The sequence of moves in a dynamic team tournament may distort the ex-ante winning probabilities and harm efficiency. This paper compares eight different rules, suggested for soccer penalty shootouts, to determine the order of actions. Their fairness is analysed under three possible mathematical models of psychological pressure. We also discuss the probability of reaching the sudden death stage, as well as the complexity and strategy-proofness of the rules. In the case of stationary scoring probabilities, it remains sufficient to use static rules in order to improve fairness. However, it is worth compensating the second-mover by making it the first-mover in the sudden death stage. Our work has the potential to impact decision-makers who can achieve better outcomes in dynamic tournaments by a carefully chosen sequence of actions.

We price European-style options written on forward contracts in a commodity market, which we model with an infinite-dimensional Heath-Jarrow-Morton (HJM) approach. For this purpose we introduce a new class of state-dependent volatility operators that map the square integrable noise into the Filipovi\'{c} space of forward curves. For calibration, we specify a fully parametrized version of our model and train a neural network to approximate the true option price as a function of the model parameters. This neural network can then be used to calibrate the HJM parameters based on observed option prices. We conduct a numerical case study based on artificially generated option prices in a deterministic volatility setting. In this setting we derive closed pricing formulas, allowing us to benchmark the neural network based calibration approach. We also study calibration in illiquid markets with a large bid-ask spread. The experiments reveal a high degree of accuracy in recovering the prices after calibration, even if the original meaning of the model parameters is partly lost in the approximation step.

In May 2020, Uniswap V2 was officially launched on Ethereum. Uniswap V2 allows users to create trading pools between any pair of cryptocurrencies, without the need for ETH as an intermediary currency. Uniswap V2 introduces new arbitrage opportunities: Traders are now able to trade cryptocurrencies cyclically: A trader can exchange currency A for B, then B for C, and finally C for A again through different trading pools. Almost surely, the three floating exchange rates are not perfectly in sync, which opens up arbitrage possibilities for cyclic trading.

In this paper, we study cyclic arbitrages in Decentralized Exchanges (DEXes) of cryptocurrencies with transaction-level data on Uniswap V2, observing 285,127 cyclic arbitrages over eight months. We conduct a theoretical analysis and an empirical evaluation to understand cyclic arbitrages systematically. We study the market size (the revenue and the cost) of cyclic arbitrages, how cyclic arbitrages change market prices, how cyclic arbitrageurs influence other participants, and the implementations of cyclic arbitrages.

Beyond the understanding of cyclic arbitrages, this paper suggests that with the smart contract technology and the replicated state machine setting of Ethereum, arbitrage strategies are easier implemented in DEXes than in Centralized Exchanges (CEXes). We find that deploying private smart contracts to implement cyclic arbitrages is more resilient to front-running attacks than applying cyclic arbitrages through public (open-source) smart contracts.

How are economies in a modern age impacted by epidemics? In what ways is economic life disrupted? How can pandemics be modeled? What can be done to mitigate and manage the danger? Does the threat of pandemics increase or decrease in the modern world? The Covid-19 pandemic has demonstrated the importance of these questions and the potential of complex systems science to provide answers. This article offers a broad overview of the history of pandemics, of established facts, and of models of infection diffusion, mitigation strategies, and economic impact. The example of the Covid-19 pandemic is used to illustrate the theoretical aspects, but the article also includes considerations concerning other historic epidemics and the danger of more infectious and less controllable outbreaks in the future.

In this paper, we introduce and analyze the fractional Barndorff-Nielsen and Shephard (BN-S) stochastic volatility model. The proposed model is based upon two desirable properties of the long-term variance process suggested by the empirical data: long-term memory and jumps. The proposed model incorporates the long-term memory and positive autocorrelation properties of fractional Brownian motion with $H>1/2$, and the jump properties of the BN-S model. We find arbitrage-free prices for variance and volatility swaps for this new model. Because fractional Brownian motion is still a Gaussian process, we derive some new expressions for the distributions of integrals of continuous Gaussian processes as we work towards an analytic expression for the prices of these swaps. The model is analyzed in connection to the quadratic hedging problem and some related analytical results are developed. The amount of derivatives required to minimize a quadratic hedging error is obtained. Finally, we provide some numerical analysis based on the VIX data. Numerical results show the efficiency of the proposed model compared to the Heston model and the classical BN-S model.

