We develop a theory of Bayesian price formation in electronic markets. We formulate a stylised model in which market participants update their Bayesian prior on an efficient price with a model-based learning process. We show that exponential intensities for aggressive orders arise naturally in this framework. The resulting theory allows us to derive simple analytic formulas for market dynamics and price impact in the case with Brownian efficient price and informed market takers. In particular we show that for small spreads there is an asymptotic market regime. We illustrate our results with numerical experiments.

Subdiffusion is a well established phenomenon in physics. In this paper we apply the subdiffusive dynamics to analyze financial markets. We focus on the financial aspect of time fractional diffusion model with moving boundary i.e. American and barrier option pricing in the subdiffusive Black-Scholes (B-S) model. Two computational methods for valuing American options in the considered model are proposed - the weighted finite difference (FD) and the Longstaff-Schwartz method. In the article it is also shown how to valuate numerically wide range of barrier options using the FD approach.

In the past two decades, the field of applied finance has tremendously benefited from graph theory. As a result, novel methods ranging from asset network estimation to hierarchical asset selection and portfolio allocation are now part of practitioners' toolboxes. In this paper, we investigate the fundamental problem of learning undirected graphical models under Laplacian structural constraints from the point of view of financial market times series data. In particular, we present natural justifications, supported by empirical evidence, for the usage of the Laplacian matrix as a model for the precision matrix of financial assets, while also establishing a direct link that reveals how Laplacian constraints are coupled to meaningful physical interpretations related to the market index factor and to conditional correlations between stocks. Those interpretations lead to a set of guidelines that practitioners should be aware of when estimating graphs in financial markets. In addition, we design numerical algorithms based on the alternating direction method of multipliers to learn undirected, weighted graphs that take into account stylized facts that are intrinsic to financial data such as heavy tails and modularity. We illustrate how to leverage the learned graphs into practical scenarios such as stock time series clustering and foreign exchange network estimation. The proposed graph learning algorithms outperform the state-of-the-art methods in an extensive set of practical experiments. Furthermore, we obtain theoretical and empirical convergence results for the proposed algorithms. Along with the developed methodologies for graph learning in financial markets, we release an R package, called fingraph, accommodating the code and data to obtain all the experimental results.

A make-your-mind-up option is an American derivative with delivery lags. We show that its put option can be decomposed as a European put and a new type of American-style derivative. The latter is an option for which the investor receives the Greek Theta of the corresponding European option as the running payoff, and decides an optimal stopping time to terminate the contract. Based on this decomposition and using free boundary techniques, we show that the associated optimal exercise boundary exists and is a strictly increasing and smooth curve, and analyze the asymptotic behavior of the value function and the optimal exercise boundary for both large maturity and small time lag.

The performance of a market for annuity contracts depends on the interaction between retirees and strategic life insurance companies with private information about their costs. We model this interaction using multi-stage and multi-attribute auctions where a retiree--auctioneer--maximizes her \emph{expected present discounted utility}. We estimate the model parameters using rich administrative data from Chile and find that retirees with low savings value firms' risk-ratings the most. The estimates also suggest that almost half the retirees who choose an annuity do not value bequest, and firms are more likely to have low annuitization cost for retirees in the top two savings deciles. Counterfactuals show that under the current mechanism, private information about costs harms only these high savers. Implementing English auctions \emph{and} prohibiting the use of risk-ratings lead to higher pensions, but only for these high savers.

The enlargement of the European Union to new countries in 2004 launched mechanisms supporting the development of various social and economic areas, as well as levelling the differences between the Community members in these areas. This article focuses on the insurance sector. Its main purpose is to analyze the development and similarity of the insurance markets of old and new members of the European Union after the enlargement in 2004.

We introduce an evolutionary framework to evaluate competing (mis)specifications in strategic situations, focusing on which misspecifications can persist over a correct specification. Agents with heterogeneous specifications coexist in a society and repeatedly match against random opponents to play a stage game. They draw Bayesian inferences about the environment based on personal experience, so their learning depends on the distribution of specifications and matching assortativity in the society. One specification is evolutionarily stable against another if, whenever sufficiently prevalent, its adherents obtain higher expected objective payoffs than their counterparts. The learning channel leads to novel stability phenomena compared to frameworks where the heritable unit of cultural transmission is a single belief instead of a specification (i.e., set of feasible beliefs). We apply the framework to linear-quadratic-normal games where players receive correlated signals but possibly misperceive the information structure. The correct specification is not evolutionarily stable against a correlational error, whose direction depends on matching assortativity. As another application, the framework also endogenizes coarse analogy classes in centipede games.

