We give an upper bound on the resources required for valuable quantum advantage in pricing derivatives. To do so, we give the first complete resource estimates for useful quantum derivative pricing, using autocallable and Target Accrual Redemption Forward (TARF) derivatives as benchmark use cases. We uncover blocking challenges in known approaches and introduce a new method for quantum derivative pricing - the \emph{re-parameterization method} - that avoids them. This method combines pre-trained variational circuits with fault-tolerant quantum computing to dramatically reduce resource requirements. We find that the benchmark use cases we examine require 7.5k logical qubits and a T-depth of 46 million and thus estimate that quantum advantage would require a logical clock speed of 10Mhz. While the resource requirements given here are out of reach of current systems, we hope they will provide a roadmap for further improvements in algorithms, implementations, and planned hardware architectures.

There has been limited success applying the Nash Bargaining Solution (NBS) in assigning intellectual property damages due to the difficulty of relating it to the specific facts of the case. Because of this, parties are not taking advantage of Georgia-Pacific factor fifteen. This paper intends to bring clarity to the NBS so it can be applied to the facts of a case. This paper normalizes the NBS and provides a methodology for determining the bargaining weight in Nash's solution. Several examples demonstrate this normalized form, and a nomograph is added for computational ease.

Causal estimation of the short-term effects of tariff-rate quotas (TRQs) on vegetable producer prices is hampered by the large variety and different growing seasons of vegetables and is therefore rarely performed. We quantify the effects of Swiss seasonal TRQs on domestic producer prices of a variety of vegetables based on a difference-in-differences estimation using a novel dataset of weekly producer prices for Switzerland and neighbouring countries. We find that TRQs increase prices of most vegetables by more than 20% above the prices in neighbouring countries during the main harvest time for most vegetables and even more than 50% for some vegetables. The effects are stronger for more perishable vegetables and for conventionally produced ones compared with organic vegetables. However, we do not find clear-cut effects of TRQs on the week-to-week price volatility of vegetables although the overall lower price volatility in Switzerland compared with neighbouring countries might be a result of the TRQ system in place.

We consider the classical problem of building an arbitrage-free implied volatility surface from bid-ask quotes. We design a fast numerical procedure, for which we prove the convergence, based on the Sinkhorn algorithm that has been recently used to solve efficiently (martingale) optimal transport problems.

It is generally understood that a given one-dimensional diffusion may be transformed by Cameron-Martin-Girsanov measure change into another one-dimensional diffusion with the same volatility but a different drift. But to achieve this we have to know that the change-of-measure local martingale that we write down is a true martingale; we provide a complete characterization of when this happens. This is then used to discuss absence of arbitrage in a generalized Heston model including the case where the Feller condition for the volatility process is violated.

An increasing number of politicians are relying on cheaper, easier to access technologies such as online social media platforms to communicate with their constituency. These platforms present a cheap and low-barrier channel of communication to politicians, potentially intensifying political competition by allowing many to enter political races. In this study, we demonstrate that lowering costs of communication, which allows many entrants to come into a competitive market, can strengthen an incumbent's position when the newcomers compete by providing more information to the voters. We show an asymmetric bad-news-good-news effect where early negative news hurts the challengers more than the positive news benefit them, such that in aggregate, an incumbent politician's chances of winning is higher with more entrants in the market. Our findings indicate that communication through social media and other platforms can intensify competition, how-ever incumbency advantage may be strengthened rather than weakened as an outcome of higher number of entrants into a political market.

Rather than directly predicting future prices or returns, we follow a more recent trend in asset management and classify the state of a market based on labels. We use numerous standard labels and even construct our own ones. The labels rely on future data to be calculated, and can be used a target for training a market state classifier using an appropriate set of market features, e.g. moving averages. The construction of those features relies on their label separation power. Only a set of reasonable distinct features can approximate the labels. For each label we use a specific neural network to classify the state using the market features from our feature space. Each classifier gives a probability to buy or to sell and combining all their recommendations (here only done in a linear way) results in what we call a trading strategy. There are many such strategies and some of them are somewhat dubious and misleading. We construct our own metric based on past returns but penalising for a low number of transactions or small capital involvement. Only top score-performance-wise trading strategies end up in final ensembles. Using the Bitcoin market we show that the strategy ensembles outperform both in returns and risk-adjusted returns in the out-of-sample period. Even more so we demonstrate that there is a clear correlation between the success achieved in the past (if measured in our custom metric) and the future.

