The Total Economic Time Capacity of a Year 525600 minutes is postulated as a time standard for a new Monetary Minute currency in this evaluation study. Consequently, the Monetary Minute MonMin is defined as a 1/525600 part of the Total Economic Time Capacity of a Year. The Value CMonMin of the Monetary Minute MonMin is equal to a 1/525600 part of the GDP, p.c., expressed in a specific state currency C. There is described how the Monetary Minutes MonMin are determined, and how their values CMonMin are calculated based on the GDP and all the population in specific economies. The Monetary Minutes trace different aggregate productivity, i.e. exploitation of the total time capacity of a year for generating of the GDP in economies of different states.

Identification and scoring functions are statistical tools to assess the calibration and the relative performance of risk measure estimates, e.g., in backtesting. A risk measures is called identifiable (elicitable) it it admits a strict identification function (strictly consistent scoring function). We consider measures of systemic risk introduced in Feinstein, Rudloff and Weber (2017). Since these are set-valued, we work within the theoretical framework of Fissler, Hlavinov\'a and Rudloff (2019) for forecast evaluation of set-valued functionals. We construct oriented selective identification functions, which induce a mixture representation of (strictly) consistent scoring functions. Their applicability is demonstrated with a comprehensive simulation study.

Many countries embroiled in non-religious civil conflicts have experienced a dramatic increase in religious competition in recent years. This study examines whether increasing competition between religions affects violence in non-religious conflicts. The study focuses on Colombia, a deeply Catholic country that has suffered one of the world's longest-running internal conflicts and, in the last few decades, has witnessed an intense increase in religious competition between the Catholic Church and new non-Catholic churches. The estimation of a dynamic treatment effect model shows that establishing the first non-Catholic church in a municipality substantially increases the probability of an attack by a left-wing guerrilla group. Further analysis suggests that the increase in guerrilla attacks is associated with the expectation among guerrilla groups that their membership will decline as a consequence of more intense competition with religious groups for followers.

Modeling counterparty risk is computationally challenging because it requires the simultaneous evaluation of all the trades with each counterparty under both market and credit risk. We present a multi-Gaussian process regression approach, which is well suited for OTC derivative portfolio valuation involved in CVA computation. Our approach avoids nested simulation or simulation and regression of cash flows by learning a Gaussian metamodel for the mark-to-market cube of a derivative portfolio. We model the joint posterior of the derivatives as a Gaussian process over function space, with the spatial covariance structure imposed on the risk factors. Monte-Carlo simulation is then used to simulate the dynamics of the risk factors. The uncertainty in portfolio valuation arising from the Gaussian process approximation is quantified numerically. Numerical experiments demonstrate the accuracy and convergence properties of our approach for CVA computations, including a counterparty portfolio of interest rate swaps.

This paper presents empirically-estimated average hourly relationships between regional electricity trade in the United States and prices, emissions, and generation from 2015 through 2018. Consistent with economic theory, the analysis finds a negative relationship between electricity prices in California and regional trade, conditional on local demand. Each 1 gigawatt-hour increase in California electricity imports is associated with an average $0.15 per megawatt-hour decrease in the California Independent System Operator's wholesale electricity price. There is a net-negative short term relationship between carbon dioxide emissions in California and electricity imports that is partially offset by positive emissions from exporting neighbors. Specifically, each 1 GWh increase in regional trade is associated with a net 70-ton average decrease in CO2 emissions across the western U.S., conditional on demand levels. The results provide evidence that electricity imports mostly displace natural gas generation on the margin in the California electricity market. A small positive relationship is observed between short-run SO2 and NOx emissions in neighboring regions and California electricity imports. The magnitude of the SO2 and NOx results suggest an average increase of 0.1 MWh from neighboring coal plants is associated with a 1 MWh increase in imports to California.

This article applies a long short-term memory recurrent neural network to mortality rate forecasting. The model can be trained jointly on the mortality rate history of different countries, ages, and sexes. The RNN-based method seems to outperform the popular Lee-Carter model.

This paper studies deep learning methodologies for portfolio optimization in the US equities market. We present a novel residual switching network that can automatically sense changes in market regimes and switch between momentum and reversal predictors accordingly. The residual switching network architecture combines two separate residual networks (ResNets), namely a switching module that learns stock market conditions, and the main module that learns momentum and reversal predictors. We demonstrate that over-fitting noisy financial data can be controlled with stacked residual blocks and further incorporating the attention mechanism can enhance powerful predictive properties. Over the period 2008 to H12017, the residual switching network (Switching-ResNet) strategy verified superior out-of-sample performance with an average annual Sharpe ratio of 2.22, compared with an average annual Sharpe ratio of 0.81 for the ANN-based strategy and 0.69 for the linear model.

Quantitative finance has had a long tradition of a bottom-up approach to complex systems inference via multi-agent systems (MAS). These statistical tools are based on modelling agents trading via a centralised order book, in order to emulate complex and diverse market phenomena. These past financial models have all relied on so-called zero-intelligence agents, so that the crucial issues of agent information and learning, central to price formation and hence to all market activity, could not be properly assessed. In order to address this, we designed a next-generation MAS stock market simulator, in which each agent learns to trade autonomously via model-free reinforcement learning. We calibrate the model to real market data from the London Stock Exchange over the years $2007$ to $2018$, and show that it can faithfully reproduce key market microstructure metrics, such as various price autocorrelation scalars over multiple time intervals. Agent learning thus enables model emulation of the microstructure with greater realism.

We provide analytical tools for pricing power options with exotic features (capped or log payoffs, gap options ...) in the framework of exponential L\'evy models driven by one-sided stable or tempered stable processes. Pricing formulas take the form of fast converging series of powers of the log-forward moneyness and of the time-to-maturity; these series are obtained via a factorized integral representation in the Mellin space evaluated by means of residues in $\mathbb{C}$ or $\mathbb{C}^2$. Comparisons with numerical methods and efficiency tests are also discussed.

We reconsider the microeconomic foundations of financial economics under Knightian Uncertainty. We remove the (implicit) assumption of a common prior and base our analysis on a common order instead. Economic viability of asset prices and the absence of arbitrage are equivalent. We show how the different versions of the Efficient Market Hypothesis are related to the assumptions one is willing to impose on the common order. We also obtain a version of the Fundamental Theorem of Asset Pricing using the notion of sublinear pricing measures. Our approach unifies recent versions of the Fundamental Theorem under a common framework.