Understanding the microeconomic details of technological catch-up processes offers great potential for informing both innovation economics and development policy. We study the economic transition of the PR China from an agrarian country to a high-tech economy as one example for such a case. It is clear from past literature that rapidly rising productivity levels played a crucial role. However, the distribution of labor productivity in Chinese firms has not been comprehensively investigated and it remains an open question if this can be used to guide economic development. We analyze labor productivity and the dynamic change of labor productivity in firm-level data for the years 1998-2013 from the Chinese Industrial Enterprise Database. We demonstrate that both variables are conveniently modeled as L\'evy alpha-stable distributions, provide parameter estimates and analyze dynamic changes to this distribution. We find that the productivity gains were not due to super-star firms, but due to a systematic shift of the entire distribution with otherwise mostly unchanged characteristics. We also found an emerging right-skew in the distribution of labor productivity change. While there are significant differences between the 31 provinces and autonomous regions of the P.R. China, we also show that there are systematic relations between micro-level and province-level variables. We conclude with some implications of these findings for development policy.

The main purpose of this work is to derive a partial differential equation for the reserves of life insurance liabilities subject to stochastic interest rates where the benefits and premiums depend directly on changes in the interest rate curve. In particular, we allow the payment streams to depend on the performance of an overnight technical interest rate, making them stochastic as well. This opens up for considering new types of contracts based on the performance of the insurer's returns on their own investments. We provide explicit solutions for the reserves when the premiums and benefits vary according to interest rate levels or averages under the Vasicek model and conduct some simulations computing reserve surfaces numerically. We also give an example of a reinsurance treaty taking over pension payments when the insurer's average returns fall under some specified threshold.

Technological change is essential for balancing economic growth and environmental sustainability. This study measures and documents energy-saving technological change to understand its trends in advanced countries over recent decades. We estimate aggregate production functions with factor-augmenting technology using cross-country panel data and shift-share instruments, thereby measuring and documenting energy-saving technological change. Our results show how energy-saving technological change varies across countries over time and the extent to which it contributes to economic growth in 12 OECD countries from the years 1978 to 2005.

Across a growing number of domains, human experts are expected to learn from and adapt to AI with superior decision-making abilities. But how can we quantify such human adaptation to AI? Using a Reinforcement Learning framework in the game of Go, we develop a simple measure of human adaptation to AI and test its usefulness in two case studies. In Case Study 1, we analyze 1.3 million move decisions made by human experts and find that a positive form of adaptation to AI (learning) occurred after the experts could observe AI's reasoning processes rather than mere actions of AI. In Case Study 2, we use our measure to detect a negative form of adaptation to AI, cheating by getting help from AI in a professional match between human experts. We discuss our measure's applications in domains other than Go, especially in domains in which AI's decision-making ability will likely surpass that of human experts.

The essence of multivariate sequential learning is all about how to extract dependencies in data. These data sets, such as hourly medical records in intensive care units and multi-frequency phonetic time series, often time exhibit not only strong serial dependencies in the individual components (the "marginal" memory) but also non-negligible memories in the cross-sectional dependencies (the "joint" memory). Because of the multivariate complexity in the evolution of the joint distribution that underlies the data generating process, we take a data-driven approach and construct a novel recurrent network architecture, termed Memory-Gated Recurrent Networks (mGRN), with gates explicitly regulating two distinct types of memories: the marginal memory and the joint memory. Through a combination of comprehensive simulation studies and empirical experiments on a range of public datasets, we show that our proposed mGRN architecture consistently outperforms state-of-the-art architectures targeting multivariate time series.

A Renewable Energy and Materials Economy (REME) is proposed as the solution to the climate change threat. REME mimics nature to produce carbon neutral liquid fuels and chemicals as well as carbon negative materials by using water, CO$_2$ from the atmosphere and renewable energy as inputs. By being in harmony with nature REME has a positive feedback between economic development and climate change protection. In REME the feedback driven accelerated rate of economic growth enables the climate change threat to be addressed in a timely manner. It is also cost-effective protection because it sequesters by monetizing the carbon removed from the air in carbon-based building materials. Thus, addressing the climate change threat is not a cost to the economy but a result of REME driven prosperity.