The central idea of the paper is to present a general simple patchwork construction principle for multivariate copulas that create unfavourable VaR (i.e. Value at Risk) scenarios while maintaining given marginal distributions. This is of particular interest for the construction of Internal Models in the insurance industry under Solvency II in the European Union. The method is exemplified with a 19-dimensional real-life data set of insurance losses.

At present, the economic measurement of the national statistical offices has not defined or captured the benefits of the digital economy activity due to the low quality or inexistence of methodologies. Currently, there is a relevant debate on the capacity of the digital economy activity to generate productivity, economic growth, and well-being through innovation and knowledge. For this reason, this research identified and studied specialized knowledge, human settlement, and digital economic activity as the factors that influence regional economic growth. As a result, the impact generated by a new business operating models based on information technology was measured. Furthermore, this research used an empirical measurement model that made it possible to identify certain phenomena such as regional poles of regional economic development (PRDE) that surround economically flourishing regions. In addition, it showed that municipalities with high degrees of economic growth were impacted by digital economic activity and specialized knowledge. This finding is consistent with economic growth theories that point to technological evolution as the main factor of modern economic growth. Consequently, this study contributed beneficial results to the local government to develop strategies framed in solving industrial cooperation of economically flourishing regions with their neighbors, facing the problem of agglomeration of resources and capital reflected in human settlement promote an imbalance in economic growth and social development.

We study a variant of the martingale optimal transport problem in a multi-period setting to derive robust price bounds of a financial derivative. On top of marginal and martingale constraints, we introduce a time-homogeneity assumption, which restricts the variability of the forward-looking transitions of the martingale across time. We provide a dual formulation in terms of superhedging and discuss relaxations of the time-homogeneity assumption by adding market frictions. In financial terms, the introduced time-homogeneity corresponds to a time-consistency condition for call prices, given the state of the stock. The time homogeneity assumption leads to improved price bounds as market data from many time points can be incorporated effectively. The approach is illustrated with two numerical examples.

Motivated by an equilibrium problem, we establish the existence of a solution for a family of Markovian backward stochastic differential equations with quadratic nonlinearity and discontinuity in $Z$. Using unique continuation and backward uniqueness, we show that the set of discontinuity has measure zero. In a continuous-time stochastic model of an endowment economy, we prove the existence of an incomplete Radner equilibrium with nondegenerate endogenous volatility.

Entering and exiting the Pandemic Recession, I study the high-frequency real-activity signals provided by a leading nowcast, the ADS Index of Business Conditions produced and released in real time by the Federal Reserve Bank of Philadelphia. I track the evolution of real-time vintage beliefs and compare them to a later-vintage chronology. Real-time ADS plunges and then swings as its underlying economic indicators swing, but the ADS paths quickly converge to indicate a return to brisk positive growth by mid-May. We show, moreover, that daily real activity path was highly correlated with the daily COVID-19 cases. Finally, I provide a comparative assessment of the real-time ADS signals provided when exiting the Great Recession.

This paper develops an empirical and theoretical case for how 'hype' among retail investors can drive large asset fluctuations. We use the dataset of discussions on WallStreetBets (WSB), an online investor forum with over nine million followers as of April 2021, to show how excitement about trading opportunities can ripple through an investor community with large market impacts. This paper finds empirical evidence of psychological contagion among retail investors by exploiting differences in stock price fluctuations and discussion intensity. We show that asset discussions on WSB are self-perpetuating: an initial set of investors attracts a larger and larger group of excited followers. Sentiments about future stock performance also spread from one individual to the next, net of any fundamental price movements. Leveraging these findings, we develop a model for how social contagion impacts prices. The proposed model and simulations show that social contagion has a destabilizing effect on markets. Finally, we establish a causal relationship between WSB activity and financial markets using an instrumental variable approach.

Reddit's WallStreetBets (WSB) community has come to prominence in light of its notable role in affecting the stock prices of what are now referred to as meme stocks. Yet very little is known about the reliability of the highly speculative investment advice disseminated on WSB. This paper analyses WSB data spanning from January 2019 to April 2021 in order to assess how successful an investment strategy relying on the community's recommendations could have been. We detect buy and sell advice and identify the community's most popular stocks, based on which we define a WSB portfolio. Our evaluation shows that this portfolio has grown approx. 200% over the last three years and approx. 480% over the last year, significantly outperforming the S&P500. The average short-term accuracy of buy and sell signals, in contrast, is not found to be significantly better than randomly or equally distributed buy decisions within the same time frame. However, we present a technique for estimating whether posts are proactive as opposed to reactive and show that by focusing on a subset of more promising buy signals, a trader could have made investments yielding higher returns than the broader market or the strategy of trusting all posted buy signals. Lastly, the analysis is also conducted specifically for the period before 2021 in order to factor out the effects of the GameStop hype of January 2021 - the results confirm the conclusions and suggest that the 2021 hype merely amplified pre-existing characteristics.

