As the Indian economy grows digitally and becomes more financially inclusive, more and more investors have started to invest in the Indian capital markets. The number of retail and institutional folios with Indian mutual fund schemes have continued to rise for the 74th consecutive month. This study considers 139 mutual fund schemes (98 equity schemes) and aims to ascertain the various metrics and parameters, retail and institutional investors continue to rely on to make investment recommendations. We compare these with the results from a data envelopment analysis model that generates an efficiency frontier based on an optimal risk, cost, and return trade-off. We further put forth an iteration of the DEA model, not only considering risk, cost, and return characteristics but also incorporating metrics such as the information ratio which hold significance for retail and institutional investors. We compare these results with traditional metrics and fund rankings published by established industry rating agencies.

In this paper an algorithm designed for large databases is introduced for the enhancement of pass rates in mathematical university lower division courses with several sections. Using integer programming techniques, the algorithm finds the optimal pairing of students and lecturers in order to maximize the success chances of the students' body. The students-lecturer success probability is computed according to their corresponding profiles stored in the data bases.

Global historical series spanning the last two centuries became recently available for primary energy consumption (PEC) and Gross Domestic Product (GDP). Here through a thorough analysis of the data, we propose a new, simple macroeconomic model whereby physical power fuels economic power. From 1820 to 1920, the linearity between global PEC and world GDP justifies basic equations where, originally, PEC incorporates unskilled human labor that consumes and converts food energy. In a consistent model, both physical capital and human capital are fed by PEC and store energy. In the following century, 1920-2016, GDP grows quicker than PEC, displaying periods of linearity of the two variables, separated by distinct jumps interpreted as radical technology shifts. The GDP to PEC ratio accumulates game-changing innovation, at an average growth rate proportional to PEC. These results seed alternative strategies for modelling and political management of the climate crisis and energy transition.

I study the relationship between the likelihood of a violent domestic conflict and the risk that such a conflict "externalizes" (i.e. spreads to another country by creating an international dispute). I consider a situation in which a domestic conflict between a government and a rebel group has the potential to externalize. I show that the risk of externalization increases the likelihood of a peaceful outcome, but only if the government is sufficiently powerful relative to the rebels, the risk of externalization is sufficiently high, and the foreign actor who can intervene in the domestic conflict is sufficiently uninterested in material costs and benefits. I show how this model helps to understand the recent and successful peace process between the Colombian government and the country's most powerful rebel group, the Revolutionary Armed Forces of Colombia (FARC).

Miles et al. [1] recently produced an analysis of the costs and benefits of lockdown policies in the face of COVID-19, focussing on the case of the U.K. They argue that the March-June UK lockdown was more costly than the benefit of lives saved, if the latter are evaluated using the NICE threshold of {\pounds}30000 for a quality-adjusted life year (QALY). Looking forwards, they argue that the costs of a lockdown for 13 weeks from mid-June would be vastly greater than the benefits under any plausible QALY costing, even in a scenario in which easing lockdown led to a second infection wave that caused more than 7000 deaths a week by mid-September. I note here two key problems that certainly significantly affect their estimates and cast doubt on their conclusions. Firstly, they cut off their calculations arbitrarily after 13 weeks, without costing the epidemic state at the end of the period. That is, they assume that we should be indifferent between mid-September states of 13 deaths a week and 7500 deaths a week, and corresponding infection rates. This seems indefensible unless one assumes that (a) there will be no future vaccine and no future improvements in treatment or in non-medical interventions, (b) that COVID-19 will inevitably continue to propagate until herd immunity is reached. Even under these assumptions it is very questionable. Secondly, they ignore the costs of serious illness, possible long-term lowering of life quality, and possible lowering of life expectancy for COVID-19 survivors. These are uncertain, but clearly not negligible, and plausibly comparable to or larger than the costs in lives lost.

In this paper, we present a novel computational framework for portfolio-wide risk management problems, where the presence of a potentially large number of risk factors makes traditional numerical techniques ineffective. The new method utilises a coupled system of BSDEs for the valuation adjustments (xVA) and solves these by a recursive application of a neural network based BSDE solver. This not only makes the computation of xVA for high-dimensional problems feasible, but also produces hedge ratios and dynamic risk measures for xVA, and allows simulations of the collateral account.

Understanding collective human behavior and dynamics at urban-scale has drawn broad interest in physics, engineering, and social sciences. Social physics often adopts a statistical perspective and treats individuals as interactive elementary units,while the economics perspective sees individuals as strategic decision makers. Here we provide a microscopic mechanism of city-scale dynamics,interpret the collective outcome in a thermodynamic framework,and verify its various implications empirically. We capture the decisions of taxi drivers in a game-theoretic model,prove the existence, uniqueness, and global asymptotic stability of Nash equilibrium. We offer a macroscopic view of this equilibrium with laws of thermodynamics. With 870 million trips of over 50k drivers in New York City,we verify this equilibrium in space and time,estimate an empirical constitutive relation,and examine the learning process at individual and collective levels. Connecting two perspectives,our work shows a promising approach to understand collective behavior of subpopulations.