We propose three different data-driven approaches for pricing European-style call options using supervised machine-learning algorithms. These approaches yield models that give a range of fair prices instead of a single price point. The performance of the models are tested on two stock market indices: NIFTY$50$ and BANKNIFTY from the Indian equity market. Although neither historical nor implied volatility is used as an input, the results show that the trained models have been able to capture the option pricing mechanism better than or similar to the Black-Scholes formula for all the experiments. Our choice of scale free I/O allows us to train models using combined data of multiple different assets from a financial market. This not only allows the models to achieve far better generalization and predictive capability, but also solves the problem of paucity of data, the primary limitation of using machine learning techniques. We also illustrate the performance of the trained models in the period leading up to the 2020 Stock Market Crash (Jan 2019 to April 2020).

We review economic research regarding the decision making processes of individuals in economics, with a particular focus on papers which tried analyzing factors that affect decision making with the evolution of the history of economic thought. The factors that are discussed here are psychological, emotional, cognitive systems, and social norms. Apart from analyzing these factors, it deals with the reasons behind the limitations of rational decision-making theory in individual decision making and the need for a behavioral theory of decision making. In this regard, it has also reviewed the role of situated learning in the decision-making process.

Credit rating is an analysis of the credit risks associated with a corporation, which reflect the level of the riskiness and reliability in investing. There have emerged many studies that implement machine learning techniques to deal with corporate credit rating. However, the ability of these models is limited by enormous amounts of data from financial statement reports. In this work, we analyze the performance of traditional machine learning models in predicting corporate credit rating. For utilizing the powerful convolutional neural networks and enormous financial data, we propose a novel end-to-end method, Corporate Credit Ratings via Convolutional Neural Networks, CCR-CNN for brevity. In the proposed model, each corporation is transformed into an image. Based on this image, CNN can capture complex feature interactions of data, which are difficult to be revealed by previous machine learning models. Extensive experiments conducted on the Chinese public-listed corporate rating dataset which we build, prove that CCR-CNN outperforms the state-of-the-art methods consistently.

With the ever-growing achievements in Artificial Intelligence (AI) and the recent boosted enthusiasm in Financial Technology (FinTech), applications such as credit scoring have gained substantial academic interest. Credit scoring helps financial experts make better decisions regarding whether or not to accept a loan application, such that loans with a high probability of default are not accepted. Apart from the noisy and highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the `right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic decisions are understandable and coherent. An interesting concept that has been recently introduced is eXplainable AI (XAI), which focuses on making black-box models more interpretable. In this work, we present a credit scoring model that is both accurate and interpretable. For classification, state-of-the-art performance on the Home Equity Line of Credit (HELOC) and Lending Club (LC) Datasets is achieved using the Extreme Gradient Boosting (XGBoost) model. The model is then further enhanced with a 360-degree explanation framework, which provides different explanations (i.e. global, local feature-based and local instance-based) that are required by different people in different situations. Evaluation through the use of functionallygrounded, application-grounded and human-grounded analysis show that the explanations provided are simple, consistent as well as satisfy the six predetermined hypotheses testing for correctness, effectiveness, easy understanding, detail sufficiency and trustworthiness.

In this paper, we consider a variety of multi-state Hidden Markov models for predicting and explaining the Bitcoin, Ether and Ripple returns in the presence of state (regime) dynamics. In addition, we examine the effects of several financial, economic and cryptocurrency specific predictors on the cryptocurrency return series. Our results indicate that the Non-Homogeneous Hidden Markov (NHHM) model with four states has the best one-step-ahead forecasting performance among all competing models for all three series. The dominance of the predictive densities over the single regime random walk model relies on the fact that the states capture alternating periods with distinct return characteristics. In particular, the four state NHHM model distinguishes bull, bear and calm regimes for the Bitcoin series, and periods with different profit and risk magnitudes for the Ether and Ripple series. Also, conditionally on the hidden states, it identifies predictors with different linear and non-linear effects on the cryptocurrency returns. These empirical findings provide important insight for portfolio management and policy implementation.

A growing attention in the empirical literature has been paid to the incidence of climate shocks and change in migration decisions. Previous literature leads to different results and uses a multitude of traditional empirical approaches.

This paper proposes a tree-based Machine Learning (ML) approach to analyze the role of the weather shocks towards an individual's intention to migrate in the six agriculture-dependent-economy countries such as Burkina Faso, Ivory Coast, Mali, Mauritania, Niger, and Senegal. We perform several tree-based algorithms (e.g., XGB, Random Forest) using the train-validation-test workflow to build robust and noise-resistant approaches. Then we determine the important features showing in which direction they are influencing the migration intention. This ML-based estimation accounts for features such as weather shocks captured by the Standardized Precipitation-Evapotranspiration Index (SPEI) for different timescales and various socioeconomic features/covariates.