We show that infinite divisibility of a trading commodity leads to a self-sustained price bubble when traders use adaptive investment strategies. The adaptive strategy can be viewed as a psychological response of a trader to the situation when the trader's estimation of future prices does not match the actual, realized price. We use a multi-agent model to illustrate the price bubble formation and to quantify its main statistical properties such as the return, the volatility, and the systematic risk of the price bubble to crash. We discuss the plausibility for bubbles to drive prices of digital currencies.

Machine learning plays an essential role in preventing financial losses in the banking industry. Perhaps the most pertinent prediction task that can result in billions of dollars in losses each year is the assessment of credit risk (i.e., the risk of default on debt). Today, much of the gains from machine learning to predict credit risk are driven by gradient boosted decision tree models. However, these gains begin to plateau without the addition of expensive new data sources or highly engineered features. In this paper, we present our attempts to create a novel approach to assessing credit risk using deep learning that does not rely on new model inputs. We propose a new credit card transaction sampling technique to use with deep recurrent and causal convolution-based neural networks that exploits long historical sequences of financial data without costly resource requirements. We show that our sequential deep learning approach using a temporal convolutional network outperformed the benchmark non-sequential tree-based model, achieving significant financial savings and earlier detection of credit risk. We also demonstrate the potential for our approach to be used in a production environment, where our sampling technique allows for sequences to be stored efficiently in memory and used for fast online learning and inference.

The distributions of trade values and relationships among countries and product categories reflect how countries select their trade partners and design export portfolios. Here we consider the exporter-importer network and the exporter-product network with directed links weighted by the logarithm of the corresponding export values each year from 1962 to 2018, and study how the weights of the outgoing links from each country are distributed. Such local logarithmic export distributions by destinations and products are found to follow approximately the Gaussian distribution across exporters and time, implying random assignment of export values on logarithmic scale. However, a non-zero skewness is identified, changing from positive to negative as exporters have more partner importers and more product categories in their portfolios. Seeking the origin, we analyze how local exports depend on the out-degree of exporter and the in-degrees of destinations/products and formulate their quantitative and measurable relation incorporating randomness, which uncovers the fundamental nature of the export strategies of individual countries.

The effective lower bound on a short term interest rate may not constrain a central bank's capacity to achieve its objectives if unconventional monetary policy (UMP) is powerful enough. We formalize this `irrelevance hypothesis' using a dynamic stochastic general equilibrium model with UMP and test it empirically for the United States and Japan using a structural vector autoregressive model that includes variables subject to occasionally binding constraints. The hypothesis is strongly rejected for both countries. However, a comparison of the impulse responses to a monetary policy shock across regimes shows that UMP has had strong delayed effects in each country.

The transformation of the electricity sector is a main element of the transition to a decarbonized economy. Conventional generators powered by fossil fuels have to be replaced by variable renewable energy (VRE) sources in combination with electricity storage and other options for providing temporal flexibility. We discuss the market dynamics of increasing VRE penetration and their integration in the electricity system. We describe the merit-order effect (decline of wholesale electricity prices as VRE penetration increases) and the cannibalization effect (decline of VRE value as their penetration increases). We further review the role of electricity storage and other flexibility options for integrating variable renewables, and how storage can contribute to mitigating the two mentioned effects. We also use a stylized open-source model to provide some graphical intuition on this. While relatively high shares of VRE are achievable with moderate amounts of electricity storage, the role of long-term storage increases as the VRE share approaches 100%.

I study the impact of corona populism -- politics aimed at denying or downplaying the danger posed by COVID-19 for strategic reasons -- on the evolution of the pandemic using regional data from Austria. The right-wing FPOE first vocalized strong support for strict lockdown measures, but made a corona populist turn at the end of the first wave of infections. Using regression analysis, I show that the vote share of the FPOE at the last national parliamentary elections is a strong predictor for the number of COVID-19 deaths after the FPOE switched their policy stance, while there is no or even a negative correlation before the policy switch. These results are robust under simple as well as sophisticated specifications of the model controlling for demographic and socioeconomic conditions. Interestingly, I do not find a statistically significant correlation between the FPOE vote share and the reported number of infections. I hypothesize that this can be traced back to a self-selection bias in testing. To explore this hypothesis, I extend the classical SIRD model to incorporate conditional quarantine and two groups of agents: the majority and the corona sceptics, where the latter are less inclined to get tested and engage in social distancing. Such a model can explain the nontrivial empirics: if mixing is sufficiently homophilic, an increase in the share of corona sceptics can cause an increase in the number of deaths without increasing the number of reported infections. I finally discuss the implications for both groups.