The bicycle is a low-cost means of transport linked to low risk of COVID-19 transmission. Governments have incentivized cycling by redistributing street space as part of their post-lockdown strategies. Here, we evaluate the impact of provisional bicycle infrastructure on cycling traffic in European cities. We scrape daily bicycle counts spanning over a decade from 736 bicycle counters in 106 European cities. We combine this with data on announced and completed pop-up bike lane road work projects. On average 11.5 kilometers of provisional pop-up bike lanes have been built per city. Each kilometer has increased cycling in a city by 0.6\%. We calculate that the new infrastructure will generate \$3 billion in health benefits per year, if cycling habits are sticky.

This paper examines effects of MiFID II on European stock markets. We study the effects of the new tick size regime, both intraday and in the closing auction. An increase (decrease) in tick size is associated with a decrease (increase) in intraday liquidity, but a more (less) stable market. In the closing auction an increase in tick size has a positive effect on liquidity. Moreover, we report a positive relationship between tick size and transacted volume, in particular in the closing auction. Finally, closing auction volumes increased heavily since MiFID II and price formation in closing auctions became more efficient.

This paper considers an initial market model, specified by its underlying assets $S$ and its flow of information $\mathbb F$, and an arbitrary random time $\tau$ which might not be an $\mathbb F$-stopping time. As the death time and the default time (that $\tau$ might represent) can be seen when they occur only, the progressive enlargement of $\mathbb F$ with $\tau$ sounds tailor-fit for modelling the new flow of information $\mathbb G$ that incorporates both $\mathbb F$ and $\tau$. In this setting of informational market, the first principal goal resides in describing as explicit as possible the set of all deflators for $(S^{\tau}, \mathbb G)$, while the second principal goal lies in addressing the No-Free-Lunch-with-Vanishing-Risk concept (NFLVR hereafter) for $(S^{\tau}, \mathbb G)$. Besides this direct application to NFLVR, the set of all deflators constitutes the dual set of all "admissible" wealth processes for the stopped model $(S^{\tau},\mathbb G)$, and hence it is vital in many hedging and pricing related optimization problems. Thanks to the deep results of Choulli et al. [7], on martingales classification and representation for progressive enlarged filtration, our two main goals are fully achieved in different versions, when the survival probability never vanishes. The results are illustrated on the two particular cases when $(S,\mathbb F)$ follows the jump-diffusion model and the discrete-time model.

We introduce a new software toolbox, called Multi-Agent eXchange Environment (MAXE), for agent-based simulation of limit order books. Offering both efficient C++ implementations and Python APIs, it allows the user to simulate large-scale agent-based market models while providing user-friendliness for rapid prototyping. Furthermore, it benefits from a versatile message-driven architecture that offers the flexibility to simulate a range of different (easily customisable) market rules and to study the effect of auxiliary factors, such as delays, on the market dynamics.

Showcasing its utility for research, we employ our simulator to investigate the influence the choice of the matching algorithm has on the behaviour of artificial trader agents in a zero-intelligence model. In addition, we investigate the role of the order processing delay in normal trading on an exchange and in the scenario of a significant price change. Our results include the findings that (i) the variance of the bid-ask spread exhibits a behavior similar to resonance of a damped harmonic oscillator with respect to the processing delay and that (ii) the delay markedly affects the impact a large trade has on the limit order book.

We consider the Graph Ornstein-Uhlenbeck (GrOU) process observed on a non-uniform discrete time grid and introduce discretised maximum likelihood estimators with parameters specific to the whole graph or specific to each component, or node. Under a high-frequency sampling scheme, we study the asymptotic behaviour of those estimators as the mesh size of the observation grid goes to zero. We prove two stable central limit theorems to the same distribution as in the continuously-observed case under both finite and infinite jump activity for the L\'evy driving noise. When a graph structure is not explicitly available, the stable convergence allows to consider purpose-specific sparse inference procedures, i.e. pruning, on the edges themselves in parallel to the GrOU inference and preserve its asymptotic properties. We apply the new estimators to wind capacity factor measurements, i.e. the ratio between the wind power produced locally compared to its rated peak power, across fifty locations in Northern Spain and Portugal. We show the superiority of those estimators compared to the standard least squares estimator through a simulation study extending known univariate results across graph configurations, noise types and amplitudes.