With the heightened volatility in stock prices during the Covid-19 pandemic, the need for price forecasting has become more critical. We investigated the forecast performance of four models including Long-Short Term Memory, XGBoost, Autoregression, and Last Value on stock prices of Facebook, Amazon, Tesla, Google, and Apple in COVID-19 pandemic time to understand the accuracy and predictability of the models in this highly volatile time region. To train the models, the data of all stocks are split into train and test datasets. The test dataset starts from January 2020 to April 2021 which covers the COVID-19 pandemic period. The results show that the Autoregression and Last value models have higher accuracy in predicting the stock prices because of the strong correlation between the previous day and the next day's price value. Additionally, the results suggest that the machine learning models (Long-Short Term Memory and XGBoost) are not performing as well as Autoregression models when the market experiences high volatility.

Identifying risk spillovers in financial markets is of great importance for assessing systemic risk and portfolio management. Granger causality in tail (or in risk) tests whether past extreme events of a time series help predicting future extreme events of another time series. The topology and connectedness of networks built with Granger causality in tail can be used to measure systemic risk and to identify risk transmitters. Here we introduce a novel test of Granger causality in tail which adopts the likelihood ratio statistic and is based on the multivariate generalization of a discrete autoregressive process for binary time series describing the sequence of extreme events of the underlying price dynamics. The proposed test has very good size and power in finite samples, especially for large sample size, allows inferring the correct time scale at which the causal interaction takes place, and it is flexible enough for multivariate extension when more than two time series are considered in order to decrease false detections as spurious effect of neglected variables. An extensive simulation study shows the performances of the proposed method with a large variety of data generating processes and it introduces also the comparison with the test of Granger causality in tail by [Hong et al., 2009]. We report both advantages and drawbacks of the different approaches, pointing out some crucial aspects related to the false detections of Granger causality for tail events. An empirical application to high frequency data of a portfolio of US stocks highlights the merits of our novel approach.

Automated market makers (AMM) have grown to obtain significant market share within the cryptocurrency ecosystem, resulting in a proliferation of new products pursuing exotic strategies for horizontal differentiation. Yet, their theoretical properties are curiously homogeneous when a set of basic assumptions are met. In this paper, we start by presenting a universal approach to deriving a formula for liquidity provisioning for AMMs. Next, we show that the constant function market maker and token swap market maker models are theoretically equivalent when liquidity reserves are uniform. Proceeding with an examination of AMM market microstructure, we show how non-linear price effect translates into slippage for traders and impermanent losses for liquidity providers. We proceed by showing how impermanent losses are a function of both volatility and market depth and discuss the implications of these findings within the context of the literature.

I propose a new conceptual framework to disentangle the impacts of weather and climate on economic activity and growth: A stochastic frontier model with climate in the production frontier and weather shocks as a source of inefficiency. I test it on a sample of 160 countries over the period 1950-2014. Temperature and rainfall determine production possibilities in both rich and poor countries; positively in cold countries and negatively in hot ones. Weather anomalies reduce inefficiency in rich countries but increase inefficiency in poor and hot countries; and more so in countries with low weather variability. The climate effect is larger that the weather effect.

The professional services industry (legal, accounting, consulting, architectural services{\ldots}) employs an important share of the active population in mature countries. However, after decades of undisputed growth, the sector appears to be at a turning point in certain geographies. This article therefore proposes a simple framework to help diagnose where growth opportunities (if any) may lie.When applied to the US economic context, the model indicates that at a macro-economic national level the sector should stall, which concurs with the trend observed over the past decade. However, it also highlights that a few industrial sectors (e.g. the US beverage industry) still offer pockets of growth for a variety of professional expertises. Replicating and fine-tuning those findings could be interesting for practitioners to steer their marketing and business development efforts. On the other hand, the quantitative framework presented in this study could pave the way for future research in the academic community.