates that, seen from the proposed model perspective, the effectively implemented epidemic management strategy is good economically, whereas substantial improvements might have been obtained in terms of health impact. Our analysis indicates that the lockdown/severe phase should have been more severe but shorter, and the adjustment phase occurred earlier. Due to the natural tendency of people to deviate from the official rules, updating measures every month over the whole epidemic episode seems to be more appropriate.

In this paper, we investigate a fully nonlinear evolutionary Hamilton-Jacobi-Bellman (HJB) parabolic equation utilizing the monotone operator technique. We consider the HJB equation arising from portfolio optimization selection, where the goal is to maximize the conditional expected value of the terminal utility of the portfolio. The fully nonlinear HJB equation is transformed into a quasilinear parabolic equation using the so-called Riccati transformation method. The transformed parabolic equation can be viewed as the porous media type of equation with source term. Under some assumptions, we obtain that the diffusion function to the quasilinear parabolic equation is globally Lipschitz continuous, which is a crucial requirement for solving the Cauchy problem. We employ Banach's fixed point theorem to obtain the existence and uniqueness of a solution to the general form of the transformed parabolic equation in a suitable Sobolev space in an abstract setting. Some financial applications of the proposed result are presented in one-dimensional space.

In this paper, I reply to the recent article by Jeffery and Verheijen (2020) 'A new soil health policy paradigm: Pay for practice not performance!'. While expressing support for their call for a more pronounced role of soil protection in agri-environmental policy, I critically discuss the two main elements of their specific proposal: its emphasis of the concept of soil health and the recommendation to use action-based payments as the main policy instrument. I argue for using soil functions as a more established concept (and thus more adequate for policy purposes), which is also informationally richer than soil health. Furthermore, I provide a more differentiated discussion of the relative advantages and disadvantages of result-based and action-based payments, while addressing the specific criticisms towards the former that Jeffery and Verheijen voice. Also, I suggest an alternative approach (a hybrid model-based scheme) that addresses the limitations of both Jeffery and Verheijen's own proposal and the valid criticisms they direct at result-based payments.

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.

Financial markets are a source of non-stationary multidimensional time series which has been drawing attention for decades. Each financial instrument has its specific changing over time properties, making their analysis a complex task. Improvement of understanding and development of methods for financial time series analysis is essential for successful operation on financial markets. In this study we propose a volume-based data pre-processing method for making financial time series more suitable for machine learning pipelines. We use a statistical approach for assessing the performance of the method. Namely, we formally state the hypotheses, set up associated classification tasks, compute effect sizes with confidence intervals, and run statistical tests to validate the hypotheses. We additionally assess the trading performance of the proposed method on historical data and compare it to a previously published approach. Our analysis shows that the proposed volume-based method allows successful classification of the financial time series patterns, and also leads to better classification performance than a price action-based method, excelling specifically on more liquid financial instruments. Finally, we propose an approach for obtaining feature interactions directly from tree-based models on example of CatBoost estimator, as well as formally assess the relatedness of the proposed approach and SHAP feature interactions with a positive outcome.

We use rank correlations as distance functions to establish the interconnectivity between stock returns, building weighted signed networks for the stocks of seven European countries, the US and Japan. We establish the theoretical relationship between the level of balance in a network and stock predictability, studying its evolution from 2005 to the third quarter of 2020. We find a clear balance-unbalance transition for six of the nine countries, following the August 2011 Black Monday in the US, when the Economic Policy Uncertainty index for this country reached its highest monthly level before the COVID-19 crisis. This sudden loss of balance is mainly caused by a reorganization of the market networks triggered by a group of low capitalization stocks belonging to the non-financial sector. After the transition, the stocks of companies in these groups become all negatively correlated between them and with most of the rest of the stocks in the market. The implied change in the network topology is directly related to a decrease in stocks predictability, a finding with novel important implications for asset allocation and portfolio hedging strategies.

