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 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 8k logical qubits and a T-depth of 54 million. We estimate that quantum advantage would require executing this program at the order of a second. 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.

Agriculture is arguably the most climate-sensitive sector of the economy. Growing concerns about anthropogenic climate change have increased research interest in assessing its potential impact on the sector and in identifying policies and adaptation strategies to help the sector cope with a changing climate. This chapter provides an overview of recent advancements in the analysis of climate change impacts and adaptation in agriculture with an emphasis on methods. The chapter provides an overview of recent research efforts addressing key conceptual and empirical challenges. The chapter also discusses practical matters about conducting research in this area and provides reproducible R code to perform common tasks of data preparation and model estimation in this literature. The chapter provides a hands-on introduction to new researchers in this area.

Closely motivated by financial considerations, we develop an integration theory which is not classical i.e. it is not necessarily associated to a measure. The base space, denoted by $\mathcal{S}$ and called a trajectory space, substitutes the set $\Omega$ in probability theory and provides a fundamental structure via conditional subsets $\mathcal{S}_{(S,j)}$ that allows the definition of conditional integrals. The setting is a natural by-product of no arbitrage assumptions that are used to model financial markets and games of chance (in a discrete infinite time framework). The constructed conditional integrals can be interpreted as required investments, at the conditioning node, for hedging an integrable function, the latter characterized a.e. and in the limit as we increase the number of portfolios used. The integral is not classical due to the fact that the original vector space of portfolio payoffs is not a vector lattice. In contrast to a classical stochastic setting, where price processes are associated to conditional expectations (with respect to risk neutral measures), we uncover a theory where prices are naturally given by conditional non-lattice integrals. One could then study analogues of classical probabilistic notions in such non-classical setting, the paper stops after defining trajectorial martingales the study of which is deferred to future work.

We theoretically analyze the problem of testing for $p$-hacking based on distributions of $p$-values across multiple studies. We provide general results for when such distributions have testable restrictions (are non-increasing) under the null of no $p$-hacking. We find novel additional testable restrictions for $p$-values based on $t$-tests. Specifically, the shape of the power functions results in both complete monotonicity as well as bounds on the distribution of $p$-values. These testable restrictions result in more powerful tests for the null hypothesis of no $p$-hacking. When there is also publication bias, our tests are joint tests for $p$-hacking and publication bias. A reanalysis of two prominent datasets shows the usefulness of our new tests.

In this article, we are presenting the relationship between environmental pollution and the income level of the selected twenty-four countries. We implemented a data-based research analysis where, for each country, we analyzed the related data for fifty-six years, from 1960 to 2016, to assess the relationship between the carbon emission and income level. After performing the related data analysis for each country, we concluded whether the results for that country were in line with the Environmental Kuznets Curve (EKC) hypothesis. The EKC hypothesis suggests that the carbon emission per capita starts a declining trend when the country-specific high level of income is reached. The results of our data analyses show that the EKC hypothesis is valid for high-income countries and the declining trends of carbon emission are clearly observed when the income level reaches a specific high enough level. On the other hand, for the non-high income countries, our analysis results show that it is too early to make an assessment at this growth stage of their economies because they have not reached their related high-enough income per capita levels yet. Furthermore, we performed two more additional analyses on high-income countries. First, we analyzed the related starting years of their carbon emission declining trends. The big variance in the starting years of the carbon emission declining trends shows that the international policies are clearly ineffective in initiating the declining trend in carbon emission. In addition, for the high-income countries, we explained the differences in their carbon emission per capita levels in 2014 with their SGI indices and their dependence on high-carbon emission energy production.

We consider a class of multiplicative processes which, added with stochastic reset events, give origin to stationary distributions with power-law tails -- ubiquitous in the statistics of social, economic, and ecological systems. Our main goal is to provide a series of exact results on the dynamics and asymptotic behaviour of increasingly complex versions of a basic multiplicative process with resets, including discrete and continuous-time variants and several degrees of randomness in the parameters that control the process. In particular, we show how the power-law distributions are built up as time elapses, how their moments behave with time, and how their stationary profiles become quantitatively determined by those parameters. Our discussion emphasizes the connection with financial systems, but these stochastic processes are also expected to be fruitful in modeling a wide variety of social and biological phenomena.