Regional quarantine policies, in which a portion of a population surrounding infections are locked down, are an important tool to contain disease. However, jurisdictional governments - such as cities, counties, states, and countries - act with minimal coordination across borders. We show that a regional quarantine policy's effectiveness depends upon whether (i) the network of interactions satisfies a balanced-growth condition, (ii) infections have a short delay in detection, and (iii) the government has control over and knowledge of the necessary parts of the network (no leakage of behaviors). As these conditions generally fail to be satisfied, especially when interactions cross borders, we show that substantial improvements are possible if governments are proactive: triggering quarantines in reaction to neighbors' infection rates, in some cases even before infections are detected internally. We also show that even a few lax governments - those that wait for nontrivial internal infection rates before quarantining - impose substantial costs on the whole system. Our results illustrate the importance of understanding contagion across policy borders and offer a starting point in designing proactive policies for decentralized jurisdictions.

In this work, we adapt a Monte Carlo algorithm introduced by Broadie and Glasserman (1997) to price a $\pi$-option. This method is based on the simulated price tree that comes from discretization and replication of possible trajectories of the underlying asset's price. As a result this algorithm produces the lower and the upper bounds that converge to the true price with the increasing depth of the tree. Under specific parametrization, this $\pi$-option is related to relative maximum drawdown and can be used in the real-market environment to protect a portfolio against volatile and unexpected price drops. We also provide some numerical analysis.

We investigate the impact of Covid-19 cases and deaths, local spread spreads of Covid-19, and Google search activities on the US stock market. We develop a temporal complex network to quantify US county level spread dynamics of Covid-19. We conduct the analysis by using the following sequence of methods: Spearman's rank correlation, Granger causality, Random Forest (RF) model, and EGARCH (1,1) model. The results suggest that Covid-19 cases and deaths, its local spread spreads, and Google searches have impacts on the abnormal stock price between January 2020 to May 2020. However, although a few of Covid-19 variables, e.g., US total deaths and US new cases exhibit causal relationship on price volatility, EGARCH model suggests that Covid-19 cases and deaths, local spread spreads of Covid-19, and Google search activities do not have impacts on price volatility.

Direct elicitation, guided by theory, is the standard method for eliciting latent preferences. The canonical direct-elicitation approach for measuring individuals' valuations for goods is the Becker-DeGroot-Marschak procedure, which generates willingness-to-pay (WTP) values that are imprecise and systematically biased by understating valuations. We show that enhancing elicited WTP values with supervised machine learning (SML) can substantially improve estimates of peoples' out-of-sample purchase behavior. Furthermore, swapping WTP data with choice data generated from a simple task, two-alternative forced choice, leads to comparable performance. Combining all the data with the best-performing SML methods yields large improvements in predicting out-of-sample purchases. We quantify the benefit of using various SML methods in conjunction with using different types of data. Our results suggest that prices set by SML would increase revenue by 28% over using the stated WTP, with the same data.

In this paper, we introduce a new time series model having a stochastic exponential tail. This model is constructed based on the Normal Tempered Stable distribution with a time-varying parameter. The model captures the stochastic exponential tail, which generates the volatility smile effect and volatility term structure in option pricing. Moreover, the model describes the time-varying volatility of volatility. We empirically show the stochastic skewness and stochastic kurtosis by applying the model to analyze S&P 500 index return data. We present the Monte-Carlo simulation technique for the parameter calibration of the model for the S&P 500 option prices. We can see that the stochastic exponential tail makes the model better to analyze the market option prices by the calibration.

We exploit variation in the timing of decriminalization of same-sex sexual intercourse across U.S. states to estimate the impact of these law changes on crime through difference-in-difference and event-study models. We provide the first evidence that sodomy law repeals led to a decline in the number of arrests for disorderly conduct, prostitution, and other sex offenses. Furthermore, we show that these repeals led to a reduction in arrests for drug and alcohol consumption.

Financial markets across all asset classes are known to exhibit trends. These trends have been exploited by traders for decades. Here, we empirically measure when trends revert, based on 30 years of daily futures prices for equity indices, interest rates, currencies and commodities. We find that trends tend to revert once they reach a critical level of statistical significance. Based on polynomial regression, we carefully measure this critical level. We find that it is universal across asset classes and has a universal scaling behavior, as the trend's time horizon runs from a few days to several years. The corresponding regression coefficients are small, but statistically highly significant, as confirmed by bootstrapping and out-of-sample testing. Our results signal to investors when to exit a trend. They also reveal how markets have become more efficient over the decades. Moreover, they point towards a potential deep analogy between financial markets and critical phenomena: our analysis supports the conjecture that financial markets can be modeled as statistical mechanical ensembles of Buy/Sell orders near critical points. In this analogy, the trend strength plays the role of an order parameter, whose dynamcis is described by a Langevin equation.