There is an emerging consensus in the literature that locally embedded capabilities and industrial know-how are key determinants of growth and diversification processes. In order to model these dynamics as a branching process, whereby industries grow as a function of the availability of related or relevant skills, industry networks are typically employed. These networks, sometimes referred to as industry spaces, describe the complex structure of the capability or skill overlap between industry pairs, measured here via inter-industry labour flows. Existing models typically deploy a local or 'nearest neighbour' approach to capture the size of the labour pool available to an industry in related sectors. This approach, however, ignores higher order interactions in the network, and the presence of industry clusters or groups of industries which exhibit high internal skill overlap. We argue that these clusters represent skill basins in which workers circulate and diffuse knowledge, and delineate the size of the skilled labour force available to an industry. By applying a multi-scale community detection algorithm to this network of flows, we identify industry clusters on a range of scales, from many small clusters to few large agglomerations. We construct a new variable, cluster employment, which captures the workforce available to an industry within its own cluster. Using a new dataset from Ireland, we show that this variable is predictive of industry employment growth, particularly in services. Furthermore, exploiting the multi-scale nature of the industrial clusters detected, we propose a methodology to uncover the optimal scale at which labour pooling operates.

This paper studies the multilevel Monte-Carlo estimator for the expectation of a maximum of conditional expectations. This problem arises naturally when considering many stress tests and appears in the calculation of the interest rate module of the standard formula for the SCR. We obtain theoretical convergence results that complements the recent work of Giles and Goda and gives some additional tractability through a parameter that somehow describes regularity properties around the maximum. We then apply the MLMC estimator to the calculation of the SCR at future dates with the standard formula for an ALM savings business on life insurance. We compare it with estimators obtained with Least Square Monte-Carlo or Neural Networks. We find that the MLMC estimator is computationally more efficient and has the main advantage to avoid regression issues, which is particularly significant in the context of projection of a balance sheet by an insurer due to the path dependency. Last, we discuss the potentiality of this numerical method and analyze in particular the effect of the portfolio allocation on the SCR at future~dates.

Social norms are rules and standards of expected behavior that emerge in societies as a result of information exchange between agents. This paper studies the effects of emergent social norms on the performance of teams. We use the $N\!K$-framework to build an agent-based model, in which agents work on a set of interdependent tasks and exchange information regarding their past behavior with their peers. Social norms emerge from these interactions. We find that social norms come at a cost for the overall performance, unless tasks assigned to the team members are highly correlated, and the effect is stronger when agents share information regarding more tasks, but is unchanged when agents communicate with more peers. Finally, we find that the established finding that the team-based incentive schemes improve performance for highly complex tasks still holds in presence of social norms.

Designing robust systems for precise prediction of future prices of stocks has always been considered a very challenging research problem. Even more challenging is to build a system for constructing an optimum portfolio of stocks based on the forecasted future stock prices. We present a deep learning-based regression model built on a long-and-short-term memory network (LSTM) network that automatically scraps the web and extracts historical stock prices based on a stock's ticker name for a specified pair of start and end dates, and forecasts the future stock prices. We deploy the model on 75 significant stocks chosen from 15 critical sectors of the Indian stock market. For each of the stocks, the model is evaluated for its forecast accuracy. Moreover, the predicted values of the stock prices are used as the basis for investment decisions, and the returns on the investments are computed. Extensive results are presented on the performance of the model. The analysis of the results demonstrates the efficacy and effectiveness of the system and enables us to compare the profitability of the sectors from the point of view of the investors in the stock market.

A new class of measures of bivariate tail dependence called tail concordance measures (TCMs) is proposed, which is defined as the limit of a measure of concordance of the underlying copula restricted to the tail region of interest. TCMs captures the extremal relationship between random variables not only along the diagonal but also along all angles weighted by a tail generating measure. Axioms of tail dependence measures are introduced, and TCMs are shown to characterize linear tail dependence measures. The infimum and supremum of TCMs over all generating measures are considered to investigate the issue of under- and overestimation of the degree of extreme co-movements. The infimum is shown to be attained by the classical tail dependence coefficient, and thus the classical notion always underestimates the degree of tail dependence. A formula for the supremum TCM is derived and shown to overestimate the degree of extreme co-movements. Estimators of the proposed measures are studied, and their performance is demonstrated in numerical experiments. For a fair assessment of tail dependence and stability of the estimation under small sample sizes, TCMs weighted over all angles are suggested, with tail Spearman's rho and tail Gini's gamma being interesting novel special cases of TCMs.