We analyze the impact of obtaining a residence permit on foreign workers' labor market and residential attachment. To overcome the usually severe selection issues, we exploit a unique migration lottery that randomly assigns access to otherwise restricted residence permits in Liechtenstein (situated between Austria and Switzerland). Using an instrumental variable approach, our results show that lottery compliers (whose migration behavior complies with the assignment in their first lottery) raise their employment probability in Liechtenstein by on average 24 percentage points across outcome periods (2008 to 2018) as a result of receiving a permit. Relatedly, their activity level and employment duration in Liechtenstein increase by on average 20 percentage points and 1.15 years, respectively, over the outcome window. These substantial and statistically significant effects are mainly driven by individuals not (yet) working in Liechtenstein prior to the lottery rather than by previous cross-border commuters. Moreover, we find both the labor market and residential effects to be persistent even several years after the lottery with no sign of fading out. These results suggest that granting resident permits to foreign workers can be effective to foster labor supply even beyond the effect of cross-border commuting from adjacent regions.

Financial inclusion and inclusive growth are the buzzwords today. Inclusive growth empowers people belonging to vulnerable sections. This in turn depends upon a variety of factors, the most important being financial inclusion, which plays a strategic role in promoting inclusive growth and helps in reducing poverty by providing regular and reliable sources of finance to the vulnerable sections. In this direction, the Government of India in its drive for financial inclusion has taken several measures to increase the access to and availing of formal financial services by unbanked households. The purpose of this paper is to assess the nature and extent of financial inclusion and its impact on the socio-economic status of households belonging to vulnerable sections focusing on inclusive growth. This has been analyzed with the theoretical background on financial access and economic growth, and by analyzing the primary data collected from the Revenue Divisions of Karnataka. The results show that there is a disparity in nature and extent of financial inclusion. Access to, availing of formal banking services pave the way to positive changes in the socio-economic status of households belonging to vulnerable sections which are correlated, leading to inclusive growth based on which the paper proposes a model to make the financial system more inclusive and pro-poor.

We examine probabilistic forecasts for battleground states in the 2020 US presidential election, using daily data from two sources over seven months: a model published by The Economist, and prices from the PredictIt exchange. We find systematic differences in accuracy over time, with markets performing better several months before the election, and the model performing better as the election approached. A simple average of the two forecasts performs better than either one of them overall, even though no average can outperform both component forecasts for any given state-date pair. This effect arises because the model and the market make different kinds of errors in different states: the model was confidently wrong in some cases, while the market was excessively uncertain in others. We conclude that there is value in using hybrid forecasting methods, and propose a market design that incorporates model forecasts via a trading bot to generate synthetic predictions. We also propose and conduct a profitability test that can be used as a novel criterion for the evaluation of forecasting performance.

We fully characterize the absence of Butterfly arbitrage in the SVI formula for implied total variance proposed by Gatheral in 2004. The main ingredient is an intermediary characterization of the necessary condition for no arbitrage obtained for any model by Fukasawa in 2012 that the inverse functions of the -d1 and -d2 of the Black-Scholes formula, viewed as functions of the log-forward moneyness, should be increasing. A natural rescaling of the SVI parameters and a meticulous analysis of the Durrleman condition allow then to obtain simple range conditions on the parameters. This leads to a straightforward implementation of a least-squares calibration algorithm on the no arbitrage domain, which yields an excellent fit on the market data we used for our tests, with the guarantee to yield smiles with no Butterfly arbitrage.

Based on the Global Entrepreneurship Monitor (GEM) surveys and conducting a panel data estimation to test our hypothesis, this paper examines whether corruption perceptions might sand or grease the wheels for entrepreneurship inside companies or intrapreneurship in a sample of 92 countries for the period 2012 to 2019. Our results find that the corruption perception sands the wheel for intrapreneurship. There is evidence of a quadratic relation, but this relation is only clear for the less developed countries, which sort of moderate the very negative effect of corruption for these countries. The results also confirm that corruption influences differently on intrapreneurship depending on the level of development of the country.

Quantum mechanics is well known to accelerate statistical sampling processes over classical techniques. In quantitative finance, statistical samplings arise broadly in many use cases. Here we focus on a particular one of such use cases, credit valuation adjustment (CVA), and identify opportunities and challenges towards quantum advantage for practical instances. To improve the depths of quantum circuits for solving such problem, we draw on various heuristics that indicate the potential for significant improvement over well-known techniques such as reversible logical circuit synthesis. In minimizing the resource requirements for amplitude amplification while maximizing the speedup gained from the quantum coherence of a noisy device, we adopt a recently developed Bayesian variant of quantum amplitude estimation using engineered likelihood functions (ELF). We perform numerical analyses to characterize the prospect of quantum speedup in concrete CVA instances over classical Monte Carlo simulations.