We find that (i) weather features improve the prediction performance although socioeconomic characteristics have more influence on migration intentions, (ii) country-specific model is necessary, and (iii) international move is influenced more by the longer timescales of SPEIs while general move (which includes internal move) by that of shorter timescales.

We solve non-Markovian optimal switching problems in discrete time on an infinite horizon, when the decision maker is risk aware and the filtration is general, and establish existence and uniqueness of solutions for the associated reflected backward stochastic difference equations. An example illustrates the interaction between delayed or missing observations and risk sensitivity.

This study aimed to find new perspectives on the use of humor through digital media. A qualitative approach was used to conduct this study, where data were collected through a literature review. Stress is caused by the inability of a person to adapt between desires and reality. All forms of stress are basically caused by a lack of understanding of human's own limitations. Inability to fight limitations that will cause frustration, conflict, anxiety, and guilt. Too much stress can threaten a person's ability to deal with the environment. As a result, employees develop various kinds of stress symptoms that can interfere with their work performance. Thus, the management of work stress is important to do, one of which uses humor. However, in the digital age, the spread of humor can be easily facilitated. The results of this review article find new perspectives to reduce stress through digital humor, namely interactive humor, funny photos, manipulations, phanimation, celebrity soundboards, and PowerPoint humor. The research shows that the use of humor as a coping strategy is able to predict positive affect and well-being work-related. Moreover, digital humor which has various forms as well as easy, fast, and wide spread, then the effect is felt increasingly significant

We find the optimal indemnity to minimize the probability of ruin when premium is calculated according to the distortion premium principle with a proportional risk load, and admissible indemnities are such that both the indemnity and retention are non-decreasing functions of the underlying loss. We reformulate the problem with the inverse survival function as the control variable and show that deductible insurance with maximum limit is optimal. Our main contribution is in solving this problem via the inverse survival function.

We consider the non-adapted version of a simple problem of portfolio optimization in a financial market that results from the presence of insider information. We analyze it via anticipating stochastic calculus and compare the results obtained by means of the Russo-Vallois forward, the Ayed-Kuo, and the Hitsuda-Skorokhod integrals. We compute the optimal portfolio for each of these cases. Our results give a partial indication that, while the forward integral yields a portfolio that is financially meaningful, the Ayed-Kuo and the Hitsuda-Skorokhod integrals do not provide an appropriate investment strategy for this problem.

Repeated history of pandemics, such as SARS, H1N1, Ebola, Zika, and COVID-19, has shown that pandemic risk is inevitable. Extraordinary shortages of medical resources have been observed in many parts of the world. Some attributing factors include the lack of sufficient stockpiles and the lack of coordinated efforts to deploy existing resources to the location of greatest needs. The paper investigates contingency planning and resources allocation from a risk management perspective, as opposed to the prevailing supply chain perspective. The key idea is that the competition of limited critical resources is not only present in different geographical locations but also at different stages of a pandemic. This paper draws on an analogy between risk aggregation and capital allocation in finance and pandemic resources planning and allocation for healthcare systems. The main contribution is to introduce new strategies for optimal stockpiling and allocation balancing spatio-temporal competitions of medical supply and demand.

Economists have mainly focused on human capital accumulation and considerably less on the causes and consequences of human capital depreciation in late adulthood. Studying human capital depreciation over the life cycle has powerful economic consequences for decision-making in old age. Using data from China, we examine how a new retirement program affects cognitive performance. We find large negative effects of pension benefits on cognitive functioning among the elderly. We detect the most substantial impact of the program on delayed recall, a significant predictor of the onset of dementia. We show suggestive evidence that the program leads to larger negative impacts among women. We demonstrate that retirement and access to a retirement pension plan plays a significant role in explaining cognitive decline at older ages.

This paper extends the sequential search model of Wolinsky (1986) by allowing firms to choose how much match value information to disclose to visiting consumers. This restores the Diamond paradox (Diamond 1971): there exist no symmetric equilibria in which consumers engage in active search, so consumers obtain zero surplus and firms obtain monopoly profits. Modifying the scenario to one in which prices are advertised, we discover that the no-active-search result persists, although the resulting symmetric equilibria are ones in which firms price at marginal cost.