More than half a century has passed since the Great Chinese Famine (1959-1961), and China has transformed from a poor, underdeveloped country to the world's leading emerging economy. Does the effect of the famine persist today? To explore this question, we combine historical data on province-level famine exposure with contemporary data on individual wealth. To better understand if the relationship is causal, we simultaneously account for the well-known historical evidence on the selection effect arising for those who survive the famine and those born during this period, as well as the issue of endogeneity on the exposure of a province to the famine. We find robust evidence showing that famine exposure has had a considerable negative effect on the contemporary wealth of individuals born during this period. Together, the evidence suggests that the famine had an adverse effect on wealth, and it is even present among the wealthiest cohort of individuals in present-day China.

The sharp devaluation of the ruble in 2014 increased the real returns to Russians from working in a global online labor marketplace, as con- tracts in this market are dollar-denominated. Russians clearly noticed the opportunity, with Russian hours-worked increasing substantially, primarily on the extensive margin -- incumbent Russians already active were fairly inelastic. Contrary to the predictions of bargaining models, there was little to no pass-through of the ruble price changes in to wages. There was also no evidence of a demand-side response, with buyers not posting more "Russian friendly" jobs, suggesting limited cross-side externalities. The key findings -- a high extensive margin elasticity but low intensive margin elasticity; little pass-through into wages; and little evidence of a cross-side externality -- have implications for market designers with respect to pricing and supply acquisition.

Despite the key role of multinational enterprises (MNEs) in both international markets and domestic economies, there is no consensus on whether or how they positively impact their host economy. In particular, do MNEs foster the creation of new domestic firms through knowledge spillovers? In this study, we look at the impact of the presence of related MNE industries on the entry and exit of domestic export industries in Irish regions before, during, and after the 2008 financial crisis. Specifically, we are interested in whether the presence of MNEs in a region results in knowledge spillovers and the creation of new domestic export activities in related sectors. To quantify how related an industry is to a region's current export basket we deploy an existing cohesion variable, closeness, that measures the relatedness of a new industry to existing industries within a region. We also introduce a new variable, strategic closeness, which captures not only the relatedness of industries within a region but their own connectivity or embeddedness. We use a dataset containing all government-supported export firms in Ireland between 2006-2018. We find that the presence of related MNE industries is associated with the entry of new domestic activity, suggesting that Irish regions benefited from domestic-MNE linkages. However this relationship was temporarily lost after the financial crisis and only recently re-established, with domestic entry dependent on the presence of highly embedded MNE sectors. Furthermore, we find that related MNEs help protect domestic industries against exit after the crisis and thereby play a role in enhancing regional resilience.

Executing even moderately large derivatives orders can be expensive and risky; it's hard to balance the uncertainty of working an order over time versus paying a liquidity premium for immediate execution. Here, we introduce the Time Is Money model, which calculates the Equilibrium Trading Horizon over which to execute an order within the adversarial forces of variance risk and liquidity premium. We construct a hypothetical at-the-money option within Arithmetic Brownian Motion and invert the Bachelier model to compute an inflection point between implied variance and liquidity cost as governed by a central limit order book, each in real time as they evolve. As a result, we demonstrate a novel, continuous-time Arrival Price framework. Further, we argue that traders should be indifferent to choosing between variance risk and liquidity cost, unless they have a predetermined bias or an exogenous position with a convex payoff. We, therefore, introduce half-life factor asymptotics to the model based on a convexity factor and compare results to existing models. We also describe a specialization of the model for trading a basket of correlated instruments, as exemplified by a futures calendar spread. Finally, we establish groundwork for microstructure optimizations as well as explore short term drift and conditional expected slippage within the Equilibrium Horizon framework.

In 1974, Robert Caro published The Power Broker, a critical biography of Robert Moses's dictatorial tenure as the "master builder" of mid-century New York. Moses profoundly transformed New York's urban fabric and transportation system, producing the Brooklyn Battery Tunnel, the Verrazano Narrows Bridge, the Westside Highway, the Cross-Bronx Expressway, the Lincoln Center, the UN headquarters, Shea Stadium, Jones Beach State Park and many other projects. However, The Power Broker did lasting damage to his public image and today he remains one of the most controversial figures in city planning history. On August 26, 1974, Moses issued a turgid 23-page statement denouncing Caro's work as "full of mistakes, unsupported charges, nasty baseless personalities, and random haymakers." Moses's original typewritten statement survives today as a grainy photocopy in the New York City Parks Department archive. To better preserve and disseminate it, I have extracted and transcribed its text using optical character recognition and edited the result to correct errors. Here I compile my transcription of Moses's statement, alongside Caro's reply to it.