The installation of high-speed rail in the world during the last two decades resulted in significant socioeconomic and environmental changes. The U.S. has the longest rail network in the world, but the focus is on carrying a wide variety of loads including coal, farm crops, industrial products, commercial goods, and miscellaneous mixed shipments. Freight and passenger services in the U.S. dates to 1970, with both carried out by private railway companies. Railways were the main means of transport between cities from the late 19th century through the middle of the 20th century. However, rapid growth in production and improvements in technologies changed those dynamics. The fierce competition for comfortability and pleasantness in passenger travel and the proliferation of aviation services in the U.S. channeled federal and state budgets towards motor vehicle infrastructure, which brought demand for railroads to a halt in the 1950s. Presently, the U.S. has no high-speed trains, aside from sections of Amtrak s Acela line in the Northeast Corridor that can reach 150 mph for only 34 miles of its 457-mile span. The average speed between New York and Boston is about 65 mph. On the other hand, China has the world s fastest and largest high-speed rail network, with more than 19,000 miles, of which the vast majority was built in the past decade. Japan s bullet trains can reach nearly 200 miles per hour and dates to the 1960s. That system moved more than 9 billion people without a single passenger casualty. In this systematic review, we studied the effect of High-Speed Rail (HSR) on the U.S. and other countries including France, Japan, Germany, Italy, and China in terms of energy consumption, land use, economic development, travel behavior, time use, human health, and quality of life.

We address the Merton problem of maximizing the expected utility of terminal wealth using techniques from variational analysis. Under a general continuous semimartingale market model with stochastic parameters, we obtain a characterization of the optimal portfolio for general utility functions in terms of a forward-backward stochastic differential equation (FBSDE) and derive solutions for a number of well-known utility functions. Our results complement a previous studies conducted on optimal strategies in markets driven by Brownian noise with random drift and volatility parameters.

We provide the first analysis of how the global spread of the novel coronavirus affects contemporaneous economic sentiment. First, we collect a global dataset on internet searches indicative of economic anxieties. We find that the arrival of coronavirus in a country led to a substantial increase in such internet searches of up to 58 percent. Second, leveraging two US representative survey experiments conducted in early and mid-March 2020, we document a rapid surge in economic anxieties after the arrival of the coronavirus in the US. Third, to understand how information about the coronavirus affects these anxieties, we measure perceptions about the coronavirus. We find substantial heterogeneity in participants' beliefs about the mortality from and contagiousness of the virus. Fourth, experimentally providing participants with information about mortality and contagiousness causally affects participants' worries regarding the aggregate economy and their personal economic situation. Finally, we document that participants' subjective mental models understate the non-linear nature of disease spread, and that these mental models shape the extent of economic worries. These results underscore the importance of public education about the virus for successful containment as well as the need for timely measures that decrease economic hardship and anxiety during a major global pandemic.

The indirect transactions between sectors of an economic system has been a long-standing open problem. There have been numerous attempts to define and mathematically formulate this concept in various other scientific fields in literature as well. The existing indirect effects formulations, however, can neither determine the direct and indirect transactions separately nor quantify these transactions between two individual sectors of interest in an economic system. The novel concepts of the direct, indirect and total transactions between any two sectors are introduced, and the corresponding requirements matrices are systematically formulated relative to both final demands and gross outputs, based on the system decomposition theory. It is demonstrated theoretically and through illustrative examples that the proposed requirements matrices accurately define and quantify the corresponding direct, indirect, and total interactions and relationships. The proposed requirements matrices for the US economy using aggregated input-output tables for multiple years are then presented and briefly analyzed.

Interest in predicting multivariate probability distributions is growing due to the increasing availability of rich datasets and computational developments. Scoring functions enable the comparison of forecast accuracy, and can potentially be used for estimation. A scoring function for multivariate distributions that has gained some popularity is the energy score. This is a generalization of the continuous ranked probability score (CRPS), which is widely used for univariate distributions. A little-known, alternative generalization is the multivariate CRPS (MCRPS). We propose a theoretical framework for scoring functions for multivariate distributions, which encompasses the energy score and MCRPS, as well as the quadratic score, which has also received little attention. We demonstrate how this framework can be used to generate new scores. For univariate distributions, it is well-established that the CRPS can be expressed as the integral over a quantile score. We show that, in a similar way, scoring functions for multivariate distributions can be "disintegrated" to obtain scoring functions for level sets. Using this, we present scoring functions for different types of level set, including those for densities and cumulative distributions. To compute the scoring functions, we propose a simple numerical algorithm. We illustrate our proposals using simulated and stock returns data.

In this paper we develop a semi-closed form solutions for the barrier (perhaps, time-dependent) and American options written on the underlying stock which follows a time-dependent OU process with a log-normal drift. This model is equivalent to the familiar Hull-White model in FI, or a time dependent OU model in FX. Semi-closed form means that given the time-dependent interest rate, continuous dividend and volatility functions, one need to solve numerically a linear (for the barrier option) or nonlinear (for the American option) Fredholm equation of the first kind. After that the option prices in all cases are presented as one-dimensional integrals of combination of the above solutions and Jacobi theta functions. We also demonstrate that computationally our method is more efficient than the backward finite difference method used for solving these problems, and can also be as efficient as the forward finite difference solver while providing better accuracy and stability.

We introduce time-inhomogeneous stochastic volatility models, in which the volatility is described by a positive function of a Volterra type continuous Gaussian process that may have extremely rough sample paths. The drift function and the volatility function are assumed to be time-dependent and locally $\omega$-continuous for some modulus of continuity $\omega$. The main result obtained in the paper is a sample path large deviation principle for the log-price process in a Gaussian model under very mild restrictions. We apply this result to study the first exit time of the log-price process from an open interval.