The aim of the present paper is to provide criteria for a central bank of how to choose among different monetary-policy rules when caring about a number of policy targets such as the output gap and expected inflation. Special attention is given to the question if policy instruments are predetermined or only forward looking. Using the new-Keynesian Phillips curve with a cost-push-shock policy-transmission mechanism, the forward-looking case implies an extreme lack of robustness and of credibility of stabilization policy. The backward-looking case is such that the simple-rule parameters can be the solution of Ramsey optimal policy under limited commitment. As a consequence, we suggest to model explicitly the rational behavior of the policy maker with Ramsey optimal policy, rather than to use simple rules with an ambiguous assumption leading to policy advice that is neither robust nor credible.

In this paper, we adopted a net liability model which assesses both market risk on the liability side and revenue risk on the asset side for a Guaranteed Minimum Maturity Benefit (GMMB) embedded in variable annuity (VA) contracts. Numeric solutions for net liabilities, fair rate of fees and Greeks of GMMB are obtained by a more accurate and fast Fourier Space time-stepping (FST) algorithm. Monte Carlo results are provided for comparative purpose. The unhedged and three statically hedged portfolios are introduced, and their performances are assessed by comparing the short term and long term portfolio's volatility. Recently, we noticed FST algorithm can only be used to hedge the gross liability of GMMB and it leads to an incorrect result when applying FST algorithm to the net liability model. We have modified the method we used in the hedging part and obtained the reliable result now. They will be reported in near future.

Social capital creates a synergy that benefits all members of a community. This review examines how social capital contributes to the food security of communities. A systematic literature review, based on Prisma, is designed to provide a state-of-the-art review on capacity social capital in this realm. The output of this method led to finding 39 related articles. Studying these articles illustrates that social capital improves food security through two mechanisms of knowledge sharing and product sharing (i.e., sharing food products). It reveals that social capital through improving the food security pillars (i.e., food availability, food accessibility, food utilization, and food system stability) affects food security. In other words, the interaction among the community members results in sharing food products and information among community members, which facilitates food availability and access to food. There are many shreds of evidence in the literature that sharing food and food products among the community member decreases household food security and provides healthy nutrition to vulnerable families and improves the food utilization pillar of food security. It is also disclosed that belonging to the social networks increases the community members' resilience and decreases the community's vulnerability that subsequently strengthens the stability of a food system. This study contributes to the common literature on food security and social capital by providing a conceptual model based on the literature. In addition to researchers, policymakers can use this study's findings to provide solutions to address food insecurity problems.

Every unique crisis, a new and novel risk factor, leads to a rapid, synchronous and panic sell-off by the investors that lead to a massive stock market crash, termed as mainshock, which usually continues for more than one day. Though most of the stocks start recovering from the crash within a short period, the effect of the crash remains throughout the recovery phase. During the recovery, as the market remains in stress, any small perturbation leads to a relatively smaller aftershock, which may also occur for a few days. Statistical analysis of the mainshock and the aftershocks for the crash of 1987, the financial crisis of 2008 and the COVID-19 pandemic shows that they follow the Gutenberg-Richter (G-R) power law. The duration of the influence of the mainshock, within which aftershocks are considered, has been calculated using structural break analysis. The analysis shows that high magnitude aftershocks comparable to the mainshock are rare but low magnitude aftershocks can be seen frequently till the full recovery of the market. It is also consistent with the psychology of the investors that when the unique crisis becomes known, the market does not react too irrationally as it did initially, and hence subsequent crashes become relatively smaller. The results indicate that there is a possibility of the occurrence of future low magnitude aftershocks due to the ongoing COVID-19 pandemic. The analysis may help investors make rational investment decisions during the stressed period after a major market crash.

I study the economic effects of testing during the outbreak of a novel disease. I propose a model where testing permits isolation of the infected and provides agents with information about the prevalence and lethality of the disease. Additional testing reduces the perceived lethality of the disease, but might increase the perceived risk of infection. As a result, more testing could increase the perceived risk of dying from the disease - i.e. "stoke fear" - and cause a fall in economic activity, despite improving health outcomes. Two main insights emerge. First, increased testing is beneficial to the economy and pays for itself if performed at a sufficiently large scale, but not necessarily otherwise. Second, heterogeneous risk perceptions across age-groups can have important aggregate consequences. For a SARS-CoV-2 calibration of the model, heterogeneous risk perceptions across young and old individuals mitigate GDP losses by 50% and reduce the death toll by 30% relative to a scenario in which all individuals have the same perceptions of risk.

Aiming to describe the wealth distribution evolution, several models consider an ensemble of interacting economic agents that exchange wealth in binary fashion. Intriguingly, models that consider an unbiased market, that gives to each agent the same chances to win in the game, are always out of equilibrium until the perfect inequality of the final state is attained. Here we present a rigorous analytical demonstration that any system driven by unbiased binary exchanges are doomed to drive the system to perfect inequality and zero mobility.