The industrial life cycle theory has proved to be helpful for describing the evolution of industries from birth to maturity. This paper is to highlight the historical evolution stage of Atlantic City's gambling industry in a structural framework covered by industrial market, industrial organization, industrial policies and innovation. Data mining was employed to obtain from local official documents, to verify the module of industrial life cycle in differential phases as introduction, development, maturity and decline. The trajectory of Atlantic City's gambling sector evolution reveals the process from the stages of introduction to decline via a set of variables describing structural properties of this industry such as product, market and organization of industry under a special industry environment in which industry recession as a result of theory of industry life cycle is a particular evidence be proved again. Innovation of the gambling industry presents the ongoing recovering process of the Atlantic City gambling industry enriches the theory of industrial life cycle in service sectors.

We study the consequences of job markets' heavy reliance on referrals. Referrals screen candidates and lead to better matches and increased productivity, but disadvantage job-seekers who have few or no connections to employed workers, leading to increased inequality. Coupled with homophily, referrals also lead to immobility: a demographic group's low current employment rate leads that group to have relatively low future employment as well. We identify conditions under which distributing referrals more evenly across a population not only reduces inequality, but also improves future productivity and economic mobility. We use the model to examine optimal policies, showing that one-time affirmative action policies involve short-run production losses, but lead to long-term improvements in equality, mobility, and productivity due to induced changes in future referrals. We also examine how the possibility of firing workers changes the effects of referrals.

The wealth of nations and the health of populations are intimately strongly associated, yet the extent to which economic prosperity (GDP per capita) causes improved health remains disputed. The purpose of this article is to analyze the impact of sovereign debt crises (SDC) on child mortality, using a sample of 57 low- and middle-income countries surveyed by the Demographic and Health Survey between the years 1990 and 2015. These surveys supply 229 household data and containing about 3 million childbirth history records. This focus on SDC instead of GDP provides a quasi-experimental moment in which the influence of unobserved confounding is less than a moment analyzing the normal fluctuations of GDP. This study measures child mortality at six thresholds: neonatal, under-one (infant), under-two, under-three, under-four, and under-five mortality. Using a machine-learning (ML) model for causal inference, this study finds that while an SDC causes an adverse yet statistically insignificant effect on neonatal mortality, all other child mortality group samples are adversely affected between a probability of 0.12 to 0.14 (all statistically significant at the 95-percent threshold). Through this ML, this study also finds that the most important treatment heterogeneity moderator, in the entire adjustment set, is whether a child is born in a low-income country.

We introduce time-inhomogeneous stochastic volatility models, in which the volatility is described by a nonnegative function of a Volterra type continuous Gaussian process that may have very rough sample paths. The main results obtained in the paper are sample path and small-noise large deviation principles for the log-price process in a time-inhomogeneous super rough Gaussian model under very mild restrictions. We use these results to study the asymptotic behavior of binary barrier options, exit time probability functions, and call options.

We estimate the relationship between GDP per capita growth and the growth rate of the national savings rate using a panel of 130 countries over the period 1960-2017. We find that GDP per capita growth increases (decreases) the growth rate of the national savings rate in poor countries (rich countries), and a higher credit-to-GDP ratio decreases the national savings rate as well as the income elasticity of the national savings rate. We develop a model with a credit constraint to explain the growth-saving relationship by the saving behavior of entrepreneurs at both the intensive and extensive margins. We further present supporting evidence for our theoretical findings by utilizing cross-country time series data of the number of new businesses registered and the corporate savings rate.

Labor displacement off-or nearshore is a performance improvement instrument that currently sparks a lot of interest in the service sector. This article proposes a model to understand the consequences of such a decision on management consulting firms. Its calibration on the market of consulting services for the German transportation industry highlights that, under realistic assumptions, labor displacement translates in price decrease by-0.5% on average per year and that for MC practices to remain competitive/profitable they have to at least increase the amount of work they off/nears shore by +0.7% a year.